title:
Measuring DNS over TCP in the Era of Increasing DNS Response Sizes: A
  View from the Edge

abstract:
  The Domain Name System (DNS) is one of the most crucial parts of the
Internet. Although the original standard defined the usage of DNS over UDP
(DoUDP) as well as DNS over TCP (DoTCP), UDP has become the predominant
protocol used in the DNS. With the introduction of new Resource Records (RRs),
the sizes of DNS responses have increased considerably. Since this can lead to
truncation or IP fragmentation, the fallback to DoTCP as required by the
standard ensures successful DNS responses by overcoming the size limitations of
DoUDP. However, the effects of the usage of DoTCP by stub resolvers are not
extensively studied to this date. We close this gap by presenting a view at
DoTCP from the Edge, issuing 12.1M DNS requests from 2,500 probes toward Public
as well as Probe DNS recursive resolvers. In our measurement study, we observe
that DoTCP is generally slower than DoUDP, where the relative increase in
Response Time is less than 37% for most resolvers. While optimizations to DoTCP
can be leveraged to further reduce the response times, we show that support on
Public resolvers is still missing, hence leaving room for optimizations in the
future. Moreover, we also find that Public resolvers generally have comparable
reliability for DoTCP and DoUDP. However, Probe resolvers show a significantly
different behavior: DoTCP queries targeting Probe resolvers fail in 3 out of 4
cases, and, therefore, do not comply with the standard. This problem will only
aggravate in the future: As DNS response sizes will continue to grow, the need
for DoTCP will solidify.


title:
Chalcogenide Perovskites: An Emerging Class of Semiconductors for
  Optoelectronics

abstract:
  Chalcogenide perovskites have received considerable interest in photovoltaic
research community owing to their stability (thermal and aqueous), non-toxicity
and lead free composition. However, to date a theoretical study mainly focusing
on the excitonic and polaronic properties are not explored rigorously, due to
its huge computational demand. Herein, we capture the excitonic and polaronic
effects in a series of chalcogenide perovskites ABS$_3$ where A=Ba, Ca, Sr, and
B=Hf, Sn by employing state-of-the art hybrid density functional theory and
many body perturbative approaches viz. GW and BSE. We find that these
perovskites possess a large exciton binding energy than 3D inorganic-organic
hybrid halide perovskites. We examine the interplay of electronic and ionic
contribution to the dielectric screening, and conclude that electronic
contribution is dominant over the ionic contribution. Further using Feynman
polaron model, polaron parameters are computed, and we observe that charge
separated polaronic states are less stable than bound excitons. Finally, the
theoretically calculated spectroscopic limited maximum efficiency (SLME)
suggests that among all chalcogenide perovskites, CaSnS$_3$ could serve as a
best choice for photovoltaic applications.


title: Dual networks based 3D Multi-Person Pose Estimation from Monocular Video

abstract:
  Monocular 3D human pose estimation has made progress in recent years. Most of
the methods focus on single persons, which estimate the poses in the
person-centric coordinates, i.e., the coordinates based on the center of the
target person. Hence, these methods are inapplicable for multi-person 3D pose
estimation, where the absolute coordinates (e.g., the camera coordinates) are
required. Moreover, multi-person pose estimation is more challenging than
single pose estimation, due to inter-person occlusion and close human
interactions. Existing top-down multi-person methods rely on human detection
(i.e., top-down approach), and thus suffer from the detection errors and cannot
produce reliable pose estimation in multi-person scenes. Meanwhile, existing
bottom-up methods that do not use human detection are not affected by detection
errors, but since they process all persons in a scene at once, they are prone
to errors, particularly for persons in small scales. To address all these
challenges, we propose the integration of top-down and bottom-up approaches to
exploit their strengths. Our top-down network estimates human joints from all
persons instead of one in an image patch, making it robust to possible
erroneous bounding boxes. Our bottom-up network incorporates human-detection
based normalized heatmaps, allowing the network to be more robust in handling
scale variations. Finally, the estimated 3D poses from the top-down and
bottom-up networks are fed into our integration network for final 3D poses. To
address the common gaps between training and testing data, we do optimization
during the test time, by refining the estimated 3D human poses using high-order
temporal constraint, re-projection loss, and bone length regularizations. Our
evaluations demonstrate the effectiveness of the proposed method. Code and
models are available: https://github.com/3dpose/3D-Multi-Person-Pose.


title: Topologizing interpretable groups in $p$-adically closed fields

abstract:
  We consider interpretable topological spaces and topological groups in a
$p$-adically closed field $K$. We identify a special class of "admissible
topologies" with topological tameness properties like generic continuity,
similar to the topology on definable subsets of $K^n$. We show every
interpretable set has at least one admissible topology, and every interpretable
group has a unique admissible group topology. We then consider definable
compactness (in the sense of Fornasiero) on interpretable groups. We show that
an interpretable group is definably compact if and only if it has finitely
satisfiable generics (fsg), generalizing an earlier result on definable groups.
As a consequence, we see that fsg is a definable property in definable families
of interpretable groups, and that any fsg interpretable group defined over
$\mathbb{Q}_p$ is definably isomorphic to a definable group.


title: The mighty force: statistical inference and high-dimensional statistics

abstract:
  This is a review to appear as a contribution to the edited volume "Spin Glass
Theory & Far Beyond - Replica Symmetry Breaking after 40 Years", World
Scientific. It showcases a selection of contributions from the spin glass
community at large to high-dimensional statistics, by focusing on three
important graph-based models and methodologies having deeply impacted the
field: inference of graphs (a.k.a. direct coupling analysis), inference from
graphs (the community detection problem), and the dynamic cavity method, which
in particular allows for inference from graphs encoding causal relations.

categories: cond-mat.dis-nn cond-mat.stat-mech cs.IT math.IT


title: Assessing Routing Algorithms for PCNs: Full Evaluation Results

abstract:
  Within this Technical Report, we present the full analysis of 61 routing
protocols for Wireless Sensor Networks (WSNs) for the purposes of routing in
Payment Channel Networks (PCNs). In addition, we present the full results of
the implementation of the three algorithms E-TORA, TERP, and M-DART.


title:
A basic macroeconomic agent-based model for analyzing monetary regime
  shifts

abstract:
  In macroeconomics, an emerging discussion of alternative monetary systems
addresses the dimensions of systemic risk in advanced financial systems.
Monetary regime changes with the aim of achieving a more sustainable financial
system have already been discussed in several European parliaments and were the
subject of a referendum in Switzerland. However, their effectiveness and
efficacy concerning macro-financial stability are not well-known. This paper
introduces a macroeconomic agent-based model (MABM) in a novel simulation
environment to simulate the current monetary system, which may serve as a basis
to implement and analyze monetary regime shifts. In this context, the monetary
system affects the lending potential of banks and might impact the dynamics of
financial crises. MABMs are predestined to replicate emergent financial crisis
dynamics, analyze institutional changes within a financial system, and thus
measure macro-financial stability. The used simulation environment makes the
model more accessible and facilitates exploring the impact of different
hypotheses and mechanisms in a less complex way. The model replicates a wide
range of stylized economic facts, including simplifying assumptions to reduce
model complexity.


title: Exposing Deepfake Face Forgeries with Guided Residuals

abstract:
  Residual-domain feature is very useful for Deepfake detection because it
suppresses irrelevant content features and preserves key manipulation traces.
However, inappropriate residual prediction will bring side effects on detection
accuracy. In addition, residual-domain features are easily affected by image
operations such as compression. Most existing works exploit either
spatial-domain features or residual-domain features, while neglecting that two
types of features are mutually correlated. In this paper, we propose a guided
residuals network, namely GRnet, which fuses spatial-domain and residual-domain
features in a mutually reinforcing way, to expose face images generated by
Deepfake. Different from existing prediction based residual extraction methods,
we propose a manipulation trace extractor (MTE) to directly remove the content
features and preserve manipulation traces. MTE is a fine-grained method that
can avoid the potential bias caused by inappropriate prediction. Moreover, an
attention fusion mechanism (AFM) is designed to selectively emphasize feature
channel maps and adaptively allocate the weights for two streams. The
experimental results show that the proposed GRnet achieves better performances
than the state-of-the-art works on four public fake face datasets including
HFF, FaceForensics++, DFDC and Celeb-DF. Especially, GRnet achieves an average
accuracy of 97.72% on the HFF dataset, which is at least 5.25% higher than the
existing works.


title:
A Feasible Sequential Linear Programming Algorithm with Application to
  Time-Optimal Path Planning Problems

abstract:
  In this paper, we propose a Feasible Sequential Linear Programming (FSLP)
algorithm applied to time-optimal control problems (TOCP) obtained through
direct multiple shooting discretization. This method is motivated by TOCP with
nonlinear constraints which arise in motion planning of mechatronic systems.
The algorithm applies a trust-region globalization strategy ensuring global
convergence. For fully determined problems our algorithm provides locally
quadratic convergence. Moreover, the algorithm keeps all iterates feasible
enabling early termination at suboptimal, feasible solutions. This additional
feasibility is achieved by an efficient iterative strategy using evaluations of
constraints, i.e., zero-order information. Convergence of the feasibility
iterations can be enforced by reduction of the trust-region radius. These
feasibility iterations maintain feasibility for general Nonlinear Programs
(NLP). Therefore, the algorithm is applicable to general NLP. We demonstrate
our algorithm's efficiency and the feasibility updates for a TOCP of an
overhead crane motion planning simulation case.


title:
How does a spontaneously speaking conversational agent affect user
  behavior?

abstract:
  This study investigated the effect of synthetic voice of conversational agent
trained with spontaneous speech on human interactants. Specifically, we
hypothesized that humans will exhibit more social responses when interacting
with conversational agent that has a synthetic voice built on spontaneous
speech. Typically, speech synthesizers are built on a speech corpus where voice
professionals read a set of written sentences. The synthesized speech is clear
as if a newscaster were reading a news or a voice actor were playing an anime
character. However, this is quite different from spontaneous speech we speak in
everyday conversation. Recent advances in speech synthesis enabled us to build
a speech synthesizer on a spontaneous speech corpus, and to obtain a near
conversational synthesized speech with reasonable quality. By making use of
these technology, we examined whether humans produce more social responses to a
spontaneously speaking conversational agent. We conducted a large-scale
conversation experiment with a conversational agent whose utterances were
synthesized with the model trained either with spontaneous speech or read
speech. The result showed that the subjects who interacted with the agent whose
utterances were synthesized from spontaneous speech tended to show shorter
response time and a larger number of backchannels. The result of a
questionnaire showed that subjects who interacted with the agent whose
utterances were synthesized from spontaneous speech tended to rate their
conversation with the agent as closer to a human conversation. These results
suggest that speech synthesis built on spontaneous speech is essential to
realize a conversational agent as a social actor.


