  Seizure onset detection in electroencephalography (EEG) signals is a
challenging task due to the non-stereotyped seizure activities as well as their
stochastic and non-stationary characteristics in nature. Joint
spectral-temporal features are believed to contain sufficient and powerful
feature information for absence seizure detection. However, the resulting
high-dimensional features involve redundant information and require heavy
computational load. Here, we discover significant low-dimensional
spectral-temporal features in terms of mean-standard deviation of wavelet
transform coefficient (MS-WTC), based on which a novel absence seizure
detection framework is developed. The EEG signals are transformed into the
spectral-temporal domain, with their low-dimensional features fed into a
convolutional neural network. Superior detection performance is achieved on the
widely-used benchmark dataset as well as a clinical dataset from the Chinese
301 Hospital. For the former, seven classification tasks were evaluated with
the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a
mean accuracy of 94.7%, overwhelming other methods with low-dimensional
temporal and spectral features. Experimental results on two seizure datasets
demonstrate reliability, efficiency and stability of our proposed MS-WTC
method, validating the significance of the extracted low-dimensional
spectral-temporal features.


  Securities issuance through intermediaries is subject to agency problems and
informational frictions. We examine these effects using SPAC data. We identify
``premium'' investors whose participation is linked to lower liquidation risk,
higher returns, and lower redemption rates, consistent with both informational
rents and agency frictions. In contrast, ``non-premium'' investors engage in
non-agency quid pro quo relationships. Specifically, they receive high returns
from an intermediary (quid) in exchange for a tacit agreement to participate in
weaker future deals (quo). These relationships serve as insurance for issuers
and intermediaries, enabling more issuers to access markets.


  Symbolic music generation aims to generate music scores automatically. A
recent trend is to use Transformer or its variants in music generation, which
is, however, suboptimal, because the full attention cannot efficiently model
the typically long music sequences (e.g., over 10,000 tokens), and the existing
models have shortcomings in generating musical repetition structures. In this
paper, we propose Museformer, a Transformer with a novel fine- and
coarse-grained attention for music generation. Specifically, with the
fine-grained attention, a token of a specific bar directly attends to all the
tokens of the bars that are most relevant to music structures (e.g., the
previous 1st, 2nd, 4th and 8th bars, selected via similarity statistics); with
the coarse-grained attention, a token only attends to the summarization of the
other bars rather than each token of them so as to reduce the computational
cost. The advantages are two-fold. First, it can capture both music
structure-related correlations via the fine-grained attention, and other
contextual information via the coarse-grained attention. Second, it is
efficient and can model over 3X longer music sequences compared to its
full-attention counterpart. Both objective and subjective experimental results
demonstrate its ability to generate long music sequences with high quality and
better structures.


  In this paper we use a formula for the $n$-th power of a $2\times2$ matrix
$A$ (in terms of the entries in $A$) to derive various combinatorial
identities. Three examples of our results follow. 1) We show that if $m$ and
$n$ are positive integers and $s \in \{0,1,2,\dots,$ $\lfloor (mn-1)/2 \rfloor
\}$, then \begin{multline*} \sum_{i,j,k,t}2^{1+2t-mn+n}
\frac{(-1)^{nk+i(n+1)}}{1+\delta_{(m-1)/2,\,i+k}} \binom{m-1-i}{i}
\binom{m-1-2i}{k}\times\\ \binom{n(m-1-2(i+k))}{2j}\binom{j}{t-n(i+k)}
\binom{n-1-s+t}{s-t}\\ =\binom{mn-1-s}{s}. \end{multline*} 2) The generalized
Fibonacci polynomial $f_{m}(x,s)$ can be expressed as \[ f_{m}(x,s)=
\sum_{k=0}^{\lfloor (m-1)/2 \rfloor}\binom{m-k-1}{k}x^{m-2k-1}s^{k}. \] We
prove that the following functional equation holds: \begin{equation*}
f_{mn}(x,s)=f_{m}(x,s)\times f_{n}\left (\,f_{m+1}(x,s)+sf_{m-1}(x,s),
\,-(-s)^{m}\right) . \end{equation*} 3) If an arithmetical function $f$ is
multiplicative and for each prime $p$ there is a complex number $g(p)$ such
that \begin{equation*} f(p^{n+1}) = f(p)f(p^{n})- g(p)f(p^{n-1}), \hspace{15pt}
n \geq 1, \end{equation*} then $f$ is said to be \emph{specially
multiplicative}. We give another derivation of the following formula for a
specially multiplicative function $f$ evaluated at a prime power:
\begin{equation*} f(p^{k})=\sum_{j=0}^{\lfloor k/2 \rfloor}(-1)^{j}
\binom{k-j}{j}f(p)^{k-2j}g(p)^{j}. \end{equation*} We also prove various other
combinatorial identities.


  Decentralized optimization is well studied for smooth unconstrained problems.
However, constrained problems or problems with composite terms are an open
direction for research. We study structured (or composite) optimization
problems, where the functional is a sum of a convex smooth function and a
proper convex proximal-friendly term. Our method builds upon an accelerated
proximal gradient descent and makes several consensus iterations between
computations. Our result illustrates that a consensus procedure approach works
for composite optimization and yields a method with a relatively simple
structure and analysis.


  Standard software analytics often involves having a large amount of data with
labels in order to commission models with acceptable performance. However,
prior work has shown that such requirements can be expensive, taking several
weeks to label thousands of commits, and not always available when traversing
new research problems and domains. Unsupervised Learning is a promising
direction to learn hidden patterns within unlabelled data, which has only been
extensively studied in defect prediction. Nevertheless, unsupervised learning
can be ineffective by itself and has not been explored in other domains (e.g.,
static analysis and issue close time).
  Motivated by this literature gap and technical limitations, we present
FRUGAL, a tuned semi-supervised method that builds on a simple optimization
scheme that does not require sophisticated (e.g., deep learners) and expensive
(e.g., 100% manually labelled data) methods. FRUGAL optimizes the unsupervised
learner's configurations (via a simple grid search) while validating our design
decision of labelling just 2.5% of the data before prediction.
  As shown by the experiments of this paper FRUGAL outperforms the
state-of-the-art adoptable static code warning recognizer and issue closed time
predictor, while reducing the cost of labelling by a factor of 40 (from 100% to
2.5%). Hence we assert that FRUGAL can save considerable effort in data
labelling especially in validating prior work or researching new problems.
  Based on this work, we suggest that proponents of complex and expensive
methods should always baseline such methods against simpler and cheaper
alternatives. For instance, a semi-supervised learner like FRUGAL can serve as
a baseline to the state-of-the-art software analytics.


  We compute gravitational waves from inspiraling stellar-mass compact objects
on the equatorial plane of a massive spinning black hole (BH). Our inspiral
orbits are computed by taking into account the adiabatic change of orbital
parameters due to gravitational radiation in the lowest order in mass ratio. We
employ an interpolation method to compute the adiabatic change at arbitrary
points inside the region of orbital parameter space computed in advance. Using
the obtained inspiral orbits and associated gravitational waves, we compute
power spectra of gravitational waves and the signal-to-noise ratio (SNR) for
several values of the BH spin, the masses of the binary, and the initial
orbital eccentricity during a hypothetical three-yrs LISA observation before
final plunge. We find that (i) the SNR increases as the BH spin and the mass of
the compact object increase for the BH mass $M \agt 10^6M_\odot$, (ii) the SNR
has a maximum for $M \approx 10^6M_\odot$, and (iii) the SNR increases as the
initial eccentricity increases for $M=10^6M_\odot$. We also show that
incorporating the contribution from the higher multipole modes of gravitational
waves is crucial for enhancing the detection rate.


  Using the notion of contiguity of simplicial maps, we adapt Farber's
topological complexity to the realm of simplicial complexes. We show that, for
a finite simplicial complex $K$, our discretized concept recovers the
topological complexity of the realization $\|K\|$. Our approach is well suited
for designing and implementing algorithms that search for optimal motion
planners for autonomous systems in real-life applications.


  This work compares two tomographic imaging technologies, time-domain
full-field optical coherence tomography (FFOCT) working in reflection and
optical transmission tomography (OTT), using a new optical setup that combines
both. We show that, due to forward-scattering properties, the axial sectioning
and contrast in OTT can be optimized by tuning illumination. The influence of
sample scattering and thickness are discussed. We illustrate the comparison of
the two methods in static (morphology) and dynamic (metabolic contrast) regimes
using cell cultures, tissues and entire organisms emphasizing the advantages of
both approaches.


  Radiation from GRBs in the prompt phase, flares and an afterglow is thought
to be produced by accelerated electrons in magnetic fields. Such emission may
be produced at collisionless shocks of baryonic outflows or at reconnection
sites (at least for the prompt and flares) of the magnetically dominated
(Poynting flux driven) outflows, where no shocks presumably form at all. An
astonishing recent discovery is that during reconnection strong small-scale
magnetic fields are produced via the Weibel instability, very much like they
are produced at relativistic shocks. The relevant physics has been successfully
and extensively studied with the PIC simulations in 2D and, to some extent, in
3D for the past few years. We discuss how these simulations predict the
existence of MeV-range synchrotron/jitter emission in some GRBs, which can be
observed with Fermi. Recent results on modeling of the spectral variability and
spectral correlations of the GRB prompt emission in the Weibel-jitter paradigm
applicable to both baryonic and magnetic-dominated outflows is reviewed with
the emphasis on observational predictions.


  The effect of topology on the collapse transition and instantaneous shape of
an energy polydisperse polymer (a model heteropolymer) is studied by means of
computer simulations. In particular, we consider three different chain
topology, namely, linear (L), ring (R) and trefoil knot (T). The heteropolymer
is modeled by assigning each monomer an interaction parameter, $\varepsilon_i$,
drawn randomly from a Gaussian distribution. Through chain size scaling the
transition temperature, $\theta$, is located and compared among the chains of
different topogies. The influence of topology is reflected in the value of
$\theta$ and observed that $\theta(\text{L}) > \theta(\text{R}) >
\theta(\text{T})$ in a similar fashion to that of the homopolymer counterpart.
Also studied chain size distributions, and the shape changes across the
transition temperature characterised through shape parameters based on the
eigenvalues of the gyration tensor. It is observed that, for the model
heteropolymer, in addition to chain topology the $\theta$-temperature also
depends on energy polydispersity.