title: VICE: Variational Inference for Concept Embeddings

abstract:
  In this paper, we introduce Variational Inference for Concept Embeddings
(VICE), an approximate Bayesian method for learning object concept embeddings
from human behavior in an odd-one-out triplet task. We use variational
inference to obtain a sparse, non-negative solution with uncertainty estimates
about each embedding value. We exploit these estimates to automatically select
the dimensions that explain the data while yielding reproducible embeddings. We
introduce a PAC learning bound for VICE that can be used to estimate
generalization performance or determine a sufficient sample size for different
experimental designs. VICE rivals or outperforms its predecessor, SPoSE, at
predicting human behavior in a triplet task. VICE object representations are
substantially more reproducible and consistent across different random
initializations.


title: Dashboard Design Patterns

abstract:
  This paper introduces design patterns for dashboards to inform their design
processes. Despite a growing number of public examples, case studies, and
general guidelines there is surprisingly little design guidance for dashboards.
Such guidance is necessary to inspire designs and discuss tradeoffs in
screenspace, interaction, and information shown. Based on a systematic review
of 144 dashboards, we report on eight groups of design patterns that provide
common solutions in dashboard design. We discuss combinations of these patterns
in dashboard genres such as narrative, analytical, or embedded dashboard. We
ran a 2 week dashboard design workshop with 23 participants of varying
expertise working on their own data and dashboards. We discuss the application
of patterns for the dashboard design processes, as well as general design
tradeoffs and common challenges. Our work complements previous surveys and aims
to support dashboard designers and researchers in co-creation, structured
design decisions, as well as future user evaluations about dashboard design
guidelines. Detailed pattern descriptions and workshop material can be found
online: https://dashboarddesignpatterns.github.io


title:
Sterile neutrinos, $0\nu\beta\beta$ decay and the W-boson mass anomaly
  in a Flipped $SU(5)$ from F-theory

abstract:
  We investigate the low energy properties of an effective local model with
flipped $SU(5)\times U(1)_{\chi}$ gauge group, constructed within the framework
of F-theory. Its origin is traced back to the $SO(10)$ symmetry -- associated
with a geometric singularity of the compactification manifold -- broken by an
internal flux which is turned on along the seven-brane in the $U(1)_{\chi}$
direction. Topological properties and the choice of flux parameters determine
the massless spectrum of the model to be that of the minimal flipped $SU(5)$
supplemented with an extra right-handed electron-type state and its complex
conjugate, $E^c+\bar E^c$, as well as neutral singlet fields. The subsequent
symmetry breaking to the $SU(3)\times SU(2)\times U(1)_Y$ gauge group occurs
with a Higgs pair in $10+\overline{10}$ representations of $SU(5)$. Next we
proceed to the phenomenological analysis of the resulting effective model and
the salient outcomes are: The $E^c+\bar E^c$ pair acquires a mass of few TeV
and as such could solve the $g_{\mu}-2$ discrepancy. Neutrino couplings to
extra neutral singlets lead to an inverse seesaw mechanism where an extra light
state could be a suitable dark matter candidate. The predictions of the model
for the ${0\nu}\beta\beta$ decay rate could be tested in near future
experiments. There are non-unitarity deviations from lepton mixing matrix
($U_{PMNS}$), which could in principle explain the new precision measurement of
the W-boson mass recently reported by CDF II collaboration.


title:
Neutral Utterances are Also Causes: Enhancing Conversational Causal
  Emotion Entailment with Social Commonsense Knowledge

abstract:
  Conversational Causal Emotion Entailment aims to detect causal utterances for
a non-neutral targeted utterance from a conversation. In this work, we build
conversations as graphs to overcome implicit contextual modelling of the
original entailment style. Following the previous work, we further introduce
the emotion information into graphs. Emotion information can markedly promote
the detection of causal utterances whose emotion is the same as the targeted
utterance. However, it is still hard to detect causal utterances with different
emotions, especially neutral ones. The reason is that models are limited in
reasoning causal clues and passing them between utterances. To alleviate this
problem, we introduce social commonsense knowledge (CSK) and propose a
Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two
utterances. As not all CSK is emotionally suitable for utterances, we therefore
propose a sentiment-realized knowledge selecting strategy to filter CSK. To
process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph
networks. Experimental results show that our method outperforms baselines and
infers more causes with different emotions from the targeted utterance.


title: Point Cloud Compression with Sibling Context and Surface Priors

abstract:
  We present a novel octree-based multi-level framework for large-scale point
cloud compression, which can organize sparse and unstructured point clouds in a
memory-efficient way. In this framework, we propose a new entropy model that
explores the hierarchical dependency in an octree using the context of
siblings' children, ancestors, and neighbors to encode the occupancy
information of each non-leaf octree node into a bitstream. Moreover, we locally
fit quadratic surfaces with a voxel-based geometry-aware module to provide
geometric priors in entropy encoding. These strong priors empower our entropy
framework to encode the octree into a more compact bitstream. In the decoding
stage, we apply a two-step heuristic strategy to restore point clouds with
better reconstruction quality. The quantitative evaluation shows that our
method outperforms state-of-the-art baselines with a bitrate improvement of
11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.


title: Arakelov inequalities in higher dimensions

abstract:
  We develop a Hodge theoretic invariant for families of projective manifolds
that measures the potential failure of an Arakelov-type inequality in higher
dimensions, one that naturally generalizes the classical Arakelov inequality
over regular quasi-projective curves. We show that for families of manifolds
with ample canonical bundle this invariant is uniformly bounded. As a
consequence we establish that such families over a base of arbitrary dimension
verify the aforementioned Arakelov inequality, answering a question of Viehweg.


title:
Superredundancy: A tool for Boolean formula minimization complexity
  analysis

abstract:
  A superredundant clause is a clause that is redundant in the resolution
closure of a formula. The converse concept of superirredundancy ensures
membership of the clause in all minimal CNF formulae that are equivalent to the
given one. This allows for building formulae where some clauses are fixed when
minimizing size. An example are proofs of complexity hardness of the problems
of minimal formula size. Others are proofs of size when forgetting variables or
revising a formula. Most clauses can be made superirredundant by splitting them
over a new variable.


title: Data-driven emotional body language generation for social robotics

abstract:
  In social robotics, endowing humanoid robots with the ability to generate
bodily expressions of affect can improve human-robot interaction and
collaboration, since humans attribute, and perhaps subconsciously anticipate,
such traces to perceive an agent as engaging, trustworthy, and socially
present. Robotic emotional body language needs to be believable, nuanced and
relevant to the context. We implemented a deep learning data-driven framework
that learns from a few hand-designed robotic bodily expressions and can
generate numerous new ones of similar believability and lifelikeness. The
framework uses the Conditional Variational Autoencoder model and a sampling
approach based on the geometric properties of the model's latent space to
condition the generative process on targeted levels of valence and arousal. The
evaluation study found that the anthropomorphism and animacy of the generated
expressions are not perceived differently from the hand-designed ones, and the
emotional conditioning was adequately differentiable between most levels except
the pairs of neutral-positive valence and low-medium arousal. Furthermore, an
exploratory analysis of the results reveals a possible impact of the
conditioning on the perceived dominance of the robot, as well as on the
participants' attention.


title: An Electrically Conductive Oleogel Paste for Edible Electronics

abstract:
  Edible electronics will facilitate point-of-care testing through safe and
cheap devices that are digested or degraded in the body or environment after
performing a specific function. Its thrive depends on creating a library of
materials that are the basic building blocks for eatable technologies. Edible
electrical conductors fabricated with green methods that allow production at a
large scale and composed of food derivatives, ingestible in large amounts
without risk for human health are needed. Here, conductive pastes made with
materials with a high tolerable upper intake limit (major of mg/kg body weight
/day) are proposed. Conductive oleogel paste composites, made with
biodegradable and food-grade materials like natural waxes, oils, and activated
carbon conductive fillers, are presented. The proposed pastes are compatible
with manufacturing processes such as direct ink writing and thus are suitable
for an industrial scale-up. These conductors are built without the use of
solvents, and with tunable electromechanical features and adhesion depending on
the composition. They have antibacterial and hydrophobic properties, so that
they can be used in contact with food preventing contamination and preserving
its organoleptic properties. As a proof-of-principle application, the edible
conductive pastes are demonstrated to be effective edible contacts for food
impedance analysis, to be integrated for example in smart fruit labels for
ripening monitoring.


title:
Investigating Two-zero Textures of Inverse Neutrino Mass Matrix under
  the Lamp Post of LMA and DLMA Solutions and Symmetry Realization

abstract:
  We study the phenomenological consequences of two-zero textures of
\textit{inverse} neutrino mass matrix($M_{\nu}^{-1}$). There are fifteen
possibilities to have two-zero textures of $M_{\nu}^{-1}$. In order to study
complete phenomenology of the model, we confront these textures with the latest
neutrino data including large mixing angle(LMA) and \textit{dark}-large mixing
angle(DLMA) degenerate solutions, later of which originates if neutrinos
exhibit non-standard interactions with matter. Out of fifteen possibilities
only seven($B_2,B_4,C_1,C_3,D_1,D_2,E_1$) are found to be in consonance with
LMA and/or DLMA solutions. In general, we find that the textures for which
$(M_{\nu}^{-1})_{11}=0$ are disallowed. The textures $B_2$, $B_4$, $C_1$, $C_3$
and $D_2$ textures are found to be allowed under both LMA and DLMA
descriptions. Also, these textures are necessarily $CP$ violating. However, the
textures which are not consistent with DLMA paradigm, for example $D_1$ and
$E_1$, allow both $CP$ conserving and violating solutions. The generic feature
of the class of model discussed in the present work is the existence of
neutrino mass hierarchy degeneracy in a particular texture. Interestingly,
amongst all the seven texture, textures with $(M_{\nu}^{-1})_{23}=0$ are found
to be either disallowed or are consistent with LMA($D_1$ and $E_1$) description
only. Except for textures $D_1$ and $E_1$, the predicted $3\sigma$ lower bound
on $|M_{ee}|$ is $\mathcal{O}(10^{-2})$ which is within the sensitivity reach
of $0\nu\beta\beta$ decay experiments. For example, the non-observation of
$0\nu\beta\beta$ decay down to these high sensitivities will refute all the
textures except $D_1$ and $E_1$. Furthermore, we have proposed a flavor model,
based on discrete non-Abelian flavor group $A_4$, wherein such textures of
$M_{\nu}^{-1}$ can be realized within Type-I seesaw setting.


title:
A novel approach for deriving the stable boundary layer height and eddy
  viscosity profiles from the Ekman equations

abstract:
  In this study, we utilize a novel approach to solve the Ekman equations for
eddy viscosity profiles in the stable boundary layer. By doing so, a well-known
expression for the stable boundary layer height by Zilitinkevich
(Boundary-Layer Meteorology, 1972, Vol. 3, 141--145) is rediscovered.


title: Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries

abstract:
  For image forensics, convolutional neural networks (CNNs) tend to learn
content features rather than subtle manipulation traces, which limits forensic
performance. Existing methods predominantly solve the above challenges by
following a general pipeline, that is, subtracting the original pixel value
from the predicted pixel value to make CNNs pay attention to the manipulation
traces. However, due to the complicated learning mechanism, these methods may
bring some unnecessary performance losses. In this work, we rethink the
advantages of gradient operator in exposing face forgery, and design two
plug-and-play modules by combining gradient operator with CNNs, namely tensor
pre-processing (TP) and manipulation trace attention (MTA) module.
Specifically, TP module refines the feature tensor of each channel in the
network by gradient operator to highlight the manipulation traces and improve
the feature representation. Moreover, MTA module considers two dimensions,
namely channel and manipulation traces, to force the network to learn the
distribution of manipulation traces. These two modules can be seamlessly
integrated into CNNs for end-to-end training. Experiments show that the
proposed network achieves better results than prior works on five public
datasets. Especially, TP module greatly improves the accuracy by at least 4.60%
compared with the existing pre-processing module only via simple tensor
refinement. The code is available at:
https://github.com/EricGzq/GocNet-pytorch.


title: Symmetry restoration, Tunnelling and the Null Energy Condition

abstract:
  A finite volume allows tunnelling between degenerate vacuua in Quantum Field
Theory, and leads to remarkable energetic features, arising from the
competition of different saddle points in the partition function. We describe
this competition for finite temperature at equilibrium, taking into account
both static and (Euclidean) time-dependent saddle points. The effective theory
for the homogeneous order parameter yields a non-extensive vacuum energy in
different temperature regimes, implying a dynamical violation of the Null
Energy Condition.


title: A CAD Framework for Simulation of Network Level Attack on Platoons

abstract:
  Recent developments in the smart mobility domain have transformed automobiles
into networked transportation agents helping realize new age, large-scale
intelligent transportation systems (ITS). The motivation behind such networked
transportation is to improve road safety as well as traffic efficiency. In this
setup, vehicles can share information about their speed and/or acceleration
values among themselves and infrastructures can share traffic signal data with
them. This enables the connected vehicles (CVs) to stay informed about their
surroundings while moving. However, the inter-vehicle communication channels
significantly broaden the attack surface. The inter-vehicle network enables an
attacker to remotely launch attacks. An attacker can create collision as well
as hamper performance by reducing the traffic efficiency. Thus, security
vulnerabilities must be taken into consideration in the early phase of the
development cycle of CVs. To the best of our knowledge, there exists no such
automated simulation tool using which engineers can verify the performance of
CV prototypes in the presence of an attacker. In this work, we present an
automated tool flow that facilitates false data injection attack synthesis and
simulation on customizable platoon structure and vehicle dynamics. This tool
can be used to simulate as well as design and verify control-theoretic
light-weight attack detection and mitigation algorithms for CVs.


title:
Filling the gap between individual-based evolutionary models and
  Hamilton-Jacobi equations

abstract:
  We consider a stochastic model for the evolution of a discrete population
structured by a trait with values on a finite grid of the torus, and with
mutation and selection. Traits are vertically inherited unless a mutation
occurs, and influence the birth and death rates. We focus on a parameter
scaling where population is large, individual mutations are small but not rare,
and the grid mesh for the trait values is much smaller than the size of
mutation steps. When considering the evolution of the population in a long time
scale, the contribution of small sub-populations may strongly influence the
dynamics. Our main result quantifies the asymptotic dynamics of sub-population
sizes on a logarithmic scale. We establish that under the parameter scaling the
logarithm of the stochastic population size process, conveniently normalized,
converges to the unique viscosity solution of a Hamilton-Jacobi equation. The
proof makes use of almost sure maximum principles and careful controls of the
martingale parts.


title:
Local Differential Privacy Meets Computational Social Choice --
  Resilience under Voter Deletion

abstract:
  The resilience of a voting system has been a central topic in computational
social choice. Many voting rules, like plurality, are shown to be vulnerable as
the attacker can target specific voters to manipulate the result. What if a
local differential privacy (LDP) mechanism is adopted such that the true
preference of a voter is never revealed in pre-election polls? In this case,
the attacker can only infer stochastic information about a voter's true
preference, and this may cause the manipulation of the electoral result
significantly harder. The goal of this paper is to provide a quantitative study
on the effect of adopting LDP mechanisms on a voting system. We introduce the
metric PoLDP (power of LDP) that quantitatively measures the difference between
the attacker's manipulation cost under LDP mechanisms and that without LDP
mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to
a voting system. We give a full characterization of PoLDP for the voting system
with plurality rule and provide general guidance towards the application of LDP
mechanisms.


title: Large Neighborhood Search based on Neural Construction Heuristics

abstract:
  We propose a Large Neighborhood Search (LNS) approach utilizing a learned
construction heuristic based on neural networks as repair operator to solve the
vehicle routing problem with time windows (VRPTW). Our method uses graph neural
networks to encode the problem and auto-regressively decodes a solution and is
trained with reinforcement learning on the construction task without requiring
any labels for supervision. The neural repair operator is combined with a local
search routine, heuristic destruction operators and a selection procedure
applied to a small population to arrive at a sophisticated solution approach.
The key idea is to use the learned model to re-construct the partially
destructed solution and to introduce randomness via the destruction heuristics
(or the stochastic policy itself) to effectively explore a large neighborhood.


title: Qubit from the classical collision entropy

abstract:
  We show how you can derive a qubit and its dynamics from a discrete
deterministic classical dynamics, conservation of the classical collision
entropy and continuity of dynamic evolution. This approach does not require
Hilbert space formalism, although you can recover it.


title: Imagining, Studying and Realising A Less Harmful App Ecosystem

abstract:
  Desktop browser extensions have long allowed users to improve their
experience online and tackle widespread harms on websites. So far, no
equivalent solution exists for mobile apps, despite the fact that individuals
now spend significantly more time on mobile than on desktop, and arguably face
similarly widespread harms.
  In this work, we investigate mobile app extensions, a previously
underexplored concept to study and address digital harms within mobile apps in
a decentralised, community-driven way. We analyse challenges to adoption of
this approach so far, and present a ready-to-use implementation for Android as
a result of significant and careful system development. Through a range of case
studies, we demonstrate that our implementation can already reduce (though not
completely eliminate) a wide range of harms - similarly as browser extensions
do on desktops.
  Our method provides a versatile foundation for a range of follow-up research
into digital harms in mobile apps that has not previously been possible, given
that browser extensions have long been a fruitful foundation for research
studies on desktops. In other words, our system tries to address the gap of a
focus on desktop interventions in previous research.


title:
On the directional asymptotic approach in optimization theory Part B:
  constraint qualifications

abstract:
  During the last years, asymptotic (or sequential) constraint qualifications,
which postulate upper semicontinuity of certain set-valued mappings and provide
a natural companion of asymptotic stationarity conditions, have been shown to
be comparatively mild, on the one hand, while possessing inherent practical
relevance from the viewpoint of numerical solution methods, on the other one.
Based on recent developments, the theory in this paper enriches asymptotic
constraint qualifications for very general nonsmooth optimization problems over
inverse images of set-valued mappings by incorporating directional data. We
compare these new directional asymptotic regularity conditions with standard
constraint qualifications from nonsmooth optimization. Further, we introduce
directional concepts of pseudo- and quasi-normality which apply to set-valued
mappings. It is shown that these properties provide sufficient conditions for
the validity of directional asymptotic regularity. Finally, a novel
coderivative-like variational tool is introduced which allows to study the
presence of directional asymptotic regularity. For geometric constraints, it is
illustrated that all appearing objects can be calculated in terms of initial
problem data.


title: On the diversity and similarity of mathematical models in science

abstract:
  In this article, the notion of a mathematical model in science is attempted
to be enlightened from several points of view. In particular, it is shown that
mathematical models are introduced differently and used differently in
different areas of science. In the present article the use of models in
statistics is taken as a basis, but links are given to several other areas. Two
particular such links are described in some detail here: First the link
connected to the chemometrical Partial Least Squares algorithm, a link that now
has been generalized to the more sophisticated envelope model. Secondly,
statistics, as is well known, relies heavily on making decisions, and it may
also in certain cases be connected to a process of taking measurements. A
mathematical model for these two activities, connected to the mind of an
observer, is introduced. This model is taken further, and shown to be important
in a new proposal for a foundation of quantum theory. Quantum mechanics, as
seen from this point of view, is described in some detail. The discussion here
is close to the discussion in a recent book and in recently published articles
by the author.