  We show that for any integer $k\ge 1$ there exists an integer $t_0(k)$ such
that for integers $t, k_1, \ldots, k_{t+1}, n$ with $t>t_0(k)$, $\max\{k_1,
\ldots, k_{t+1}\}\le k$, and $n > 2k(t+1)$, the following holds: If $F_i
\subseteq {[n]\choose k_i}$ and $|F_i|> {n\choose k_i}-{n-t\choose k_i} -
{n-t-k \choose k_i-1} + 1$ for all $i \in [t+1]$, then either $\{F_1,\ldots,
F_{t+1}\}$ admits a rainbow matching of size $t+1$ or there exists $W\in
{[n]\choose t}$ such that $W$ is a vertex cover of $F_i$ for all $i\in [t+1]$.
This may be viewed as a rainbow non-uniform extension of the classical
Hilton-Milner theorem. We also show that the same holds for every $t$ and $n >
2k^3t$, generalizing a recent stability result of Frankl and Kupavskii on
matchings to rainbow matchings.


  We prove that the kernel of the action the group algebra of the Weyl group
acting on tensor space (via restriction of the action from the general linear
group) is a cell ideal with respect to the alternating Murphy basis. This
provides an analogue of the second fundamental theory of invariant theory for
the partition algebra over an arbitrary commutative ring and proves that the
centraliser algebras of the partition algebra are cellular. We also prove
similar results for the half partition algebras.


  Over the past few years the Angry Birds AI competition has been held in an
attempt to develop intelligent agents that can successfully and efficiently
solve levels for the video game Angry Birds. Many different agents and
strategies have been developed to solve the complex and challenging physical
reasoning problems associated with such a game. However none of these agents
attempt one of the key strategies which humans employ to solve Angry Birds
levels, which is restarting levels. Restarting is important in Angry Birds
because sometimes the level is no longer solvable or some given shot made has
little to no benefit towards the ultimate goal of the game. This paper proposes
a framework and experimental evaluation for when to restart levels in Angry
Birds. We demonstrate that restarting is a viable strategy to improve agent
performance in many cases.


  Inspired by the behavior of humans talking in noisy environments, we propose
an embodied embedded cognition approach to improve automatic speech recognition
(ASR) systems for robots in challenging environments, such as with ego noise,
using binaural sound source localization (SSL). The approach is verified by
measuring the impact of SSL with a humanoid robot head on the performance of an
ASR system. More specifically, a robot orients itself toward the angle where
the signal-to-noise ratio (SNR) of speech is maximized for one microphone
before doing an ASR task. First, a spiking neural network inspired by the
midbrain auditory system based on our previous work is applied to calculate the
sound signal angle. Then, a feedforward neural network is used to handle high
levels of ego noise and reverberation in the signal. Finally, the sound signal
is fed into an ASR system. For ASR, we use a system developed by our group and
compare its performance with and without the support from SSL. We test our SSL
and ASR systems on two humanoid platforms with different structural and
material properties. With our approach we halve the sentence error rate with
respect to the common downmixing of both channels. Surprisingly, the ASR
performance is more than two times better when the angle between the humanoid
head and the sound source allows sound waves to be reflected most intensely
from the pinna to the ear microphone, rather than when sound waves arrive
perpendicularly to the membrane.


  Ship detection in aerial images remains an active yet challenging task due to
arbitrary object orientation and complex background from a bird's-eye
perspective. Most of the existing methods rely on angular prediction or
predefined anchor boxes, making these methods highly sensitive to unstable
angular regression and excessive hyper-parameter setting. To address these
issues, we replace the angular-based object encoding with an
anchor-and-angle-free paradigm, and propose a novel detector deploying a center
and four midpoints for encoding each oriented object, namely MidNet. MidNet
designs a symmetrical deformable convolution customized for enhancing the
midpoints of ships, then the center and midpoints for an identical ship are
adaptively matched by predicting corresponding centripetal shift and matching
radius. Finally, a concise analytical geometry algorithm is proposed to refine
the centers and midpoints step-wisely for building precise oriented bounding
boxes. On two public ship detection datasets, HRSC2016 and FGSD2021, MidNet
outperforms the state-of-the-art detectors by achieving APs of 90.52% and
86.50%. Additionally, MidNet obtains competitive results in the ship detection
of DOTA.


  The mini-proceedings of the 14th Meeting of the "Working Group on Rad.
Corrections and MC Generators for Low Energies" held in Frascati on September
13, 2013, as a satellite meeting of the PHIPSI13 conference in Rome, are
presented. These meetings, started in 2006, have as aim to bring together
experimentalists and theorists working in the fields of meson transition form
factors, hadronic contributions to (g-2)_\mu and the effective fine structure
constant, and development of MonteCarlo generators and Radiative Corrections
for precision e+e- and tau physics.


  The use of generalized LASSO is a common technique for recovery of structured
high-dimensional signals. Each generalized LASSO program has a governing
parameter whose optimal value depends on properties of the data. At this
optimal value, compressed sensing theory explains why LASSO programs recover
structured high-dimensional signals with minimax order-optimal error.
Unfortunately in practice, the optimal choice is generally unknown and must be
estimated. Thus, we investigate stability of each LASSO program with respect to
its governing parameter. Our goal is to aid the practitioner in answering the
following question: given real data, which LASSO program should be used? We
take a step towards answering this by analyzing the case where the measurement
matrix is identity (the so-called proximal denoising setup) and we use
$\ell_{1}$ regularization. For each LASSO program, we specify settings in which
that program is provably unstable with respect to its governing parameter. We
support our analysis with detailed numerical simulations. For example, there
are settings where a 0.1% underestimate of a LASSO parameter can increase the
error significantly; and a 50% underestimate can cause the error to increase by
a factor of $10^{9}$.


  In this note, by making use of a known hypergeometric series identity, I
prove two Ramanujan-type series for the Catalan's constant. The convergence
rate of these central binomial series surpasses those of all known similar
series, including a classical formula by Ramanujan and a recent formula by
Lupas. Interestingly, this suggests that an Ap\'{e}ry-like irrationality proof
could be found for this constant.


  This paper evaluated the effectiveness of using generative AI to simplify
science communication and enhance the public's understanding of science. By
comparing lay summaries of journal articles from PNAS, yoked to those generated
by AI, this work first assessed linguistic simplicity across such summaries and
public perceptions. Study 1a analyzed simplicity features of PNAS abstracts
(scientific summaries) and significance statements (lay summaries), observing
that lay summaries were indeed linguistically simpler, but effect size
differences were small. Study 1b used a large language model, GPT-4, to create
significance statements based on paper abstracts and this more than doubled the
average effect size without fine-tuning. Study 2 experimentally demonstrated
that simply-written GPT summaries facilitated more favorable perceptions of
scientists (they were perceived as more credible and trustworthy, but less
intelligent) than more complexly-written human PNAS summaries. Crucially, Study
3 experimentally demonstrated that participants comprehended scientific writing
better after reading simple GPT summaries compared to complex PNAS summaries.
In their own words, participants also summarized scientific papers in a more
detailed and concrete manner after reading GPT summaries compared to PNAS
summaries of the same article. AI has the potential to engage scientific
communities and the public via a simple language heuristic, advocating for its
integration into scientific dissemination for a more informed society.


  We study the non-equilibrium steady states that emerge when two interacting
three-dimensional Potts blocks slide on each other. As at equilibrium the Potts
model exhibits different types of phase transitions for different numbers $q$
of spin states, we consider the following three cases: $q=2$ (i.e. the Ising
case), $q=3$, and $q=9$, which at equilibrium yield respectively a second order
phase transition, a weak first order transition and a strong first order
transition. In our study we focus on the anisotropic character of the steady
states that result from the relative motion and discuss the change in
finite-size signatures when changing the number $q$ of spin states.


  This note describes five subjects of some interest for the density functional
theory in nuclear physics. These are, respectively, i) the need for concave
functionals, ii) the nature of the Kohn-Sham potential for the radial density
theory, iii) a proper implementation of a density functional for an "intrinsic"
rotational density, iv) the possible existence of a potential driving the
square root of the density, and v) the existence of many models where a density
functional can be explicitly constructed.


  We correct one erroneous statement made in our recent paper "Medial axis and
singularities".


  We show that the equality $m_1(f(x))=m_2(g(x))$ for $x$ in a neighborhood of
a point $a$ remains valid for all $x$ provided that $f$ and $g$ are open
holomorphic maps, $f(a)=g(a)=0$ and $m_1,$ $m_2$ are Minkowski functionals of
bounded balanced domains. Moreover, a polynomial relation between $f$ and $g$
is obtained. Next we generalize these results to bounded quasi-balanced
domains.
  Moreover, the main results of \cite{Ber-Piz} and \cite{Bou} are significantly
extended and their proofs are essentially simplified.


  The rapid proliferation of machine learning models across domains and
deployment settings has given rise to various communities (e.g. industry
practitioners) which seek to benchmark models across tasks and objectives of
personal value. Unfortunately, these users cannot use standard benchmark
results to perform such value-driven comparisons as traditional benchmarks
evaluate models on a single objective (e.g. average accuracy) and fail to
facilitate a standardized training framework that controls for confounding
variables (e.g. computational budget), making fair comparisons difficult. To
address these challenges, we introduce the open-source Ludwig Benchmarking
Toolkit (LBT), a personalized benchmarking toolkit for running end-to-end
benchmark studies (from hyperparameter optimization to evaluation) across an
easily extensible set of tasks, deep learning models, datasets and evaluation
metrics. LBT provides a configurable interface for controlling training and
customizing evaluation, a standardized training framework for eliminating
confounding variables, and support for multi-objective evaluation. We
demonstrate how LBT can be used to create personalized benchmark studies with a
large-scale comparative analysis for text classification across 7 models and 9
datasets. We explore the trade-offs between inference latency and performance,
relationships between dataset attributes and performance, and the effects of
pretraining on convergence and robustness, showing how LBT can be used to
satisfy various benchmarking objectives.


  We consider how to learn multi-step predictions efficiently. Conventional
algorithms wait until observing actual outcomes before performing the
computations to update their predictions. If predictions are made at a high
rate or span over a large amount of time, substantial computation can be
required to store all relevant observations and to update all predictions when
the outcome is finally observed. We show that the exact same predictions can be
learned in a much more computationally congenial way, with uniform per-step
computation that does not depend on the span of the predictions. We apply this
idea to various settings of increasing generality, repeatedly adding desired
properties and each time deriving an equivalent span-independent algorithm for
the conventional algorithm that satisfies these desiderata. Interestingly,
along the way several known algorithmic constructs emerge spontaneously from
our derivations, including dutch eligibility traces, temporal difference
errors, and averaging. This allows us to link these constructs one-to-one to
the corresponding desiderata, unambiguously connecting the `how' to the `why'.
Each step, we make sure that the derived algorithm subsumes the previous
algorithms, thereby retaining their properties. Ultimately we arrive at a
single general temporal-difference algorithm that is applicable to the full
setting of reinforcement learning.


  Following the success in advancing natural language processing and
understanding, transformers are expected to bring revolutionary changes to
computer vision. This work provides a comprehensive study on the robustness of
vision transformers (ViTs) against adversarial perturbations. Tested on various
white-box and transfer attack settings, we find that ViTs possess better
adversarial robustness when compared with MLP-Mixer and convolutional neural
networks (CNNs) including ConvNeXt, and this observation also holds for
certified robustness. Through frequency analysis and feature visualization, we
summarize the following main observations contributing to the improved
robustness of ViTs: 1) Features learned by ViTs contain less high-frequency
patterns that have spurious correlation, which helps explain why ViTs are less
sensitive to high-frequency perturbations than CNNs and MLP-Mixer, and there is
a high correlation between how much the model learns high-frequency features
and its robustness against different frequency-based perturbations. 2)
Introducing convolutional or tokens-to-token blocks for learning high-frequency
features in ViTs can improve classification accuracy but at the cost of
adversarial robustness. 3) Modern CNN designs that borrow techniques from ViTs
including activation function, layer norm, larger kernel size to imitate the
global attention, and patchify the images as inputs, etc., could help bridge
the performance gap between ViTs and CNNs not only in terms of performance, but
also certified and empirical adversarial robustness. Moreover, we show
adversarial training is also applicable to ViT for training robust models, and
sharpness-aware minimization can also help improve robustness, while
pre-training with clean images on larger datasets does not significantly
improve adversarial robustness.


  Fluctuations of the Chern-Simons gauge field in the composite Fermi liquid
description of the half-filled Landau level are pair breaking in all angular
momentum channels. For short-range electron-electron interactions these
fluctuations are sufficiently strong to drive any T=0 pairing transition first
order. For Coulomb interactions these fluctuations are weaker and a continuous
transition is possible.


  Discrete quantum trajectories of systems under random unitary gates and
projective measurements have been shown to feature transitions in the
entanglement scaling that are not encoded in the density matrix. In this paper,
we study the projective transverse field Ising model, a stochastic model with
two noncommuting projective measurements and no unitary dynamics. We
numerically demonstrate that their competition drives an entanglement
transition between two distinct steady states that both exhibit area law
entanglement, and introduce a classical but nonlocal model that captures the
entanglement dynamics completely. Exploiting a map to bond percolation, we
argue that the critical system in one dimension is described by a conformal
field theory, and derive the universal scaling of the entanglement entropy and
the critical exponent for the scaling of the mutual information of two spins
exactly. We conclude with an interpretation of the entanglement transition in
the context of quantum error correction.


  This work reveals a counter-intuitive but basic process of ionic screening in
nano-fluidic channels. Steady-state numerical simulations and mathematical
analysis show that, under significant longitudinal ionic transport, the
screening ionic charges can be locally inverted in the channels: their charge
sign becomes the same as that of the channel surface charges. The process is
identified to originate from the coupling of ionic electro-diffusion transport
and junction 2-D electrostatics. This finding may expand our understanding on
ionic screening and electrical double layers in nano-channels. Furthermore, the
charge inversion process results in a body-force torque on channel fluids,
which is a possible mechanism for vortex generation in the channels and their
nonlinear current-voltage characteristics.


  We present studies for an individual based model of three interacting
populations whose individuals are mobile in a 2D-lattice. We focus on the
pattern formation in the spatial distributions of the populations. Also
relevant is the relationship between pattern formation and features of the
populations' time series. Our model displays travelling waves solutions,
clustering and uniform distributions, all related to the parameters values. We
also observed that the regeneration rate, the parameter associated to the
primary level of trophic chain, the plants, regulated the presence of
predators, as well as the type of spatial configuration.


  We present an optical-to-radio study of the BL Lac object S4 0954+658
observations during 1998-2023. The measurements were obtained with the SAO RAS
Zeiss-1000 1-m and AS-500/2 0.5-m telescopes in 2003-2023, with the RATAN-600
radio telescope at 1.25 (0.96, 1.1), 2.3, 4.7 (3.7, 3.9), 8.2 (7.7), 11.2, 22.3
(21.7) GHz in 1998-2023, with the IAA RAS RT-32 Zelenchukskaya and Badary
telescopes at 5.05 and 8.63 GHz in 2020--2023, and with the RT-22 single-dish
telescope of CrAO RAS at 36.8 GHz in 2009-2023. In this period the blazar had
been showing extremely high broadband activity with the variability amplitude
of flux densities up to 70-100% both in the optical and radio domains. In the
period of 2014-2023 the blazar had been showing the historically highest
activity in the radio wavelengths, and we detected multiple radio flares of
varying amplitude and duration. The large flares last on average from 0.3 to 1
year at 22-36.8 GHz and slightly longer at 5-11.2 GHz. The optical flares are
shorter and last 7-50 days. In the most active epoch of 2018-2023 the
characteristic time scale $\tau$ of variation at 5-22 GHz is about 100 days and
about 1000 days for the state with lower activity in 2009-2014. We found a
general correlation between the optical, radio, and $\gamma$-ray flux
variations, which suggests that we observe the same photon population from
different emission regions. We estimated linear size of this region as 0.5-2 pc
for different epochs. A broadband two components radio spectrum of S4 0954+658
jet was modelled by using both electrons and protons as emitting particles. It
is shown that the synchrotron radio waves in this AGN may be generated by
relativistic protons.


  The low-energy strong interaction of antikaons (K-) with nuclei has many
facets and rep- resents a lively and challenging research field. It is
interconnected to the peculiar role of strangeness, since the strange quark is
rather light, but still much heavier than the up and down quarks. Thus, when
strangeness is involved one has to deal with spontaneous and explicit symmetry
breaking in QCD. It is well known that the antikaon interaction with nucleons
is attractive, but how strong ? Is the interaction strong enough to bind
nucleons to form kaonic nuclei and, if so, what are the properties (binding
energy, decay width)? There are controversial indications for such bound states
and new results are expected to come soon. The existence of antikaon mediated
bound states might have important consequences since it would open the
possibility for the formation of cold baryonic matter of high density which
might have a severe impact in astrophysics for the understanding of the
composi- tion of compact (neutron) stars. New experimental opportunities could
be provided by the AMADEUS experiment at the DA?NE electron-positron collider
at LNF-INFN (Frascati, Italy). Pre-AMADEUS studies on the antikaon interaction
with nuclei are carried out by analysis of data collected by KLOE in till 2005
and in special data runs using a carbon target insert. Studies for the
dedicated AMADEUS detector setup taking advantage of the low-energy antikaons
from Phi-meson decay delivered by DAFNE are in progress. Some re- sults
obtained so far and the perspectives of the AMADEUS experiment are presented
and discussed.


  Let $\fa$ denote an ideal of a $d$-dimensional Gorenstein local ring $R$ and
$M$ and $N$ two finitely generated $R$-modules with $\pd M< \infty$. It is
shown that $H^d_{\fa}(M,N)=0$ if and only if $\dim \hat{R}\big/
\fa\hat{R}+\fp>0$ for all
$\fp\in\Ass_{\hat{R}}\hat{M}\cap\Supp_{\hat{R}}\hat{N}$.


  We study the corrupted bandit problem, i.e. a stochastic multi-armed bandit
problem with $k$ unknown reward distributions, which are heavy-tailed and
corrupted by a history-independent adversary or Nature. To be specific, the
reward obtained by playing an arm comes from corresponding heavy-tailed reward
distribution with probability $1-\varepsilon \in (0.5,1]$ and an arbitrary
corruption distribution of unbounded support with probability $\varepsilon \in
[0,0.5)$.
  First, we provide $\textit{a problem-dependent lower bound on the regret}$ of
any corrupted bandit algorithm. The lower bounds indicate that the corrupted
bandit problem is harder than the classical stochastic bandit problem with
sub-Gaussian or heavy-tail rewards.
  Following that, we propose a novel UCB-type algorithm for corrupted bandits,
namely HubUCB, that builds on Huber's estimator for robust mean estimation.
Leveraging a novel concentration inequality of Huber's estimator, we prove that
HubUCB achieves a near-optimal regret upper bound.
  Since computing Huber's estimator has quadratic complexity, we further
introduce a sequential version of Huber's estimator that exhibits linear
complexity. We leverage this sequential estimator to design SeqHubUCB that
enjoys similar regret guarantees while reducing the computational burden.
  Finally, we experimentally illustrate the efficiency of HubUCB and SeqHubUCB
in solving corrupted bandits for different reward distributions and different
levels of corruptions.


  We obtain the bi-Hamiltonian structure of the super KP hierarchy based on the
even super KP operator $\Lambda = \theta^{2} + \sum^{\infty}_{i=-2}U_{i}
\theta^{-i-1}$, as a supersymmetric extension of the ordinary KP bi-Hamiltonian
structure. It is expected to give rise to a universal super $W$-algebra
incorporating all known extended superconformal $W_{N}$ algebras by reduction.
We also construct the super BKP hierarchy by imposing a set of anti-self-dual
constraints on the super KP hierarchy.


  We consider a three-dimensional (3D) lattice model associated with the
intertwiner of the quantized coordinate ring $A_q(sl_3)$, and introduce a
family of layer to layer transfer matrices on $m\times n$ square lattice. By
using the tetrahedron equation we derive their commutativity and bilinear
relations mixing various boundary conditions. At $q=0$ and $m=n$, they lead to
a new proof of the steady state probability of the $n$-species totally
asymmetric zero range process obtained recently by the authors, revealing the
3D integrability in the matrix product construction.