title:
BSRA: Block-based Super Resolution Accelerator with Hardware Efficient
  Pixel Attention

abstract:
  Increasingly, convolution neural network (CNN) based super resolution models
have been proposed for better reconstruction results, but their large model
size and complicated structure inhibit their real-time hardware implementation.
Current hardware designs are limited to a plain network and suffer from lower
quality and high memory bandwidth requirements. This paper proposes a super
resolution hardware accelerator with hardware efficient pixel attention that
just needs 25.9K parameters and simple structure but achieves 0.38dB better
reconstruction images than the widely used FSRCNN. The accelerator adopts full
model block wise convolution for full model layer fusion to reduce external
memory access to model input and output only. In addition, CNN and pixel
attention are well supported by PE arrays with distributed weights. The final
implementation can support full HD image reconstruction at 30 frames per second
with TSMC 40nm CMOS process.


title:
Sparse Compressed Spiking Neural Network Accelerator for Object
  Detection

abstract:
  Spiking neural networks (SNNs), which are inspired by the human brain, have
recently gained popularity due to their relatively simple and low-power
hardware for transmitting binary spikes and highly sparse activation maps.
However, because SNNs contain extra time dimension information, the SNN
accelerator will require more buffers and take longer to infer, especially for
the more difficult high-resolution object detection task. As a result, this
paper proposes a sparse compressed spiking neural network accelerator that
takes advantage of the high sparsity of activation maps and weights by
utilizing the proposed gated one-to-all product for low power and highly
parallel model execution. The experimental result of the neural network shows
71.5$\%$ mAP with mixed (1,3) time steps on the IVS 3cls dataset. The
accelerator with the TSMC 28nm CMOS process can achieve 1024$\times$576@29
frames per second processing when running at 500MHz with 35.88TOPS/W energy
efficiency and 1.05mJ energy consumption per frame.


title:
Zebra: Memory Bandwidth Reduction for CNN Accelerators With Zero Block
  Regularization of Activation Maps

abstract:
  The large amount of memory bandwidth between local buffer and external DRAM
has become the speedup bottleneck of CNN hardware accelerators, especially for
activation maps. To reduce memory bandwidth, we propose to learn pruning
unimportant blocks dynamically with zero block regularization of activation
maps (Zebra). This strategy has low computational overhead and could easily
integrate with other pruning methods for better performance. The experimental
results show that the proposed method can reduce 70\% of memory bandwidth for
Resnet-18 on Tiny-Imagenet within 1\% accuracy drops and 2\% accuracy gain with
the combination of Network Slimming.


title: VSA: Reconfigurable Vectorwise Spiking Neural Network Accelerator

abstract:
  Spiking neural networks (SNNs) that enable low-power design on edge devices
have recently attracted significant research. However, the temporal
characteristic of SNNs causes high latency, high bandwidth and high energy
consumption for the hardware. In this work, we propose a binary weight spiking
model with IF-based Batch Normalization for small time steps and low hardware
cost when direct training with input encoding layer and spatio-temporal back
propagation (STBP). In addition, we propose a vectorwise hardware accelerator
that is reconfigurable for different models, inference time steps and even
supports the encoding layer to receive multi-bit input. The required memory
bandwidth is further reduced by two-layer fusion mechanism. The implementation
result shows competitive accuracy on the MNIST and CIFAR-10 datasets with only
8 time steps, and achieves power efficiency of 25.9 TOPS/W.


title:
S0-No-More: A Z-Wave NonceGet Denial of Service Attack utilizing
  included but offline NodeIDs

abstract:
  In this paper a vulnerability in the Z-Wave protocol specification,
especially in the S0 Z-Wave protocol is presented. Devices supporting this
standard can be blocked (denial of service) through continuous S0 NonceGet
requests. This way a whole network can be blocked if the attacked devices are
Z-Wave network controller. This also effects S2 network controller as long as
they support S0 NonceGet requests. As only a minimal amount of nonce requests
(1 per ~2 seconds) is required to conduct the attack it cannot be prevented by
standard countermeasures against jamming.


title: Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs

abstract:
  Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly
challenging problem as traditional subgraph matching methods are not capable to
deal with noise and missing information. To address this problem, it has been
recently introduced a promising approach based on jointly embedding logical
queries and KGs into a low-dimensional space to identify answer entities.
However, existing proposals ignore critical semantic knowledge inherently
available in KGs, such as type information. To leverage type information, we
propose a novel TypE-aware Message Passing (TEMP) model, which enhances the
entity and relation representations in queries, and simultaneously improves
generalization, deductive and inductive reasoning. Remarkably, TEMP is a
plug-and-play model that can be easily incorporated into existing
embedding-based models to improve their performance. Extensive experiments on
three real-world datasets demonstrate TEMP's effectiveness.


title: Electroweak dark matter model accounting for the CDF $W$-mass anomaly

abstract:
  Recently, the CDF collaboration reported a new measurement of the $W$ boson
mass $M_W = 80.4335 \pm 0.0094$ GeV, which shows a $\sim 7\sigma$ deviation
from the standard model prediction $80.3545 \pm 0.0057$ GeV obtained by the
electroweak (EW) global fit. This deviation can be explained by new physics
generating moderate EW oblique parameters $S$, $T$, and $U$. In this work, we
use the loop corrections induced by some extra EW multiplets to explain the CDF
$M_W$ anomaly. The lightest neutral particle in the multiplets can also serve
as a candidate of cold dark matter (DM). We consider two such models, namely
singlet-triplet scalar DM and singlet-doublet fermionic DM models, and perform
numerical scans to find the parameter points accounting for the $M_W$ anomaly.
The constraints from the correct DM thermal relic density and direct detection
are also taken into account. We find the parameter points simultaneously
interpreting the $M_W$ anomaly and satisfying the DM requirements in the former
model, but do not find such parameter points in the latter model.


title: On verifying expectations and observations of intelligent agents

abstract:
  Public observation logic (POL) is a variant of dynamic epistemic logic to
reason about agent expectations and agent observations. Agents have certain
expectations, regarding the situation at hand, that are actuated by the
relevant protocols, and they eliminate possible worlds in which their
expectations do not match with their observations. In this work, we investigate
the computational complexity of the model checking problem for POL and prove
its PSPACE-completeness. We also study various syntactic fragments of POL. We
exemplify the applicability of POL model checking in verifying different
characteristics and features of an interactive system with respect to the
distinct expectations and (matching) observations of the system. Finally, we
provide a discussion on the implementation of the model checking algorithms.


title:
Lieb lattices formed by real atoms on Ag(111) and their lattice constant
  dependent electronic properties

abstract:
  Scanning tunneling microscopy is a powerful tool to build artificial atomic
structures even not exist in nature but possess exotic properties. We here
constructed Lieb lattices with different lattice constants by real atoms, i.e.,
Fe atoms on Ag(111) and probed their electronic properties. We find a
surprising long-range effective electron wavefunction overlap between Fe
adatoms as it exhibits a 1/r2-dependence with the interatomic distance r
instead of the theoretically predicted exponential one. Combining control
experiments, tight-binding and Green's function calculations, we attribute the
observed long-range overlap to be enabled by the surface state. Our findings
not only enrich the understanding of the electron wavefunction overlap, but
also provide a convenient platform to design and explore the artificial
structures and future devices with real atoms.


title:
Solving PDEs by Variational Physics-Informed Neural Networks: an a
  posteriori error analysis

abstract:
  We consider the discretization of elliptic boundary-value problems by
variational physics-informed neural networks (VPINNs), in which test functions
are continuous, piecewise linear functions on a triangulation of the domain. We
define an a posteriori error estimator, made of a residual-type term, a
loss-function term, and data oscillation terms. We prove that the estimator is
both reliable and efficient in controlling the energy norm of the error between
the exact and VPINN solutions. Numerical results are in excellent agreement
with the theoretical predictions.


title:
More Programming Than Programming: Teaching Formal Methods in a Software
  Engineering Programme

abstract:
  Formal methods for software correctness are critical to the future of
software engineering - and so must be an essential part of software engineering
education. Unfortunately, formal methods are often resisted by students due to
perceived difficulty, mathematicity, and practical irrelevance. We redeveloped
our software correctness course by taking a programming intensive approach,
using the solver-aided language Dafny to provide instant formative feedback via
automated assessment. Our redeveloped course increased student retention and
resulted in the best evaluation for the course for at least ten years.


title: Controlling Floquet states on ultrashort time scales

abstract:
  The advent of ultrafast laser science offers the unique opportunity to
combine Floquet engineering with extreme time resolution, further pushing the
optical control of matter into the petahertz domain. However, what is the
shortest driving pulse for which Floquet states can be realised remains an
unsolved matter, thus limiting the application of Floquet theory to pulses
composed by many optical cycles. Here we ionized Ne atoms with few-femtosecond
pulses of selected time duration and show that a Floquet state can be
established already within 10 cycles of the driving field. For shorter pulses,
down to 2 cycles, the finite lifetime of the driven state can still be
explained using an analytical model based on Floquet theory. By demonstrating
that the population of the Floquet sidebands can be controlled not only with
the driving laser pulse intensity and frequency, but also by its duration, our
results add a new lever to the toolbox of Floquet engineering.


title:
Unitary and non-unitary operations on the Poincar\'e sphere and
  Pancharatnam-Berry phase with $\mathbf{Z}$ matrices

abstract:
  In polarization optics unitary and non-unitary operations can be carried out
by the Jones matrix. $\mathbf{Z}$ matrix is the $4\times 4$ analogue of the
Jones matrix and the Mueller matrix of a nondepolarizing optical medium can be
written as $\mathbf{M}=\mathbf{Z}\mathbf{Z}^*$. Jones matrix acts on the two
component complex Jones vector, while the $\mathbf{Z}$ matrix acts on the four
component real Stokes vector. Polarizer and retarder $\mathbf{Z}$ matrices can
be written in compact forms in terms of the components of the position vector
on the Poincar\'e sphere. In this note it is shown that the Pancharatnam-Berry
geometric phase can be demonstrated by unitary and non-unitary $\mathbf{Z}$
matrix operations.