  This technical note studies the distributed optimization problem of a sum of
nonsmooth convex cost functions with local constraints. At first, we propose a
novel distributed continuous-time projected algorithm, in which each agent
knows its local cost function and local constraint set, for the constrained
optimization problem. Then we prove that all the agents of the algorithm can
find the same optimal solution, and meanwhile, keep the states bounded while
seeking the optimal solutions. We conduct a complete convergence analysis by
employing nonsmooth Lyapunov functions for the stability analysis of
differential inclusions. Finally, we provide a numerical example for
illustration.


  This paper proposes a novel approach for storing and retrieving massive DNA
sequences.. The method is based on a perceptual hash function, commonly used to
determine the similarity between digital images, that we adapted for DNA
sequences. Perceptual hash function presented here is based on a Discrete
Cosine Transform Sign Only (DCT-SO). Each nucleotide is encoded as a fixed gray
level intensity pixel and the hash is calculated from its significant frequency
characteristics. This results to a drastic data reduction between the sequence
and the perceptual hash. Unlike cryptographic hash functions, perceptual hashes
are not affected by "avalanche effect" and thus can be compared. The similarity
distance between two hashes is estimated with the Hamming Distance, which is
used to retrieve DNA sequences. Experiments that we conducted show that our
approach is relevant for storing massive DNA sequences, and retrieving them.


  Following recent work in which events which may correspond to a bound
tetraneutron ($^4$n) were observed, it is pointed out that from the theoretical
perspective the two-body nucleon-nucleon force cannot by itself bind four
neutrons, even if it can bind a dineutron.
  A very strong phenomenological four-nucleon (4N) force is needed in order to
bind the tetraneutron. Such a 4N force, if it existed, would bind $^4$He by
about 100 MeV. Alternative experiments such as ($^8$He,$^4$n) are proposed to
search for the tetraneutron.


  We examine the Dzyaloshinski-Moriya (DM) and
Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions in thermal
equilibrium submitted to the anisotropic Heisenberg two-qubit model in an
inhomogeneous magnetic field. The steerability between the two qubits is
evaluated using quantum steering. The concurrence serves as a witness to
quantum entanglement. Both the extracted work and the ideal efficiency of the
two qubits are quantified. We discuss how quantum correlations behave in
relation to the bath's temperature and the
Kaplan-Shekhtman-Entin-Wohlman-Aharony coupling parameter. We find that the
nonclassical correlations in a two-qubit Heisenberg XYZ Model are fragile under
thermal effects. Nevertheless, the results indicate that the concurrence is
stronger than quantum steering under thermal effects. We obtained that an
extraction of work is comparable with the bare energies.


  We study the electronic states of quasi-one-dimensional organic conductors
using the single band Hubbard model at half-filling. We treat the effects of
the on-site Coulomb interaction by the fluctuation-exchange (FLEX) method, and
calculate the phase diagram and physical properties. The calculated pressure
dependence of the Neel temperature coincides well with the experimental one. We
also show that a pseudogap is formed in the density of states near the chemical
potential and that d-wave superconductivity appears next to the
antiferromagnetic state. Moreover the NMR relaxation rate increases on cooling
in the low-temperature region.


  This note reviews the model of computation generated by photonic circuits,
comprising edges that are traversed by photons in a single time-bin and
vertices given by idealised lossless beam splitters and phase shifters. The
circuit model is abstracted as a representation of the symmetric monoidal
category of unitary matrices on the bosonic Fock space of multimode photons. A
diagrammatic language, designed to aid with the understanding and development
of photonic algorithms, is presented that encapsulates the category properties
of this representation. As demonstrations of the formalism, the boson sampling
scheme and the protocol of Knill, Laflamme and Milburn are developed on the
single-rail photonic computer, and a parity-based model for the qudit is
investigated.


  The automatic generation of educational questions will play a key role in
scaling online education, enabling self-assessment at scale when a global
population is manoeuvring their personalised learning journeys. We develop
\textit{EduQG}, a novel educational question generation model built by adapting
a large language model. Our extensive experiments demonstrate that
\textit{EduQG} can produce superior educational questions by further
pre-training and fine-tuning a pre-trained language model on the scientific
text and science question data.


  Observational data show that the correlation between supermassive black holes
(MBH) and galaxy bulge (Mbulge) masses follows a nearly linear trend, and that
the correlation is strongest with the bulge rather than the total stellar mass
(Mgal). With increasing redshift, the ratio Gamma=MBH/Mbulge relative to z=0
also seems to be larger for MBH >~ 10^{8.5} Msol. This study looks more closely
at statistics to better understand the creation and observations of the
MBH-Mbulge correlation. It is possible to show that if galaxy merging
statistics can drive the correlation, minor mergers are responsible for causing
a *convergence to linearity* most evident at high masses, whereas major mergers
have a central limit convergence that more strongly *reduces the scatter*. This
statistical reasoning is agnostic about galaxy morphology. Therefore, combining
statistical prediction (more major mergers ==> tighter correlation) with
observations (bulges = tightest correlation), would lead one to conclude that
more major mergers (throughout an entire merger tree, not just the primary
branch) give rise to more prominent bulges. With regard to controversial
findings that Gamma increases with redshift, this study shows why the
luminosity function (LF) bias argument, taken correctly at face value,
strengthens rather than weakens the results. However, correcting for LF bias is
unwarranted because the BH mass scale for quasars is bootstrapped to the
MBH-Sigma* correlation in normal galaxies at z=0, and quasar-quasar comparisons
are internally consistent. In Monte-Carlo simulations, high Gamma objects are
"under-merged" galaxies that take longer to converge to linearity via minor
mergers. Another evidence that the galaxies are undermassive at z >~ 2 for
their MBH is that the quasar hosts are very compact for their expected mass.


  The state complexity of the result of a regular operation is often positively
correlated with the number of distinct transformations induced by letters in
the minimal deterministic finite automaton of the input languages. That is,
more transformations in the inputs means higher state complexity in the output.
When this correlation holds, the state complexity of a unary operation can be
maximized using languages in which there is one letter corresponding to each
possible transformation; for operations of higher arity, we can use $m$-tuples
of languages in which there is one letter corresponding to each possible
$m$-tuple of transformations. In this way, a small set of languages can be used
as witnesses for many common regular operations, eliminating the need to search
for witnesses -- though at the expense of using very large alphabets. We
formalize this approach and examine its limitations. We define a class of
"uniform" operations for which this approach works; the class is closed under
composition and includes common operations such as star, concatenation,
reversal, union, and complement. Our main result is that the worst-case state
complexity of a uniform operation can be determined by considering a finite set
of witnesses, and this set depends only on the arity of the operation and the
state complexities of the inputs.


  Motivated by distributed schedulers that combine the power-of-d-choices with
late binding and systems that use replication with cancellation-on-start, we
study the performance of the LL(d) policy which assigns a job to a server that
currently has the least workload among d randomly selected servers in
large-scale homogeneous clusters. We consider general service time
distributions and propose a partial integro-differential equation to describe
the evolution of the system. This equation relies on the earlier proven ansatz
for LL(d) which asserts that the workload distribution of any finite set of
queues becomes independent of one another as the number of servers tends to
infinity. Based on this equation we propose a fixed point iteration for the
limiting workload distribution and study its convergence. For exponential job
sizes we present a simple closed form expression for the limiting workload
distribution that is valid for any work-conserving service discipline as well
as for the limiting response time distribution in case of
first-come-first-served scheduling. We further show that for phase-type
distributed job sizes the limiting workload and response time distribution can
be expressed via the unique solution of a simple set of ordinary differential
equations. Numerical and analytical results that compare response time of the
classic power-of-d-choices algorithm and the LL(d) policy are also presented
and the accuracy of the limiting response time distribution for finite systems
is illustrated using simulation.


  In this study, we propose and analyze in simulations a new, highly flexible
method of implementing synaptic plasticity in a wafer-scale, accelerated
neuromorphic hardware system. The study focuses on globally modulated STDP, as
a special use-case of this method. Flexibility is achieved by embedding a
general-purpose processor dedicated to plasticity into the wafer. To evaluate
the suitability of the proposed system, we use a reward modulated STDP rule in
a spike train learning task. A single layer of neurons is trained to fire at
specific points in time with only the reward as feedback. This model is
simulated to measure its performance, i.e. the increase in received reward
after learning. Using this performance as baseline, we then simulate the model
with various constraints imposed by the proposed implementation and compare the
performance. The simulated constraints include discretized synaptic weights, a
restricted interface between analog synapses and embedded processor, and
mismatch of analog circuits. We find that probabilistic updates can increase
the performance of low-resolution weights, a simple interface between analog
synapses and processor is sufficient for learning, and performance is
insensitive to mismatch. Further, we consider communication latency between
wafer and the conventional control computer system that is simulating the
environment. This latency increases the delay, with which the reward is sent to
the embedded processor. Because of the time continuous operation of the analog
synapses, delay can cause a deviation of the updates as compared to the not
delayed situation. We find that for highly accelerated systems latency has to
be kept to a minimum. This study demonstrates the suitability of the proposed
implementation to emulate the selected reward modulated STDP learning rule.


  Recent advances in two-dimensional (2D) magnetism have heightened interest in
layered magnetic materials due to their potential for spintronics. In
particular, layered semiconducting antiferromagnets exhibit intriguing
low-dimensional semiconducting behavior with both charge and spin as carrier
controls. However, synthesis of these compounds is challenging and remains
rare. Here, we conducted firstprinciples based high-throughput search to screen
potentially stable mixed metal phosphorous trichalcogenides (MM'P2X6, where M
and M' are transition metals and X is a chalcogenide) that have a wide range of
tunable bandgaps and interesting magnetic properties. Among the potential
candidates, we successfully synthesized a stable semiconducting layered
magnetic material, CdFeP2Se6, that exhibits a short-range antiferromagnetic
order at TN = 21 K with an indirect band gap of 2.23 eV. Our work suggests that
highthroughput screening assisted synthesis be an effective method for layered
magnetic materials discovery.