title: Frustration shapes multi-channel Kondo physics: a star graph perspective

abstract:
  We study the overscreened multi-channel Kondo (MCK) model using the recently
developed unitary renormalization group (URG) technique. Our results display
the importance of ground state degeneracy in explaining various important
properties like the breakdown of screening and the presence of local non-Fermi
liquids. The impurity susceptibility of the intermediate coupling fixed point
Hamiltonian in the zero-bandwidth (or star graph) limit shows a power-law
divergence at low temperature, signalling its critical nature. Despite the
absence of inter-channel coupling in the MCK fixed point Hamiltonian, the study
of mutual information between any two channels shows non-zero correlation
between them. A spectral flow analysis of the star graph reveals that the
degenerate ground state manifold possesses topological quantum numbers. The low
energy effective Hamiltonian obtained upon adding a finite non-zero conduction
bath dispersion to the star graph Hamiltonian for both the two and
three-channel cases displays the presence of local non-Fermi liquids arising
from inter-channel quantum fluctuations. Discontinuous behaviour is observed in
several measures of ground state entanglement, signalling the underlying
orthogonality catastrophe associated with the degenerate ground state manifold.
We extend our results to underscreened and perfectly screened MCK models
through duality arguments. A study of channel anisotropy under renormalisation
flow reveals a series of quantum phase transitions due to the change in ground
state degeneracy. Our work thus presents a template for the study of how a
degenerate ground state manifold arising from symmetry and duality properties
in a multichannel quantum impurity model can lead to novel multicritical phases
at intermediate coupling.


title: Relatively acceptable notation

abstract:
  Shapiro's notations for natural numbers, and the associated desideratum of
acceptability - the property of a notation that all recursive functions are
computable in it - is well-known in philosophy of computing. Computable
structure theory, however, although capable of fully reconstructing Shapiro's
approach, seems to be off philosophers' radar. Based on the case study of
natural numbers with standard order, we make initial steps to reconcile these
two perspectives. First, we lay the elementary conceptual groundwork for the
reconstruction of Shapiro's approach in terms of computable structures and
show, on a few examples, how results pertinent to the former can inform our
understanding of the latter. Secondly, we prove a new result, inspired by
Shapiro's notion of acceptability, but also relevant for computable structure
theory. The result explores the relationship between the classical notion of
degree spectrum of a computable function on the structure in question -
specifically, having all c.e. degrees as a spectrum - and our ability to
compute the (image of the) successor from the (image of the) function in any
computable copy of the structure. The latter property may be otherwise seen as
relativized acceptability of every notation for the structure.


title: Lattice simulations of adjoint QCD with one Dirac overlap fermion

abstract:
  In this work we investigate the infrared behaviour of a Yang-Mills theory
coupled to a massless fermion in the adjoint representation of the gauge group
SU(2). This model has many interesting properties, corresponding to the
$\mathcal{N}=2$ Super-Yang-Mills theory without scalars and in the recent years
there has been an increasing interest toward understanding whether confinement
and fermion condensation occur at low energy. We simulate the theory on the
lattice close to the massless limit using the overlap discretization of the
fermion action, allowing a precise and clean study of the chiral symmetry
breaking pattern and of the fermion condensate. We present results for the
scale setting, the condensate and the running of the coupling constant through
the gradient flow -- all of them pointing to a theory without an infrared fixed
point and remaining confined deep in the infrared regime.


title:
Millimeter-Wave in Milliseconds: Sliding Window Network Coding
  Outperforms Rateless Codes

abstract:
  Ultra-reliability and low-latency are pivotal requirements of the new
generation of communication networks. Over the past years, to increase
throughput, adaptive active antennas were introduced in advanced wireless
communications, specifically in the domain of millimeter-wave (mmWave).
Consequently, new lower-layer techniques were proposed to cope with the higher
dimensional and electronic steerable beams. The transition from
omni-directional to highly directional antennas presents a new type of wireless
systems that deliver high bandwidth, but that are susceptible to high losses
and high latency variation. Classical approaches cannot close the rising gap
between high throughput and low delay in those advanced systems. In this work,
we incorporate effective sliding window network coding solutions in mmWave
communications. While legacy systems such as rateless codes improve delay, our
results show that they do not provide low latency communications (LLC - below
10 ms), but fixed sliding window random linear network coding (RLNC), does.
Moreover, adaptive sliding window RLNC obtains ultra-reliable LLC (URLLC - LLC
with maximum delay below 10 ms with more than 99% probability).


title:
An Information Maximization Based Blind Source Separation Approach for
  Dependent and Independent Sources

abstract:
  We introduce a new information maximization (infomax) approach for the blind
source separation problem. The proposed framework provides an
information-theoretic perspective for determinant maximization-based structured
matrix factorization methods such as nonnegative and polytopic matrix
factorization. For this purpose, we use an alternative joint entropy measure
based on the log-determinant of covariance, which we refer to as
log-determinant (LD) entropy. The corresponding (LD) mutual information between
two vectors reflects a level of their correlation. We pose the infomax BSS
criterion as the maximization of the LD-mutual information between the input
and output of the separator under the constraint that the output vectors lie in
a presumed domain set. In contrast to the ICA infomax approach, the proposed
information maximization approach can separate both dependent and independent
sources. Furthermore, we can provide a finite sample guarantee for the perfect
separation condition in the noiseless case.


title: Rusty Links in Local Chains

abstract:
  Rust successfully applies ownership types to control memory allocation. This
restricts the programs' topologies to the point where doubly-linked lists
cannot be programmed in Safe Rust. We sketch how more flexible "local"
ownership could be added to Rust, permitting multiple mutable references to
objects, provided each reference is bounded by the object's lifetime. To
maintain thread-safety, locally owned objects must remain thread-local; to
maintain memory safety, local objects can be deallocated when their owner's
lifetime expires.


title:
On an explicit reciprocity law in local class field theory via
  $(\varphi, \Gamma)$-modules

abstract:
  Let $K$ be an unramified extension of $\mathbb{Q}_2$ and $\mu_{2^n}$ the
group of $2^n$-th root of unity for a fixed integer $n\geqslant 2$. In this
paper, we give an explicit formula for the $\mu_{2^n}$-valued Hilbert symbol
over $K_n := K(\mu_{2^n})$ using the theory of $(\varphi, \Gamma)$-modules.


title:
Balanced Performance Between Energy-Delay and Bit Error Rate in UAV
  Relay Networks

abstract:
  This paper presents a new strategy for simultaneously reducing energy
consumption, transmission delays, and bit error rate in Unmanned Aerial Vehicle
UAV networks. A UAV is fitted with a wireless Bidirectional Relay BR to enable
coverage network extension and increase transmission throughput. The downside
of the BR advantages is the delay in data transmission caused by the UAV
movement. A consequence of this delay is increased total energy consumption,
causing further degradation in bit error rate performance, especially at high
SNR levels. In wireless communication, the trade-off between delay and energy
consumption is, fortunately, possible to improve performance. Therefore, this
study aims to enhance UAV network performance by reducing energy consumption,
data transmission delay, and bit error rate. A multi-objective algorithm is
employed to generate an adaptive optimal energy allocation strategy based on
the balance between energy consumption and transmission delays. The results of
theoretical analysis are illustrated with several examples. As herein
demonstrated, the proposed solution effectively balances delay and energy
efficiency in a customised system design and improves the bit error rate in UAV
networks


title: $\infty$-type theories

abstract:
  We introduce $\infty$-type theories as an $\infty$-categorical generalization
of the categorical definition of type theories introduced by the second named
author. We establish analogous results to the previous work including the
construction of initial models of $\infty$-type theories, the construction of
internal languages of models of $\infty$-type theories, and the theory-model
correspondence for $\infty$-type theories. Some structured
$(\infty,1)$-categories are naturally regarded as models of some $\infty$-type
theories. Thus, since every (1-categorical) type theory is in particular an
$\infty$-type theory, $\infty$-type theories provide a unified framework for
connections between type theories and $(\infty,1)$-categorical structures. As
an application we prove Kapulkin and Lumsdaine's conjecture that the dependent
type theory with intensional identity types gives internal languages for
$(\infty,1)$-categories with finite limits.


title: Optimal preference satisfaction for conflict-free joint decisions

abstract:
  We all have preferences when multiple choices are available. If we insist on
satisfying our preferences only, we may suffer a loss due to conflicts with
other people's identical selections. Such a case applies when the choice cannot
be divided into multiple pieces due to the intrinsic nature of the resources.
Former studies, such as the top trading cycle, examined how to conduct fair
joint decision-making while avoiding decision conflicts from the perspective of
game theory when multiple players have their own deterministic preference
profiles. However, in reality, probabilistic preferences can naturally appear
in relation to the stochastic decision-making of humans. Here, we theoretically
derive conflict-free joint decision-making that can satisfy the probabilistic
preferences of all individual players. More specifically, we mathematically
prove the conditions wherein the deviation of the resultant chance of obtaining
each choice from the individual preference profile, which we call the loss,
becomes zero, meaning that all players' satisfaction is perfectly appreciated
while avoiding decision conflicts. Furthermore, even in situations where
zero-loss conflict-free joint decision-making is unachievable, we show how to
derive joint decision-making that accomplishes the theoretical minimum loss
while ensuring conflict-free choices. Numerical demonstrations are also shown
with several benchmarks.


title: A construction of pseudo-reductive groups with non-reduced root system

abstract:
  We describe a straightforward construction of the pseudo-split absolutely
pseudo-simple groups of minimal type with irreducible root systems of type
$BC_n$; these exist only in characteristic $2$. We also give a formula for the
dimensions of their irreducible modules.


title:
Source-only realizations, weakly reversible deficiency one networks and
  dynamical equivalence

abstract:
  Reaction networks can display a wide array of dynamics. However, it is
possible for different reaction networks to display the same dynamics. This
phenomenon is called dynamical equivalence and makes network identification a
hard problem to solve. We show that to find a strongly endotactic, endotactic,
consistent, or conservative realization that is dynamically equivalent to the
mass-action system generated by a given network, it suffices to consider only
the source vertices of the given network. In addition, we show that weakly
reversible deficiency one realizations are not unique. We also present a
characterization of the dynamical relationships that exist between several
types of weakly reversible deficiency one networks.