  We study the computational complexity of fairly allocating a set of
indivisible items under externalities. In this recently-proposed setting, in
addition to the utility the agent gets from their bundle, they also receive
utility from items allocated to other agents. We focus on the extended
definitions of envy-freeness up to one item (EF1) and of envy-freeness up to
any item (EFX), and we provide the landscape of their complexity for several
different scenarios. We prove that it is NP-complete to decide whether there
exists an EFX allocation, even when there are only three agents, or even when
there are only six different values for the items. We complement these negative
results by showing that when both the number of agents and the number of
different values for items are bounded by a parameter the problem becomes
fixed-parameter tractable. Furthermore, we prove that two-valued and
binary-valued instances are equivalent and that EFX and EF1 allocations
coincide for this class of instances. Finally, motivated from real-life
scenarios, we focus on a class of structured valuation functions, which we term
agent/item-correlated. We prove their equivalence to the ``standard'' setting
without externalities. Therefore, all previous results for EF1 and EFX apply
immediately for these valuations.


  We present a learning-based planner that aims to robustly drive a vehicle by
mimicking human drivers' driving behavior. We leverage a mid-to-mid approach
that allows us to manipulate the input to our imitation learning network
freely. With that in mind, we propose a novel feedback synthesizer for data
augmentation. It allows our agent to gain more driving experience in various
previously unseen environments that are likely to encounter, thus improving
overall performance. This is in contrast to prior works that rely purely on
random synthesizers. Furthermore, rather than completely commit to imitating,
we introduce task losses that penalize undesirable behaviors, such as
collision, off-road, and so on. Unlike prior works, this is done by introducing
a differentiable vehicle rasterizer that directly converts the waypoints output
by the network into images. This effectively avoids the usage of heavyweight
ConvLSTM networks, therefore, yields a faster model inference time. About the
network architecture, we exploit an attention mechanism that allows the network
to reason critical objects in the scene and produce better interpretable
attention heatmaps. To further enhance the safety and robustness of the
network, we add an optional optimization-based post-processing planner
improving the driving comfort. We comprehensively validate our method's
effectiveness in different scenarios that are specifically created for
evaluating self-driving vehicles. Results demonstrate that our learning-based
planner achieves high intelligence and can handle complex situations. Detailed
ablation and visualization analysis are included to further demonstrate each of
our proposed modules' effectiveness in our method.


  We study the minimizers of the sum of the principal Dirichlet eigenvalue of
the negative Laplacian and the perimeter with respect to a general norm in the
class of Jordan domains in the plane. This is equivalent (modulo scaling) to
minimizing the said eigenvalue (or the base frequency of a drum of this shape)
subject to a hard constraint on the perimeter. We show that, for all norms, a
minimizer exists, is unique up to spatial translations and is convex but not
necessarily smooth. We give conditions on the norm that characterize the
appearance of facets and corners. We also demonstrate that near minimizers have
to be close to the optimal ones in the Hausdorff distance. Our motivation for
considering this class of variational problems comes from a study of random
walks in random environment interacting through the boundary of their support.


  This approach permits much finer adjustments of the beam direction and
position when compared to other beam steering techniques of the same mechanical
precision. This results in a much increased precision, accuracy and mechanical
stability. A precision of better than 5 {\mu}rad and 5 {\mu}m is demonstrated,
resulting in a resolution in coupling efficiency of 0.1%. Together with the
added flexibility of an additional beam steering element, this allows a great
simplification of the design of the fiber coupler, which normally is the most
complex and sensitive element on an optical fiber breadboard. We demonstrate a
fiber to fiber coupling efficiency of more than 89.8%, with a stability of 0.2%
in a stable temperature environment and 2% fluctuations over a temperature
range from 10C to 40C over a measurement time of 14 hours. Furthermore, we do
not observe any non-reversible change in the coupling efficiency after
performing a series of tests over large temperature variations. This technique
finds direct application in proposed missions for quantum experiments in space,
e.g.where laser beams are used to cool and manipulate atomic clouds.


  Because of the growing interest for mobile device and pervasive applications
deployed on cloud computing, the providing of intelligent and ubiquitous
context-aware applications that take into account the user's context is one of
the main challenges in future applications. In this article we consider how to
augment applications aware context used by mobiles device and deployed on cloud
computing. The behavior of these applications should depend not only on their
internal state and user interactions but also on the context sensed during
their execution. Indeed, our approach based on two essential mechanisms,
context sensoring and context reasoning. We consider the information acquired
by the context sensoring as a product line and we use feature models to
represent this information received, the services provided by cloud provider,
the available resources and constraints. At the context reasoning step, the
context provisioning feature model (CSCAFM) algorithm permits to generate
service context-aware that fulfils the requirements of use ensures a certain
level of performance of resources.


  Since the early days of Tomita-Takesaki theory, it is known that a von
Neumann algebra $M$ that admits a state $\varphi$ with trivial centralizer
$M_\varphi$ must be a type III$_1$ factor, but the converse remained open. We
solve this problem and prove that such ergodic states form a dense $G_\delta$
set among all faithful normal states on any III$_1$ factor with separable
predual. Through Connes' Radon-Nikodym cocycle theorem, this problem is related
to the existence of ergodic cocycle perturbations for outer group actions,
which we consider in the second part of the paper.


  Achieving high-fidelity entangling operations between qubits consistently is
essential for the performance of multi-qubit systems and is a crucial factor in
achieving fault-tolerant quantum processors. Solid-state platforms are
particularly exposed to errors due to materials-induced variability between
qubits, which leads to performance inconsistencies. Here we study the errors in
a spin qubit processor, tying them to their physical origins. We leverage this
knowledge to demonstrate consistent and repeatable operation with above 99%
fidelity of two-qubit gates in the technologically important silicon
metal-oxide-semiconductor (SiMOS) quantum dot platform. We undertake a detailed
study of these operations by analysing the physical errors and fidelities in
multiple devices through numerous trials and extended periods to ensure that we
capture the variation and the most common error types. Physical error sources
include the slow nuclear and electrical noise on single qubits and contextual
noise. The identification of the noise sources can be used to maintain
performance within tolerance as well as inform future device fabrication.
Furthermore, we investigate the impact of qubit design, feedback systems, and
robust gates on implementing scalable, high-fidelity control strategies. These
results are achieved by using three different characterization methods, we
measure entangling gate fidelities ranging from 96.8% to 99.8%. Our analysis
tools identify the causes of qubit degradation and offer ways understand their
physical mechanisms. These results highlight both the capabilities and
challenges for the scaling up of silicon spin-based qubits into full-scale
quantum processors.


  We investigate SUSY breaking mediated through the deformation of the
spacetime geometry due to the backreaction of a nontrivial configuration of a
bulk scalar field. To illustrate its features, we work with a toy model in
which the bulk is four dimensions. Using the superconformal formulation of
SUGRA, we provide a systematic method of deriving the 3D effective action
expressed by the superfields.


  Raking is widely used in categorical data modeling and survey practice but
faced with methodological and computational challenges. We develop a Bayesian
paradigm for raking by incorporating the marginal constraints as a prior
distribution via two main strategies: 1) constructing the solution subspaces
via basis functions or projection matrix and 2) modeling soft constraints. The
proposed Bayes-raking estimation integrates the models for the margins, the
sample selection and response mechanism, and the outcome, with the capability
to propagate all sources of uncertainty. Computation is done via Stan, and
codes are ready for public use. Simulation studies show that Bayes-raking can
perform as well as raking with large samples and outperform in terms of
validity and efficiency gains, especially with a sparse contingency table or
dependent raking factors. We apply the new method to the Longitudinal Study of
Wellbeing study and demonstrate that model-based approaches significantly
improve inferential reliability and substantive findings as a unified survey
inference framework.


  In the context of personalized medicine, text mining methods pose an
interesting option for identifying disease-gene associations, as they can be
used to generate novel links between diseases and genes which may complement
knowledge from structured databases. The most straightforward approach to
extract such links from text is to rely on a simple assumption postulating an
association between all genes and diseases that co-occur within the same
document. However, this approach (i) tends to yield a number of spurious
associations, (ii) does not capture different relevant types of associations,
and (iii) is incapable of aggregating knowledge that is spread across
documents. Thus, we propose an approach in which disease-gene co-occurrences
and gene-gene interactions are represented in an RDF graph. A machine
learning-based classifier is trained that incorporates features extracted from
the graph to separate disease-gene pairs into valid disease-gene associations
and spurious ones. On the manually curated Genetic Testing Registry, our
approach yields a 30 points increase in F1 score over a plain co-occurrence
baseline.


  In this paper new infinite families of linear binary completely transitive
codes are presented. They have covering radius $\rho = 3$ and 4, and are a half
part of the binary Hamming and the binary extended Hamming code of length
$n=2^m-1$ and $2^m$, respectively, where $m$ is even. From these new completely
transitive codes, in the usual way, i.e., as coset graphs, new presentations of
infinite families of distance transitive coset graphs of diameter three and
four, respectively, are constructed.


  We study the optimal sample complexity of a given workload of linear queries
under the constraints of differential privacy. The sample complexity of a query
answering mechanism under error parameter $\alpha$ is the smallest $n$ such
that the mechanism answers the workload with error at most $\alpha$ on any
database of size $n$. Following a line of research started by Hardt and Talwar
[STOC 2010], we analyze sample complexity using the tools of asymptotic convex
geometry. We study the sensitivity polytope, a natural convex body associated
with a query workload that quantifies how query answers can change between
neighboring databases. This is the information that, roughly speaking, is
protected by a differentially private algorithm, and, for this reason, we
expect that a "bigger" sensitivity polytope implies larger sample complexity.
Our results identify the mean Gaussian width as an appropriate measure of the
size of the polytope, and show sample complexity lower bounds in terms of this
quantity. Our lower bounds completely characterize the workloads for which the
Gaussian noise mechanism is optimal up to constants as those having
asymptotically maximal Gaussian width.
  Our techniques also yield an alternative proof of Pisier's Volume Number
Theorem which also suggests an approach to improving the parameters of the
theorem.


  Manufacturing new soft materials with specific optical, mechanical and
magnetic properties is a significant challenge. Assembling and manipulating
colloidal particles at fluid interfaces is a promising way to make such
materials. We use lattice-Boltzmann simulations to investigate the response of
magnetic ellipsoidal particles adsorbed at liquid-liquid interfaces to external
magnetic fields. We provide further evidence for the first-order orientation
phase transition predicted by Bresme and Faraudo [Journal of Physics: Condensed
Matter 19 (2007), 375110]. We show that capillary interface deformations around
the ellipsoidal particle significantly affect the tilt-angle of the particle
for a given dipole-field strength, altering the properties of the orientation
transition. We propose scaling laws governing this transition, and suggest how
to use these deformations to facilitate particle assembly at fluid-fluid
interfaces.


  In this work, the charm quark mass is obtained from a QCD sum rule analysis
of the charmonium system. In our investigation we include results from
nonrelativistic QCD at next-to-next-to-leading order. Using the pole mass
scheme, we obtain a value of $M_c=1.70\pm 0.13$ GeV for the charm pole mass.
The introduction of a potential-subtracted mass leads to an improved scale
dependence. The running ${\rm \bar{MS}}$-mass is then determined to be
$m_c(m_c) = 1.23 \pm 0.09$ GeV.


  With the increasing emphasis on data privacy, the significance of machine
unlearning has grown substantially. Class unlearning, which involves enabling a
trained model to forget data belonging to a specific class learned before, is
important as classification tasks account for the majority of today's machine
learning as a service (MLaaS). Retraining the model on the original data,
excluding the data to be forgotten (a.k.a forgetting data), is a common
approach to class unlearning. However, the availability of original data during
the unlearning phase is not always guaranteed, leading to the exploration of
class unlearning with restricted data access. While current unlearning methods
with restricted data access usually generate proxy sample via the trained
neural network classifier, they typically focus on training and forgetting
balanced data. However, the imbalanced original data can cause trouble for
these proxies and unlearning, particularly when the forgetting data consists
predominantly of the majority class. To address this issue, we propose the
GENerative Imbalanced Unlearning (GENIU) framework. GENIU utilizes a
Variational Autoencoder (VAE) to concurrently train a proxy generator alongside
the original model. These generated proxies accurately represent each class and
are leveraged in the unlearning phase, eliminating the reliance on the original
training data. To further mitigate the performance degradation resulting from
forgetting the majority class, we introduce an in-batch tuning strategy that
works with the generated proxies. GENIU is the first practical framework for
class unlearning in imbalanced data settings and restricted data access,
ensuring the preservation of essential information for future unlearning.
Experimental results confirm the superiority of GENIU over existing methods,
establishing its effectiveness in empirical scenarios.


  A new beam injection scheme is proposed for the Fermilab Booster to increase
beam brightness. The beam is injected on the deceleration part of the
sinusoidal magnetic ramp and capture is started immediately after the
injection. During the entire capture process we impose Pdot=0 in a changing B
field. Beam dynamics simulations clearly show that this method is very
efficient with no longitudinal beam emittance dilution and no beam loss. As a
consequence of preserved emittance, the required RF power on a typical Booster
cycle can be reduced by ~30% as compared with the scheme in current operation.
Further, we also propose snap bunch rotation at extraction to reduce dP/P of
the beam to improve the slip-stacking efficiency in MI/RR.


  The pressure induced bcc to hcp transition in Fe has been investigated via
ab-initio electronic structure calculations. It is found by the disordered
local moment (DLM) calculations that the temperature induced spin fluctuations
result in the decrease of the energy of Burgers type lattice distortions and
softening of the transverse $N$-point $TA_1$ phonon mode with $[\bar{1}10]$
polarization. As a consequence, spin disorder in an system leads to the
increase of the amplitude of atomic displacements. On the other hand, the
exchange coupling parameters obtained in our calculations strongly decrease at
large amplitude of lattice distortions. This results in a mutual interrelation
of structural and magnetic degrees of freedom leading to the instability of the
bcc structure under pressure at finite temperature.


  Model-based reinforcement learning (RL) algorithms allow us to combine
model-generated data with those collected from interaction with the real system
in order to alleviate the data efficiency problem in RL. However, designing
such algorithms is often challenging because the bias in simulated data may
overshadow the ease of data generation. A potential solution to this challenge
is to jointly learn and improve model and policy using a universal objective
function. In this paper, we leverage the connection between RL and
probabilistic inference, and formulate such an objective function as a
variational lower-bound of a log-likelihood. This allows us to use expectation
maximization (EM) and iteratively fix a baseline policy and learn a variational
distribution, consisting of a model and a policy (E-step), followed by
improving the baseline policy given the learned variational distribution
(M-step). We propose model-based and model-free policy iteration (actor-critic)
style algorithms for the E-step and show how the variational distribution
learned by them can be used to optimize the M-step in a fully model-based
fashion. Our experiments on a number of continuous control tasks show that
despite being more complex, our model-based (E-step) algorithm, called {\em
variational model-based policy optimization} (VMBPO), is more sample-efficient
and robust to hyper-parameter tuning than its model-free (E-step) counterpart.
Using the same control tasks, we also compare VMBPO with several
state-of-the-art model-based and model-free RL algorithms and show its sample
efficiency and performance.


  This paper presents our research regarding spoiler detection in reviews. In
this use case, we describe the method of fine-tuning and organizing the
available text-based model tasks with the latest deep learning achievements and
techniques to interpret the models' results.
  Until now, spoiler research has been rarely described in the literature. We
tested the transfer learning approach and different latest transformer
architectures on two open datasets with annotated spoilers (ROC AUC above 81\%
on TV Tropes Movies dataset, and Goodreads dataset above 88\%). We also
collected data and assembled a new dataset with fine-grained annotations. To
that end, we employed interpretability techniques and measures to assess the
models' reliability and explain their results.


  Machine learning in atomistic materials science has grown to become a
powerful tool, with most approaches focusing on atomic arrangements, typically
decomposed into local atomic environments. This approach, while well-suited for
machine-learned interatomic potentials, is conceptually at odds with learning
complex intrinsic properties of materials, often driven by spectral properties
commonly represented in reciprocal space (e.g., band gaps or mobilities) which
cannot be readily atomically partitioned. For such applications, methods which
represent the electronic rather than the atomic structure could be more
promising. In this work, we present a general framework focused on
electronic-structure descriptors which take advantage of the natural symmetries
and inherent interpretability of physical models. Using this framework, we
formulate two such representations and apply them respectively to measuring the
similarity of carbon nanotubes and barium titanate polymorphs, and to the
discovery of novel transparent conducting materials (TCMs) in the Materials
Cloud 3D database (MC3D). A random forest classifier trained on 1% of the
materials in the MC3D is able to correctly label 76% of entries in database
which meet common screening criteria for promising TCMs.


  Processes in which a jet recoils against an electroweak boson complement
studies of jet quenching in heavy ion collisions at the LHC. As the boson does
not interact strongly it escapes the dense medium unmodified and thus provides
a more direct access to the hard scattering kinematics than can be obtained in
di-jet events. First measurements of jet modification in these processes are
now available from the LHC experiments and will improve greatly with better
statistics in the future. We present an extension of JEWEL to boson-jet
processes. JEWEL is a dynamical framework for jet evolution in a dense
background based on perturbative QCD, that is in agreement with a large variety
of jet observables. We also obtain a good description of the CMS and ATLAS data
for y+jet and Z+jet processes at 2.76 TeV and 5.02 TeV.


  We investigate the possibilities of generation of solitons and vortices in a
degenerate gas of neutral fermionic atoms. In analogy with, already
experimentally demonstrated, technique applied to gaseous Bose-Einstein
condensate we propose the phase engineering of a Fermi gas as a practical route
to excited states with solitons and vortices. We stress that solitons and
vortices appear even in a noninteracting fermionic gas. For solitons, in a
system with sufficiently large number of fermions and appropriate trap
configuration, the Pauli blocking acts as the interaction between particles.


  The thermodynamic description of dislocation glide in crystals depends
crucially on the shape of the Peierls barrier that the dislocation has to
overcome when moving in the lattice. While the height of this barrier can be
obtained unequivocally using saddle-point search algorithms such as the Nudged
Elastic Band (NEB) method, its exact shape depends on the chosen approximation
of the transition pathway of the system. The purpose of this paper is to
formulate a procedure that allows to identify the position of the dislocation
directly from the displacements of atoms in its core. We investigate the
performance of this model by calculating curved paths of a 1/2[111] screw
dislocation in tungsten from a series of images obtained recently using the NEB
method at zero applied stress and for positive/negative shear stresses
perpendicular to the slip direction. The Peierls barriers plotted along these
curved paths are shown to be quite different from those obtained previously by
assuming the straight dislocation path.


  Encrypted cloud storage services are steadily increasing in popularity, with
many commercial solutions currently available. In such solutions, the cloud
storage is trusted for data availability, but not for confidentiality.
Additionally, the user's device is considered secure, and the user is expected
to behave correctly.
  We argue that such assumptions are not met in reality: e.g., users routinely
forget passwords and fail to make backups, and users' devices get stolen or
become infected with malware. Therefore, we consider a more extensive threat
model, where users' devices are susceptible to attacks and common human errors
are possible. Given this model, we analyze 10 popular commercial services and
show that none of them provides good confidentiality and data availability.
  Motivated by the lack of adequate solutions in the market, we design a novel
scheme called Two-Factor Encryption (2FE) that draws inspiration from
two-factor authentication and turns file encryption and decryption into an
interactive process where two user devices, like a laptop and a smartphone,
must interact. 2FE provides strong confidentiality and availability guarantees,
as it withstands compromised cloud storage, one stolen or compromised user
device at a time, and various human errors. 2FE achieves this by leveraging
secret sharing with additional techniques such as oblivious pseudorandom
functions and zero-knowledge proofs. We evaluate 2FE experimentally and show
that its performance overhead is small. Finally, we explain how our approach
can be adapted to other related use cases such as cryptocurrency wallets.


  One of the remaining issues in the problems of dwarf novae is whether or not
enhanced mass transfer due to irradiation of the secondary stars could occur
during outbursts. In a previous paper (Osaki and Meyer 2003), we presented a
theoretical analysis that shows no appreciable enhancement of the mass outflow
rate. This conclusion is challenged by Smak (2004) who claims that equations
used in our analysis were incorrect and that in systems with short orbital
periods substantial enhancement could occur. In this letter, we examine the
origin of such divergent conclusions. We show that Smak's solutions are
unacceptable from the standpoint of the equation of continuity and that our
analysis is an appropriate one to treat this problem.