title: Highest Cusped Waves for the Burgers-Hilbert equation

abstract:
  In this paper we prove the existence of a periodic highest, cusped, traveling
wave solution for the Burgers-Hilbert equation $f_{t} + f f_{x} = H[f]$ and
give its asymptotic behaviour at $0$. The proof combines careful asymptotic
analysis and a computer-assisted approach.


title: Metastable states in the $J_1$-$J_2$ Ising model

abstract:
  Competing interactions of nearest and next-nearest-neighbor electron spins on
a lattice with coupling constants ${J_1}$ and ${J_2}$, respectively, give the
system some interesting and unusual properties and may even underlie high
temperature superconductivity according to the resonant valence bond theory.
Here we study the ${ J_1-J_2}$ Ising model on the square lattice using the
Random local field approximation (RLFA) and Monte Carlo (MC) simulations for
various values of the ratio ${p = J_2/|J_1|}$. We show that the accuracy of the
RLFA is comparable to the mean field cluster approximation, but, in addition,
and most surprisingly, the RLFA predicts metastable states with zero
polarization at low temperature for ${p \in (0,1)}$. This is confirmed by our
MC simulations, in which the system reaches these states after the energy
relaxation of initially randomly oriented spins. Furthermore, in a moderate
external field, the MC simulations reveal the existence of still other
metastable states with nonzero polarizations. These findings could be crucial
for explaining the magnetic and electric properties of materials, in
particular, under ultrafast nonequilibrium conditions.


title: Seeding Diversity into AI Art

abstract:
  This paper argues that generative art driven by conformance to a visual
and/or semantic corpus lacks the necessary criteria to be considered creative.
Among several issues identified in the literature, we focus on the fact that
generative adversarial networks (GANs) that create a single image, in a vacuum,
lack a concept of novelty regarding how their product differs from previously
created ones. We envision that an algorithm that combines the novelty
preservation mechanisms in evolutionary algorithms with the power of GANs can
deliberately guide its creative process towards output that is both good and
novel. In this paper, we use recent advances in image generation based on
semantic prompts using OpenAI's CLIP model, interrupting the GAN's iterative
process with short cycles of evolutionary divergent search. The results of
evolution are then used to continue the GAN's iterative process; we hypothesise
that this intervention will lead to more novel outputs. Testing our hypothesis
using novelty search with local competition, a quality-diversity evolutionary
algorithm that can increase visual diversity while maintaining quality in the
form of adherence to the semantic prompt, we explore how different notions of
visual diversity can affect both the process and the product of the algorithm.
Results show that even a simplistic measure of visual diversity can help
counter a drift towards similar images caused by the GAN. This first experiment
opens a new direction for introducing higher intentionality and a more nuanced
drive for GANs.


title:
Second-order flows for computing the ground states of rotating
  Bose-Einstein condensates

abstract:
  Second-order flows in this paper refer to some artificial evolutionary
differential equations involving second-order time derivatives distinguished
from gradient flows which are considered to be first-order flows. This is a
popular topic due to the recent advances of inertial dynamics with damping in
convex optimization. Mathematically, the ground state of a rotating
Bose-Einstein condensate (BEC) can be modelled as a minimizer of the
Gross-Pitaevskii energy functional with angular momentum rotational term under
the normalization constraint. We introduce two types of second-order flows as
energy minimization strategies for this constrained non-convex optimization
problem, in order to approach the ground state. The proposed artificial
dynamics are novel second-order nonlinear hyperbolic partial differential
equations with dissipation. Several numerical discretization schemes are
discussed, including explicit and semi-implicit methods for temporal
discretization, combined with a Fourier pseudospectral method for spatial
discretization. These provide us a series of efficient and robust algorithms
for computing the ground states of rotating BECs. Particularly, the newly
developed algorithms turn out to be superior to the state-of-the-art numerical
methods based on the gradient flow. This is judged by the fact that larger size
of time steps can be applied in explicit methods, and much smaller number of
time steps are needed in both explicit and semi-implicit discretizations, while
every time step encounters almost the same computational complexity in
comparison with the respective gradient flow type approaches. Rich and detailed
numerical examples are documented for verification and comparison.


title: Biographical: A Semi-Supervised Relation Extraction Dataset

abstract:
  Extracting biographical information from online documents is a popular
research topic among the information extraction (IE) community. Various natural
language processing (NLP) techniques such as text classification, text
summarisation and relation extraction are commonly used to achieve this. Among
these techniques, RE is the most common since it can be directly used to build
biographical knowledge graphs. RE is usually framed as a supervised machine
learning (ML) problem, where ML models are trained on annotated datasets.
However, there are few annotated datasets for RE since the annotation process
can be costly and time-consuming. To address this, we developed Biographical,
the first semi-supervised dataset for RE. The dataset, which is aimed towards
digital humanities (DH) and historical research, is automatically compiled by
aligning sentences from Wikipedia articles with matching structured data from
sources including Pantheon and Wikidata. By exploiting the structure of
Wikipedia articles and robust named entity recognition (NER), we match
information with relatively high precision in order to compile annotated
relation pairs for ten different relations that are important in the DH domain.
Furthermore, we demonstrate the effectiveness of the dataset by training a
state-of-the-art neural model to classify relation pairs, and evaluate it on a
manually annotated gold standard set. Biographical is primarily aimed at
training neural models for RE within the domain of digital humanities and
history, but as we discuss at the end of this paper, it can be useful for other
purposes as well.


title: Deep-Attack over the Deep Reinforcement Learning

abstract:
  Recent adversarial attack developments have made reinforcement learning more
vulnerable, and different approaches exist to deploy attacks against it, where
the key is how to choose the right timing of the attack. Some work tries to
design an attack evaluation function to select critical points that will be
attacked if the value is greater than a certain threshold. This approach makes
it difficult to find the right place to deploy an attack without considering
the long-term impact. In addition, there is a lack of appropriate indicators of
assessment during attacks. To make the attacks more intelligent as well as to
remedy the existing problems, we propose the reinforcement learning-based
attacking framework by considering the effectiveness and stealthy
spontaneously, while we also propose a new metric to evaluate the performance
of the attack model in these two aspects. Experimental results show the
effectiveness of our proposed model and the goodness of our proposed evaluation
metric. Furthermore, we validate the transferability of the model, and also its
robustness under the adversarial training.


title: Distinguishing Dirac vs. Majorana Neutrinos: a Cosmological Probe

abstract:
  Cosmic background neutrinos ($C_{\nu}B)$ helicity composition is different
for Dirac or Majorana neutrinos making detectors based on $C_{\nu}B$ capture
sensitive to the nature of neutrinos. We calculate, for the first time, the
helicity changes of neutrinos crossing dark matter fields, to quantitatively
calculate this effect on the capture rate. We show that a fraction of neutrinos
change their helicity, regardless of them being deflected by a void or a dark
matter halo. The average signal from the 100 most massive voids or halos in a
Gpc$^3$ gives a prediction that if neutrinos are Dirac, the density of the
$C_{\nu} B$ background measured on Earth should be 48 cm${^{-3}}$ for
left-helical neutrinos, a decrease of 15% (53.6 cm${^{-3}}$; 5%) for a halo
(void) with respect to the standard calculation without including gravitational
effects due to large scale structures. In terms of the total capture rate in a
100 g tritium detector, this translates in $4.9^{+1.1}_{-0.8}$ neutrinos per
year for the Dirac case, as a function of the unknown neutrino mass scale, or
8.1 per year if neutrinos are Majorana. Thus although smaller than the factor
two for the non-relativistic case, it is still large enough to be detected and
it highlights the power of future $C_{\nu} B$ detectors, as an alternative to
neutrinoless double beta decay experiments, to discover the neutrino nature.


title:
Automated Driving Systems: Impact of Haptic Guidance on Driving
  Performance after a Take Over Request

abstract:
  In conditional automation, a response from the driver is expected when a take
over request is issued due to unexpected events, emergencies, or reaching the
operational design domain boundaries. Cooperation between the automated driving
system and the driver can help to guarantee a safe and pleasant transfer if the
driver is guided through a haptic guidance system that applies a slight
counter-steering force to the steering wheel. We examine in this work the
impact of haptic guidance systems on driving performance after a take over
request was triggered to avoid sudden obstacles on the road. We studied
different driver conditions that involved Non-Driving Related Tasks (NRDT).
Results showed that haptic guidance systems increased road safety by reducing
the lateral error, the distance and reaction time to a sudden obstacle and the
number of collisions.


title:
Novel quantum phase of the chromium spinel oxide HgCr$_{\rm 2}$O$_{\rm
  4}$ in high magnetic fields

abstract:
  In this study, we have performed the magnetocaloric effect and the specific
heat measurements of chromium spinel oxide HgCr$_2$O$_4$, wherein the magnetic
Cr$^{3+}$ ions form a highly frustrated pyrochlore lattice with significant
spin-lattice coupling. In addition to the known magnetic-field-induced phases,
our thermodynamic measurements detect a novel quantum phase just before the
saturation of the magnetization, which has not been expected from the classical
theories of the pyrochlore lattice antiferromagnet with spin-lattice coupling.
Based on recent theoretical model calculation, we discuss the possibility of a
spin nematic state appearing for this quantum phase.


title: 100% of the non-trivial zeros of $\zeta(s)$ are on the critical line

abstract:
  In this manuscript we denote by $N(T)$ the number of zeros $\rho$ of
$\zeta(s)$ such that $0<\Im(\rho)<T$. Denote by $N_0(T)$ the number of zeros
$\rho$ of $\zeta(s)$ such that $\Re(\rho)=\frac{1}{2}$ and $0<\Im(\rho)<T$.
Denote by $N_\epsilon(T)$ the number of zeros $\rho$ of $\zeta(s)$ such that
$\frac{1}{2}-\epsilon<\Re(\rho)<\frac{1}{2}+\epsilon$ and
$0<\Im(\rho)<T+\epsilon$ where $\epsilon>0$ is arbitrarily small. We consider a
rectangle $R_\epsilon=\{\sigma+it\mid \frac{1}{2}-\epsilon\leq \sigma\leq
\frac{1}{2}+\epsilon,\ 0\leq t\leq T+\epsilon\}$. Since $\xi(s)$ is non zero in
the real axis, so choose $\epsilon>0$ such that $\xi(s)$ has no zeros on the
boundary of rectangle $R_\epsilon$. So we use the Cauchy's argument principle
on $\xi(s)$ on the rectangle $R_\epsilon$ along with properties of Riemann zeta
function and Riemann xi function and prove that as $T\to \infty$
$$N_0(T)=\frac{T}{2\pi}\log\left(\frac{T}{2\pi}\right)-\frac{T}{2\pi}-\frac{1}{8}+\mathcal{O}(\log
T)$$ Also if we have $$\kappa=\liminf_{T\to \infty} \frac{N_0(T)}{N(T)}$$ then
we prove as a consequence that $\kappa=1$.