  We study the problem of learning personalized decision policies from
observational data while accounting for possible unobserved confounding.
Previous approaches, which assume unconfoundedness, i.e., that no unobserved
confounders affect both the treatment assignment as well as outcome, can lead
to policies that introduce harm rather than benefit when some unobserved
confounding is present, as is generally the case with observational data.
Instead, since policy value and regret may not be point-identifiable, we study
a method that minimizes the worst-case estimated regret of a candidate policy
against a baseline policy over an uncertainty set for propensity weights that
controls the extent of unobserved confounding. We prove generalization
guarantees that ensure our policy will be safe when applied in practice and
will in fact obtain the best-possible uniform control on the range of all
possible population regrets that agree with the possible extent of confounding.
We develop efficient algorithmic solutions to compute this confounding-robust
policy. Finally, we assess and compare our methods on synthetic and
semi-synthetic data. In particular, we consider a case study on personalizing
hormone replacement therapy based on observational data, where we validate our
results on a randomized experiment. We demonstrate that hidden confounding can
hinder existing policy learning approaches and lead to unwarranted harm, while
our robust approach guarantees safety and focuses on well-evidenced
improvement, a necessity for making personalized treatment policies learned
from observational data reliable in practice.


  We present a simple sequential sentence encoder for multi-domain natural
language inference. Our encoder is based on stacked bidirectional LSTM-RNNs
with shortcut connections and fine-tuning of word embeddings. The overall
supervised model uses the above encoder to encode two input sentences into two
vectors, and then uses a classifier over the vector combination to label the
relationship between these two sentences as that of entailment, contradiction,
or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements
over existing encoders on matched and mismatched multi-domain natural language
inference (top non-ensemble single-model result in the EMNLP RepEval 2017
Shared Task (Nangia et al., 2017)). Moreover, they achieve the new
state-of-the-art encoding result on the original SNLI dataset (Bowman et al.,
2015).


  Chiral Quark Soliton Model has been successfully applied to describe heavy
baryon spectrum, both for charm and bottom, leading to the conclusion that the
heavy quark has no effect on the soliton. This suggests that replacing a heavy
quark by a heavy anti-diquark $\bar{Q}\bar{Q}$ in color triplet should give a
viable description of heavy tetraquarks. We follow this strategy to compute
tetraquark masses. To estimate heavy diquark masses we use Cornell potential
with appropriately rescaled parameters. The lightest charm tetraquark is 70 MeV
above the $DD^*$ threshold. On the contrary, both non-strange and strange
bottom tetraquarks are bound by approximately 140 MeV and 60 MeV, respectively.


  We examine diagonal combs, a recently identified class of infinite metric
graphs whose properties depend on one parameter. These graphs exhibit a
fascinating regime where they possess infinite volume while maintaining purely
discrete spectrum for the Neumann Laplacian. In this regime, we establish
polynomial upper and lower bounds on the $k$-th eigenvalue, revealing that the
eigenvalues grow at a rate strictly slower than quadratic. However, once the
diagonal combs transition to finite volume, their growth accelerates to a
quadratic rate. Our methodology involves employing spectral geometric
principles tailored for metric graphs, complemented by deriving estimates for
the $k$-th eigenvalue on compact metric graphs.


  Lie contact structures generalize the classical Lie sphere geometry of
oriented hyperspheres in the standard sphere. They can be equivalently
described as parabolic geometries corresponding to the contact grading of
orthogonal real Lie algebra. It follows the underlying geometric structure can
be interpreted in several equivalent ways. In particular, we show this is given
by a split-quaternionic structure on the contact distribution, which is
compatible with the Levi bracket. In this vein, we study the geometry of
chains, a distinguished family of curves appearing in any parabolic contact
geometry. Also to the system of chains there is associated a canonical
parabolic geometry of specific type. Up to some exceptions in low dimensions,
it turns out this can be obtained by an extension of the parabolic geometry
associated to the Lie contact structure if and only if the latter is locally
flat. In that case we can show that chains are never geodesics of an affine
connection, hence, in particular, the path geometry of chains is always
non-trivial. Using appropriately this fact, we conclude that the path geometry
of chains allows to recover the Lie contact structure, hence, in particular,
transformations preserving chains must preserve the Lie contact structure.


  We show that positive measure domination implies uniform almost everywhere
domination and that this proof translates into a proof in the subsystem
WWKL$_0$ (but not in RCA$_0$) of the equivalence of various Lebesgue measure
regularity statements introduced by Dobrinen and Simpson. This work also allows
us to prove that low for weak $2$-randomness is the same as low for
Martin-L\"of randomness (a result independently obtained by Nies). Using the
same technique, we show that $\leq_{LR}$ implies $\leq_{LK}$, generalizing the
fact that low for Martin-L\"of randomness implies low for $K$.


  The creation of collective many-body quantum states from a two-dimensional
lattice gas of atoms is studied. Our approach relies on the van-der-Waals
interaction that is present between alkali metal atoms when laser excited to
high-lying Rydberg s-states. We focus on a regime in which the laser driving is
strong compared to the interaction between Rydberg atoms. Here energetically
low-lying many-particle states can be calculated approximately from a quadratic
Hamiltonian. The potential usefulness of these states as a resource for the
creation of deterministic single-photon sources is illustrated. The properties
of these photon states are determined from the interplay between the particular
geometry of the lattice and the interatomic spacing.


  Waveform sampling systems are used pervasively in the design of front end
electronics for radiation detection. The introduction of new feature extraction
algorithms (eg. neural networks) to waveform sampling has the great potential
to substantially improve the performance and enrich the capability. To analyze
the limits of such algorithms and thus illuminate the direction of resolution
optimization, in this paper we systematically simulate the detection procedure
of contemporary radiation detectors with an emphasis on pulse timing. Neural
networks and variants of constant fraction discrimination are studied in a wide
range of analog channel frequency and noise level. Furthermore, we propose an
estimation of multivariate Cram\'er Rao lower bound within the model using
intrinsic-extrinsic parametrization and prior information. Two case studies
(single photon detection and shashlik-type calorimeter) verify the reliability
of the proposed method and show it works as a useful guideline when assessing
the abilities of various feature extraction algorithms.


  Two entangled particles in threedimensional Hilbert space (per particle) are
considered in an EPR-type arrangement. On each side the Kochen-Specker
observables $\{J_1^2,J_2^2,J_3^2\}$ and $\{\bar J_1^2, \bar J_2^2,J_3^2\}$ with
$[J_1^2,\bar J_1^2]\neq 0$ are measured. The outcomes of measurements of
$J_3^2$ (via $J_1^2,J_2^2$) and $J_3^2$ (via $\bar J_1^2,\bar J_2^2$) are
compared. Although formally $J_3^2$ is associated with the same projection
operator, a strong form of quantum contextuality states that an outcome depends
on the complete disposition of the measurement apparatus, in particular whether
$J_1^2$ or $\bar J_1^2$ is measured alongside. It is argued that in this case
it is impossible to measure contextuality directly, a necessary condition being
a non-operational counterfactuality of the argument.


  We present the first numerical-relativity simulation of a compact-object
binary whose gravitational waveform is long enough to cover the entire
frequency band of advanced gravitational-wave detectors, such as LIGO, Virgo
and KAGRA, for mass ratio 7 and total mass as low as $45.5\,M_\odot$. We find
that effective-one-body models, either uncalibrated or calibrated against
substantially shorter numerical-relativity waveforms at smaller mass ratios,
reproduce our new waveform remarkably well, with a negligible loss in detection
rate due to modeling error. In contrast, post-Newtonian inspiral waveforms and
existing calibrated phenomenological inspiral-merger-ringdown waveforms display
greater disagreement with our new simulation. The disagreement varies
substantially depending on the specific post-Newtonian approximant used.


  Various applications of voice synthesis have been developed independently
despite the fact that they generate "voice" as output in common. In addition,
the majority of voice synthesis models currently rely on annotated audio data,
but it is crucial to scale them to self-supervised datasets in order to
effectively capture the wide range of acoustic variations present in human
voice, including speaker identity, emotion, and prosody. In this work, we
propose Make-A-Voice, a unified framework for synthesizing and manipulating
voice signals from discrete representations. Make-A-Voice leverages a
"coarse-to-fine" approach to model the human voice, which involves three
stages: 1) semantic stage: model high-level transformation between linguistic
content and self-supervised semantic tokens, 2) acoustic stage: introduce
varying control signals as acoustic conditions for semantic-to-acoustic
modeling, and 3) generation stage: synthesize high-fidelity waveforms from
acoustic tokens. Make-A-Voice offers notable benefits as a unified voice
synthesis framework: 1) Data scalability: the major backbone (i.e., acoustic
and generation stage) does not require any annotations, and thus the training
data could be scaled up. 2) Controllability and conditioning flexibility: we
investigate different conditioning mechanisms and effectively handle three
voice synthesis applications, including text-to-speech (TTS), voice conversion
(VC), and singing voice synthesis (SVS) by re-synthesizing the discrete voice
representations with prompt guidance. Experimental results demonstrate that
Make-A-Voice exhibits superior audio quality and style similarity compared with
competitive baseline models. Audio samples are available at
https://Make-A-Voice.github.io


  We analyze the difference between ex ante and ex post equilibria in classical
games played with the assistance of a nonlocal (quantum or no-signaling)
resource. In physics, the playing of these games is known as performing
bipartite Bell-type experiments. By analyzing the Clauser-Horn-Shimony-Holt
game, we find a constructive procedure to find two-person Bayesian games with a
nonlocal (i.e. no-signaling, and, in many cases, quantum) advantage. Most games
of this kind known from the literature can be constructed along this principle,
and share the property that their relevant ex ante equilibria are ex post
equilibria as well. We introduce a new type of game, based on the Bell-theorem
by V\'ertesi and Bene, which does not have the latter property: the ex ante and
ex post equilibria differ.


  A family of sets is said to be symmetric if its automorphism group is
transitive, and $3$-wise intersecting if any three sets in the family have
nonempty intersection. Frankl conjectured in 1981 that if $\mathcal{A}$ is a
symmetric $3$-wise intersecting family of subsets of $\{1,2,\dots,n\}$, then
$|\mathcal{A}| = o(2^n)$. Here, we give a short proof of Frankl's conjecture
using a 'sharp threshold' result of Friedgut and Kalai.


  Searches for gravitational waves (GWs) from binary black holes using
interferometric GW detectors require the construction of template banks for
performing matched filtering while analyzing the data. Placement of templates
over the parameter space of binaries, as well as coincidence tests of GW
triggers from multiple detectors make use of the definition of a metric over
the space of gravitational waveforms. Although recent searches have employed
waveform templates coherently describing the inspiral, merger and ringdown
(IMR) of the coalescence, the metric used in the template banks and coincidence
tests was derived from post-Newtonian inspiral waveforms. In this paper, we
compute the template-space metric of the IMR waveform family IMRPhenomB over
the parameter space of masses and the effective spin parameter. We also propose
a coordinate system, which is a modified version of post-Newtonian chirp time
coordinates, in which the metric is slowly varying over the parameter space.
The match function analytically computed using the metric has excellent
agreement with the "exact" match function computed numerically. We show that
the metric is able to provide a reasonable approximation to the match function
of other IMR waveform families, such that the effective-one-body model
calibrated to numerical relativity (EOBNRv2). The availability of this metric
can contribute to improving the sensitivity of searches for GWs from binary
black holes in the advanced detector era.


  Multilayered van der Waals structures often lack periodicity, which
difficults their modeling. Building on previous work for bilayers, we develop a
tight-binding based, momentum space formalism capable of describing
incommensurate multilayered van der Waals structures for arbitrary lattice
mismatch and/or misalignment between different layers. We demonstrate how the
developed formalism can be used to model angle-resolved photoemission
spectroscopy measurements, and scanning tunnelling spectroscopy which can probe
the local and total density of states. The general method is then applied to
incommensurate twisted trilayer graphene structures. It is found that the
coupling between the three layers can significantly affect the low energy
spectral properties, which cannot be simply attributed to the pairwise
hybridization between the layers.


  If the observed dark-energy density $\rho_\Lambda$ is interpreted as the net
contribution of the energy density of the vacuum, $\rho_\Lambda \equiv \rho_V
\sim M_V^4$, and the corresponding vacuum length scale $\lambda_V = M_V^{-1}$
as the cutoff scale controlling the low-energy, effective field-theory limit of
gravity, it follows that the conventional cosmological scenario based on the
effective gravitational equations may be valid only up to the Tev energy scale.
Such a possibility would be strongly disfavored by the existence of a relic
background of primordial gravitational radiation of intensity compatible with
present (or near future) experimental sensitivities.


  Advances in gene sequencing have enabled in silico analyses of microbial
genomes and have led to the revision of concepts of microbial taxonomy and
evolution. We explore deficiencies in existing multiple sequence global
alignment algorithms and introduce a new indexing scheme to partition the
dynamic programming algorithm hypercube scoring tensor over processors based on
the dependency between partitions to be scored in parallel. The performance of
algorithms is compared in the study of rpoB gene sequences of Mycoplasma
species.


  This paper analyzes a time-stepping discontinuous Galerkin method for
fractional diffusion-wave problems. This method uses piecewise constant
functions in the temporal discretization and continuous piecewise linear
functions in the spatial discretization. Nearly optimal convergence rate with
respect to the regularity of the solution is established when the source term
is nonsmooth, and nearly optimal convergence rate $ \ln(1/\tau)(\sqrt{\ln(1/h)}
h^2+\tau)$ is derived under appropriate regularity assumption on the source
term. Convergence is also established without smoothness assumption on the
initial value. Finally, numerical experiments are performed to verify the
theoretical results.


  Despite being one of the longest known classes of astrophysical transients,
novae continue to present modern surprises. The Fermi-LAT discovered that many
if not all novae are GeV gamma ray sources, even though theoretical models had
not even considered them as a possible source class. More recently, MAGIC and
H.E.S.S. detected TeV gamma rays from a nova. Moreover, there is strong
evidence that the gamma rays are produced hadronically, and that the
long-studied optical emission by novae is also shock-powered. If this is true,
novae should emit a neutrino signal correlated with their gamma-ray and optical
signals. We present the first search for neutrinos from novae. Because the
neutrino energy spectrum is expected to match the gamma-ray spectrum, we use an
IceCube DeepCore event selection focused on GeV-TeV neutrinos. We present
results from two searches, one for neutrinos correlated with gamma-ray emission
and one for neutrinos correlated with optical emission. The event selection
presented here is promising for additional astrophysical transients including
gamma-ray bursts and gravitational wave sources.


  The dynamic approximate membership problem asks to represent a set S of size
n, whose elements are provided in an on-line fashion, supporting membership
queries without false negatives and with a false positive rate at most epsilon.
That is, the membership algorithm must be correct on each x in S, and may err
with probability at most epsilon on each x not in S.
  We study a well-motivated, yet insufficiently explored, variant of this
problem where the size n of the set is not known in advance. Existing optimal
approximate membership data structures require that the size is known in
advance, but in many practical scenarios this is not a realistic assumption.
Moreover, even if the eventual size n of the set is known in advance, it is
desirable to have the smallest possible space usage also when the current
number of inserted elements is smaller than n. Our contribution consists of the
following results:
  - We show a super-linear gap between the space complexity when the size is
known in advance and the space complexity when the size is not known in
advance.
  - We show that our space lower bound is tight, and can even be matched by a
highly efficient data structure.


  We consider the stochastic dynamics of a system linearly coupled to a
hierarchical thermal bath with two well-separated inherent timescales: one
slow, and one fast. The slow part of the bath is modeled as a set of harmonic
oscillators and taken into account explicitly, while the effects of the fast
part of the bath are simulated by dissipative and stochastic Langevin forces,
uncorrelated in space and time, acting on oscillators of the slow part of the
bath. We demonstrate for this model the robust emergence of a fractional
Langevin equation with a power-law decaying memory kernel. The conditions of
such an emergence and the specific value of the fractional exponent depend only
on the asymptotic low-frequency spectral properties of the slow part of the
bath.


  In this paper, we focus on unsupervised domain adaptation for Machine Reading
Comprehension (MRC), where the source domain has a large amount of labeled
data, while only unlabeled passages are available in the target domain. To this
end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where
($i$) pseudo questions are first generated for unlabeled passages in the target
domain, and then ($ii$) a domain classifier is incorporated into an MRC model
to predict which domain a given passage-question pair comes from. The
classifier and the passage-question encoder are jointly trained using
adversarial learning to enforce domain-invariant representation learning.
Comprehensive evaluations demonstrate that our approach ($i$) is generalizable
to different MRC models and datasets, ($ii$) can be combined with pre-trained
large-scale language models (such as ELMo and BERT), and ($iii$) can be
extended to semi-supervised learning.


  We use Kashiwara-Nakashima's combinatorics of crystal graphs associated to
the roots sytems $B_{n}$ and $D_{n}$ to extend the results of
\QCITE{cite}{}{lec3} and \QCITE{cite}{}{Mor} by showing that Morris type
recurrence formulas also exist for the orthogonal root systems. We derive from
these formulas a statistic on Kashiwara-Nakashima's tableaux of types
$B_{n},C_{n}$ and $D_{n}$ generalizing Lascoux-Sch\UNICODE{0xfc}tzenberger's
charge and from which it is possible to compute the Kostka-Foulkes polynomials
$K_{\lambda ,\mu}(q)$ with restrictive conditions on $(\lambda ,\mu)$ . This
statistic is different from that obtained in \QCITE{cite}{}{lec3} from the
cyclage graph structure on tableaux of type $C_{n}$. We show that such a
structure also exists for the tableaux of types $B_{n}$ and $D_{n}$ but can not
be simply related to the Kostka-Foulkes polynomials. Finally we give explicit
formulas for $K_{\lambda ,\mu}(q)$ when $| \lambda | \leq 3,$ or $n=2$ and $\mu
=0$.


  Fluid flows play a central role in scientific and technological development,
and many of these flows are characterized by a dominant oscillation, such as
the vortex shedding in the wake of nearly all transportation vehicles. The
ability to control vortex shedding is critical to improve the aerodynamic
performance of these unsteady fluid flow systems. This goal requires precise
characterization of how perturbations affect the long-time phase of the
oscillatory flow, as well as the ability to control transient behaviors. In
this work, we develop an energy-efficient flow control strategy to rapidly
alter the oscillation phase of time-periodic fluid flows, leveraging theory
developed for periodic biological systems. First, we perform a
phase-sensitivity analysis to construct a reduced-order model for the response
of the flow oscillation to impulsive control inputs at various phases. Next, we
introduce two control strategies for real-time phase control based on the
phase-sensitivity function: 1) optimal phase control, obtained by solving the
Euler-Lagrange equations as a two-point boundary value problem, and 2)
model-predictive control (MPC). Our approach is demonstrated for two unsteady
flow systems, the incompressible laminar flow past a circular cylinder and the
flow past an airfoil. We show that effective phase control may be achieved with
several actuation strategies, including blowing and rotary control. Moreover,
our control approach uses realistic measurements of the lift force on the body,
rather than requiring high-dimensional measurements of the full flow field.


  The Schrodinger equation for a charged particle constrained to a curved
surface in the presence of a vector potential is derived using the method of
forms. In the limit that the particle is brought infinitesimally close to the
surface, a term arises that couples the component of the vector potential
normal to the surface to the mean curvature of the surface.


  Novel solutions of Minkowski QED2+1 and large $N_f$ QCD Schwinger-Dyson
equations will be presented and discussed. The resultant propagators of
confined degrees of freedom will be shown.


  We study thermoelectric properties of the system composed of a monoatomic
chain on a surface and additional electrode coupled to the chain, which can be
an STM tip. In particular, we are interested in thermopower, electric and
thermal conductance, Wiedemann-Franz relation and thermoelectric figure of
merit, which is a direct measure of the usefulness of the system for
applications. We discuss the modifications of the STM wire topography due to
temperature gradient between the electrodes. Finally, we also make connection
to STM experiment, in which the thermopower has been directly measured, showing
different structure, not visible in topography spectra.