title:
Derivation of the fractional porous medium equation from a microscopic
  dynamics

abstract:
  In this article we derive the fractional porous medium equation for any power
of the fractional Laplacian as the hydrodynamic limit of a microscopic dynamics
of random particles with long range interactions, but the jump rate highly
depends on the occupancy near the sites where the interactions take place.


title:
Hydrogen emission from meteors and meteorites: mapping traces of
  H$_{2}$O molecules and organic compounds in small Solar system bodies

abstract:
  The hydrogen emission from meteors is assumed to originate mainly from the
meteoroid composition, making it a potential tracer of H$_{2}$O molecules and
organic compounds. H$\alpha$ line was previously detected in individual
fireballs, but its variation in a larger meteor dataset and dependency on the
dynamical origin and physical properties have not yet been studied. Here we
investigate the relative intensity of H$\alpha$ within 304 meteor spectra
observed by the AMOS network. We demonstrate that H$\alpha$ emission is favored
in faster meteors ($v_i >>$ 30 km s$^{-1}$) which form the high-temperature
spectral component. H$\alpha$ was found to be a characteristic spectral feature
of cometary meteoroids with $\sim$ 92\% of all meteoroids with detected
H$\alpha$ originating from Halley-type and long-period orbits. Our results
suggest that hydrogen is being depleted from meteoroids with lower perihelion
distances (q $<$ 0.4 au). No asteroidal meteoroids with detected H emission
were found. However, using spectral data from simulated ablation of different
meteorite types, we show that H emission from asteroidal materials can occur,
and apparently correlates with their water and organic matter content.
Strongest H emission was detected from carbonaceous chondrites (CM and CV) and
achondrites (ureilite and aubrite), while it was lacking in most ordinary
chondrites. The detection of H$\alpha$ in asteroidal meteoroids could be used
to identify meteoroids of carbonaceous or achondritic composition. Overall, our
results suggest that H$\alpha$ emission correlates with the emission of other
volatiles (Na and CN) and presents a suitable tracer of water and organic
matter in meteoroids.


title: Period integrals of hypersurfaces via tropical geometry

abstract:
  Let $\left\{ Z_t \right\}_t$ be a one-parameter family of complex toric
hypersurfaces of dimension $d \geq 1$. We compute asymptotics of period
integrals for $\left\{ Z_t \right\}_t$ by applying the method of
Abouzaid--Ganatra--Iritani--Sheridan, which uses tropical geometry. As
integrands, we consider Poincar\'{e} residues of meromorphic $(d+1)$-forms on
the ambient toric variety, which have poles along the hypersurface $Z_t$. The
cycles over which we integrate them are spheres and tori which correspond to
tropical $(0, d)$-cycles and $(d, 0)$-cycles on the tropicalization of $\left\{
Z_t \right\}_t$ respectively. In the case of $d=1$, we explicitly write down
the polarized logarithmic Hodge structure of Kato--Usui at the limit as a
corollary.


title:
Statistical Analysis of Received Signal Strength in Industrial IoT
  Distributed Massive MIMO Systems

abstract:
  The Fifth Generation (5G) of wireless networks introduced native support for
Machine-Type Communication (MTC), which is a key enabler for the Internet of
Things (IoT) revolution. Current 5G standards are not yet capable of fully
satisfying the requirements of critical MTC (cMTC) and massive MTC (mMTC) use
cases. This is the main reason why industry and academia have already started
working on technical solutions for beyond-5G and Sixth Generation (6G)
networks. One technological solution that has been extensively studied is the
combination of network densification, massive Multiple-Input Multiple-Output
(mMIMO) systems and user-centric design, which is known as distributed mMIMO or
Cell-Free (CF) mMIMO. Under this new paradigm, there are no longer cell
boundaries: all the Access Points (APs) on the network cooperate to jointly
serve all the devices. In this paper, we compare the performance of traditional
mMIMO and different distributed mMIMO setups, and quantify the macro diversity
and signal spatial diversity performance they provide. Aiming at the uplink in
industrial indoor scenarios, we adopt a path loss model based on real
measurement campaigns. Monte Carlo simulation results show that the grid
deployment of APs provide higher average channel gains, but radio stripes
deployments provide lower variability of the received signal strength.


title: Semantic localization in BIM using a 3D LiDAR sensor

abstract:
  Conventional sensor-based localization relies on high-precision maps. These
maps are generally built using specialized mapping techniques, which involve
high labor and computational costs. While in the architectural, engineering and
construction industry, building information models (BIMs) are available and can
provide informative descriptions of environments. This paper explores an
effective way to localize a mobile 3D LiDAR sensor in BIM considering both
geometric and semantic properties. Specifically, we first convert original BIM
to semantic maps using categories and locations of BIM elements. After that, a
coarse-to-fine semantic localization is performed to align laser points to the
map via iterative closest point registration. The experimental results show
that the semantic localization can track the pose with only scan matching and
present centimeter-level errors over 340 meters traveling, thus demonstrating
the feasibility of the proposed mapping-free localization framework. The
results also show that using semantic information can help reduce localization
errors in BIM.


title:
Stochastic resetting of a population of random walks with
  resetting-rate-dependent diffusivity

abstract:
  We consider the problem of diffusion with stochastic resetting in a
population of random walks where the diffusion coefficient is not constant, but
behaves as a power-law of the average resetting rate of the population.
Resetting occurs only beyond a threshold distance from the origin. This problem
is motivated by physical realizations like soft matter under shear, where
diffusion of a walk is induced by resetting events of other walks. We first
reformulate in the broader context of diffusion with stochastic resetting the
so-called H\'ebraud-Lequeux model for plasticity in dense soft matter, in which
diffusivity is proportional to the average resetting rate. Depending on
parameter values, the response to a weak external field may be either linear or
non-linear with a non-zero average position for a vanishing applied field, and
the transition between these two regimes may be interpreted as a continuous
phase transition. Extending the model by considering a general power-law
relation between diffusivity and average resetting rate, we notably find a
discontinuous phase transition between a finite diffusivity and a vanishing
diffusivity in the small field limit.


title:
Modular Anomaly Equation for Schur Index of $\mathcal{N}=4$
  Super-Yang-Mills

abstract:
  We propose a novel modular anomaly equation for the unflavored Schur index in
the $\mathcal{N}=4$ $SU(N)$ super-Yang-Mills theory. The vanishing conditions
overdetermine the modular ambiguity ansatz from the equation, thus together
they are sufficient to recursively compute the exact Schur indices for all
$SU(N)$ gauge groups. Using the representations as MacMahon's generalized
sum-of-divisors functions and Jacobi forms, we then prove our proposal as well
as elucidate a general formula conjectured by Pan and Peelaers.


title:
A Novel Approach to Fairness in Automated Decision-Making using
  Affective Normalization

abstract:
  Any decision, such as one about who to hire, involves two components. First,
a rational component, i.e., they have a good education, they speak clearly.
Second, an affective component, based on observables such as visual features of
race and gender, and possibly biased by stereotypes. Here we propose a method
for measuring the affective, socially biased, component, thus enabling its
removal. That is, given a decision-making process, these affective measurements
remove the affective bias in the decision, rendering it fair across a set of
categories defined by the method itself. We thus propose that this may solve
three key problems in intersectional fairness: (1) the definition of categories
over which fairness is a consideration; (2) an infinite regress into smaller
and smaller groups; and (3) ensuring a fair distribution based on basic human
rights or other prior information. The primary idea in this paper is that
fairness biases can be measured using affective coherence, and that this can be
used to normalize outcome mappings. We aim for this conceptual work to expose a
novel method for handling fairness problems that uses emotional coherence as an
independent measure of bias that goes beyond statistical parity.


title: Entity-aware Transformers for Entity Search

abstract:
  Pre-trained language models such as BERT have been a key ingredient to
achieve state-of-the-art results on a variety of tasks in natural language
processing and, more recently, also in information retrieval.Recent research
even claims that BERT is able to capture factual knowledge about entity
relations and properties, the information that is commonly obtained from
knowledge graphs. This paper investigates the following question: Do BERT-based
entity retrieval models benefit from additional entity information stored in
knowledge graphs? To address this research question, we map entity embeddings
into the same input space as a pre-trained BERT model and inject these entity
embeddings into the BERT model. This entity-enriched language model is then
employed on the entity retrieval task. We show that the entity-enriched BERT
model improves effectiveness on entity-oriented queries over a regular BERT
model, establishing a new state-of-the-art result for the entity retrieval
task, with substantial improvements for complex natural language queries and
queries requesting a list of entities with a certain property. Additionally, we
show that the entity information provided by our entity-enriched model
particularly helps queries related to less popular entities. Last, we observe
empirically that the entity-enriched BERT models enable fine-tuning on limited
training data, which otherwise would not be feasible due to the known
instabilities of BERT in few-sample fine-tuning, thereby contributing to
data-efficient training of BERT for entity search.


title:
La R\'esistance: Harnessing Heterogeneous Resources for Adaptive
  Resiliency in 6G Networks

abstract:
  Recent years have seen more critical applications designed to protect human
lives (e.g., environmental sensing, emergency response, and tactical defense)
being deployed over wireless networks. These critical deployments expect higher
data rates, ultra-low latency, and ultra-high reliability. 6G wireless networks
are expected to fill the gap in terms of the first two aspects (i.e., higher
data rates and ultra-low latency), however providing ultra-high reliability is
a wide open challenge. All is well when everything works but when there is a
failure or a security attack, the entire system collapses exposing the
associated human lives to imminent danger. Avoiding this requires the strongest
of assurances that safety and security aspects are protected no matter what.
Large scale critical applications are waiting for this piece of the puzzle to
be solved. At this juncture, we envision the bold theme of La R\'esistance 6G
(LR6G) that would pave the way for deploying mission-critical applications and
services over 6G networks. It aims to achieve ultra-high reliability and
resiliency to any disruptions, be it failures or security attacks. A few
disruptions are easy to handle such as a cloud VM or primary link failure. In
both of these cases, applications can be restored by activating the standby
resource. However, some disruptions can be detrimental such as when a
cut-vertex fails or when the disruption leaves the critical application to fail
without access to a standby resource. These critical applications (e.g. Smart
Manufacturing, Smart City, Ocean monitoring, Wildfire monitoring, etc.) are
highly distributed in nature. They must continue to deliver their mission
objectives during a disruption to protect human lives. In this paper, we
present our LR6G vision and outline the challenges towards achieving this
vision.


title:
MEGH: A parametric class of general hazard models for clustered survival
  data

abstract:
  In many applications of survival data analysis, the individuals are treated
in different medical centres or belong to different clusters defined by
geographical or administrative regions. The analysis of such data requires
accounting for between-cluster variability. Ignoring such variability would
impose unrealistic assumptions in the analysis and could affect the inference
on the statistical models. We develop a novel parametric mixed-effects general
hazard (MEGH) model that is particularly suitable for the analysis of clustered
survival data. The proposed structure generalises the mixed-effects
proportional hazards (MEPH) and mixed-effects accelerated failure time (MEAFT)
structures, among other structures, which are obtained as special cases of the
MEGH structure. We develop a likelihood-based algorithm for parameter
estimation in general subclasses of the MEGH model, which is implemented in our
R package {\tt MEGH}. We propose diagnostic tools for assessing the random
effects and their distributional assumption in the proposed MEGH model. We
investigate the performance of the MEGH model using theoretical and simulation
studies, as well as a real data application on leukemia.


title: CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

abstract:
  Recently, large-scale pre-training methods like CLIP have made great progress
in multi-modal research such as text-video retrieval. In CLIP, transformers are
vital for modeling complex multi-modal relations. However, in the vision
transformer of CLIP, the essential visual tokenization process, which produces
discrete visual token sequences, generates many homogeneous tokens due to the
redundancy nature of consecutive and similar frames in videos. This
significantly increases computation costs and hinders the deployment of video
retrieval models in web applications. In this paper, to reduce the number of
redundant video tokens, we design a multi-segment token clustering algorithm to
find the most representative tokens and drop the non-essential ones. As the
frame redundancy occurs mostly in consecutive frames, we divide videos into
multiple segments and conduct segment-level clustering. Center tokens from each
segment are later concatenated into a new sequence, while their original
spatial-temporal relations are well maintained. We instantiate two clustering
algorithms to efficiently find deterministic medoids and iteratively partition
groups in high dimensional space. Through this token clustering and center
selection procedure, we successfully reduce computation costs by removing
redundant visual tokens. This method further enhances segment-level semantic
alignment between video and text representations, enforcing the spatio-temporal
interactions of tokens from within-segment frames. Our method, coined as
CenterCLIP, surpasses existing state-of-the-art by a large margin on typical
text-video benchmarks, while reducing the training memory cost by 35\% and
accelerating the inference speed by 14\% at the best case. The code is
available at
\href{{https://github.com/mzhaoshuai/CenterCLIP}}{{https://github.com/mzhaoshuai/CenterCLIP}}.


title: Exploration in Deep Reinforcement Learning: A Survey

abstract:
  This paper reviews exploration techniques in deep reinforcement learning.
Exploration techniques are of primary importance when solving sparse reward
problems. In sparse reward problems, the reward is rare, which means that the
agent will not find the reward often by acting randomly. In such a scenario, it
is challenging for reinforcement learning to learn rewards and actions
association. Thus more sophisticated exploration methods need to be devised.
This review provides a comprehensive overview of existing exploration
approaches, which are categorized based on the key contributions as follows
reward novel states, reward diverse behaviours, goal-based methods,
probabilistic methods, imitation-based methods, safe exploration and
random-based methods. Then, the unsolved challenges are discussed to provide
valuable future research directions. Finally, the approaches of different
categories are compared in terms of complexity, computational effort and
overall performance.


title: Online Learning in Fisher Markets with Unknown Agent Preferences

abstract:
  In a Fisher market, agents (users) spend a budget of (artificial) currency to
buy goods that maximize their utilities, and producers set prices on
capacity-constrained goods such that the market clears. The equilibrium prices
in such a market are typically computed through the solution of a convex
program, e.g., the Eisenberg-Gale program, that aggregates users' preferences
into a centralized social welfare objective. However, the computation of
equilibrium prices using convex programs assumes that all transactions happen
in a static market wherein all users are present simultaneously and relies on
complete information on each user's budget and utility function.
  Since, in practice, information on users' utilities and budgets is unknown
and users tend to arrive over time in the market, we study an online variant of
Fisher markets, wherein users enter the market sequentially. We focus on the
setting where users have linear utilities with privately known utility and
budget parameters drawn i.i.d. from a distribution $\mathcal{D}$. In this
setting, we develop a simple yet effective algorithm to set prices that
preserves user privacy while achieving a regret and capacity violation of
$O(\sqrt{n})$, where $n$ is the number of arriving users and the capacities of
the goods scale as $O(n)$. Here, our regret measure represents the optimality
gap in the objective of the Eisenberg-Gale program between the online
allocation policy and that of an offline oracle with complete information on
users' budgets and utilities. To establish the efficacy of our approach, we
show that even an algorithm that sets expected equilibrium prices with perfect
information on the distribution $\mathcal{D}$ cannot achieve both a regret and
constraint violation of better than $\Omega(\sqrt{n})$. Finally, we present
numerical experiments to demonstrate the performance of our approach relative
to several benchmarks.


title: Metastable defects decrease the fill factor of solar cells

abstract:
  Cu(In,Ga)Se2 based solar cells exceed power conversion efficiencies of 23 %.
Yet, the fill factor of these solar cells, with best values around 80 %, is
relatively low (Si reaches 84.9%) mostly due to diode factors greater than one.
Recently, we proposed metastable defects, a general feature of the Cu(In,Ga)Se2
alloy, to be the origin of the increased diode factor. We measure the diode
factor of the bare absorber layers by excitation-dependent photoluminescence.
For high quality and thus high luminescent polycrystalline absorbers, we
evaluate the diode factor excitation dependence over four orders of magnitude.
Using simulations and the model of metastable defects, we can well describe the
experimental findings on n- and p-type epitaxial films as well as the
polycrystalline absorbers, providing additional evidence for this model. We
find that the diode factors measured optically by photoluminescence impose a
lower limit for the diode factor measured electrically on a finished solar
cell. Interestingly, the lowest diode factor (optical and electrical) and
consequently highest fill factor of 81.0 % is obtained by Ag alloying, i.e. an
(Ag,Cu)(In,Ga)Se2 absorber. This finding hints to a pathway to increase fill
factors and thus efficiencies for Cu(In,Ga)Se2-based solar cells.


title:
Insights on the variability of Cu filament formation in the SiO2
  electrolyte of quantized-conductance conductive bridge random access memory
  devices

abstract:
  Conductive bridge random access memory devices such as Cu/SiO2/W are
promising candidates for applications in neuromorphic computing due to their
fast, low-voltage switching, multiple-conductance states, scalability, low
off-current, and full compatibility with advanced Si CMOS technologies. The
conductance states, which can be quantized, originate from the formation of a
Cu filament in the SiO2 electrolyte due to cation-migration-based
electrochemical processes. A major challenge related to the filamentary nature
is the strong variability of the voltage required to switch the device to its
conducting state. Here, based on a statistical analysis of more than hundred
fifty Cu/SiO2/W devices, we point to the key role of the activation energy
distribution for copper ion diffusion in the amorphous SiO2. The cycle-to-cycle
variability is modeled well when considering the theoretical energy landscape
for Cu diffusion paths to grow the filament. Perspectives of this work point to
developing strategies to narrow the distribution of activation energies in
amorphous SiO2.


title: Discrete models of continuous behavior of collective adaptive systems

abstract:
  Artificial ants are "small" units, moving autonomously around on a shared,
dynamically changing "space", directly or indirectly exchanging some kind of
information. Artificial ants are frequently conceived as a paradigm for
collective adaptive systems. In this paper, we discuss means to represent
continuous moves of "ants" in discrete models. More generally, we challenge the
role of the notion of "time" in artificial ant systems and models. We suggest a
modeling framework that structures behavior along causal dependencies, and not
along temporal relations. We present all arguments by help of a simple example.
As a modeling framework we employ Heraklit; an emerging framework that already
has proven its worth in many contexts.


title:
Function Decomposition Tree with Causality-First Perspective and
  Systematic Description of Problems in Materials Informatics

abstract:
  As interdisciplinary science is flourishing because of materials informatics
and additional factors; a systematic way is required for expressing knowledge
and facilitating communication between scientists in various fields. A function
decomposition tree is such a representation, but domain scientists face
difficulty in constructing it. Thus, this study cites the general problems
encountered by beginners in generating function decomposition trees and
proposes a new function decomposition representation method based on a
causality-first perspective for resolution of these problems. The
causality-first decomposition tree was obtained from a workflow expressed
according to the processing sequence. Moreover, we developed a program that
performed automatic conversion using the features of the causality-first
decomposition trees. The proposed method was applied to materials informatics
to demonstrate the systematic representation of expert knowledge and its
usefullness.


title: The storage ring proton EDM experiment

abstract:
  We describe a proposal to search for an intrinsic electric dipole moment
(EDM) of the proton with a sensitivity of \targetsens, based on the vertical
rotation of the polarization of a stored proton beam. The New Physics reach is
of order $10^~3$TeV mass scale. Observation of the proton EDM provides the best
probe of CP-violation in the Higgs sector, at a level of sensitivity that may
be inaccessible to electron-EDM experiments. The improvement in the sensitivity
to $\theta_{QCD}$, a parameter crucial in axion and axion dark matter physics,
is about three orders of magnitude.