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arxiv_dataset-191002309.05196 | Does Writing with Language Models Reduce Content Diversity?
cs.CL cs.CY cs.HC cs.LG
Large language models (LLMs) have led to a surge in collaborative writing
with model assistance. As different users incorporate suggestions from the same
model, there is a risk of decreased diversity in the produced content,
potentially limiting diverse perspectives in public discourse. In this work, we
measure the impact of co-writing on diversity via a controlled experiment,
where users write argumentative essays in three setups -- using a base LLM
(GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We
develop a set of diversity metrics and find that writing with InstructGPT (but
not the GPT3) results in a statistically significant reduction in diversity.
Specifically, it increases the similarity between the writings of different
authors and reduces the overall lexical and content diversity. We additionally
find that this effect is mainly attributable to InstructGPT contributing less
diverse text to co-written essays. In contrast, the user-contributed text
remains unaffected by model collaboration. This suggests that the recent
improvement in generation quality from adapting models to human feedback might
come at the cost of more homogeneous and less diverse content.
| arxiv topic:cs.CL cs.CY cs.HC cs.LG |
arxiv_dataset-191012309.05296 | Polyimides Crosslinked by Aromatic Molecules and Nanocomposites for High
Temperature Capacitive Energy Storage
physics.app-ph
High temperature polymer-based dielectric capacitors are crucial for
application in electronic power systems. However, the storage performance of
conventional dielectrics polymer dramatically deteriorates due to the thermal
breakdown under concurrent high temperatures and electric fields, and there are
hardly reports on the causes of thermal breakdown from the aspects of the high
temperature conduction loss and Joule heat dissipation. Herein, a combined
strategy of crosslinking and compositing for polyimide-based nanocomposites is
proposed, which minimizes the thermal breakdown by significantly inhibiting the
high-temperature conduction loss and enhancing the high thermal conductivity.
Furthermore, the rationale of the strategy was theoretically and experimentally
verified from multiple perspectives. The charge-trapping effect is directly
observed and quantitatively probed by Kelvin probe force microscopy with nano
level resolution, indicating that the crosslinking network introduces local
deep traps and effectively suppresses the charge transport. The thermal
conductivity of the nanocomposites inhibits the high temperature thermal
breakdown, which is confirmed by phase field simulations. Consequently, the
optimized nanocomposites possess an ultra high discharge energy density(Ud) of
5.45 J/cm3 and 3.54 J/cm3 with a charge discharge efficiency, respectively,
which outperforms the reported polyimide based dielectric nanocomposites. This
work provides a scalable direction for high temperature polymer based
capacitors with excellent performance.
| arxiv topic:physics.app-ph |
arxiv_dataset-191022309.05396 | SlideSpeech: A Large-Scale Slide-Enriched Audio-Visual Corpus
cs.SD eess.AS
Multi-Modal automatic speech recognition (ASR) techniques aim to leverage
additional modalities to improve the performance of speech recognition systems.
While existing approaches primarily focus on video or contextual information,
the utilization of extra supplementary textual information has been overlooked.
Recognizing the abundance of online conference videos with slides, which
provide rich domain-specific information in the form of text and images, we
release SlideSpeech, a large-scale audio-visual corpus enriched with slides.
The corpus contains 1,705 videos, 1,000+ hours, with 473 hours of high-quality
transcribed speech. Moreover, the corpus contains a significant amount of
real-time synchronized slides. In this work, we present the pipeline for
constructing the corpus and propose baseline methods for utilizing text
information in the visual slide context. Through the application of keyword
extraction and contextual ASR methods in the benchmark system, we demonstrate
the potential of improving speech recognition performance by incorporating
textual information from supplementary video slides.
| arxiv topic:cs.SD eess.AS |
arxiv_dataset-191032309.05496 | Bayesian spatial+: A joint model perspective
stat.ME
Spatial confounding is a common issue in spatial regression models, occurring
when spatially indexed covariates that model the mean of the response are
correlated with a spatial effect included in the model. This dependence,
particularly at high spatial frequencies combined with smoothing, can introduce
bias in the regression coefficient estimates. The spatial+ framework is a
widely used two-stage frequentist approach to mitigate spatial confounding by
explicitly modeling and removing the spatial structure in the confounding
covariate, replacing it with residuals in the second-stage model for the
response. However, frequentist spatial+ does not propagate uncertainty from the
first-stage estimation to the second stage, and inference can be cumbersome in
a frequentist setting. In contrast, a Bayesian joint modeling framework
inherently propagates uncertainty between stages and allows for direct
inference on the model parameters. Despite its advantages, the original
spatial+ method does not ensure the residuals and spatial effects in the
second-stage model are free of shared high spatial frequencies without
additional assumptions. To address this, we propose a novel joint prior for the
smoothness parameters of the spatial effects that mitigates this issue while
preserving the predictive power of the model. We demonstrate the efficacy of
our approach through simulation studies and real-world applications.
| arxiv topic:stat.ME |
arxiv_dataset-191042309.05596 | Output-Positive Adaptive Control of Hyperbolic PDE-ODE Cascades
math.OC
In this paper, we propose a new adaptive Control Barrier Function (aCBF)
method to design the output-positive adaptive control law for a hyperbolic
PDE-ODE cascade with parametric uncertainties. This method employs the recent
adaptive control approach with batch least-squares identification (BaLSI,
pronounced "ballsy") that completes perfect parameter identification in finite
time and offers a previously unforeseen advantage in safe control design with
aCBF, which we elucidate in this paper. Since the true challenge is exhibited
for CBF of a high relative degree, we undertake a control design in this paper
for a class of systems that possess a particularly extreme relative degree:
$2\times2$ hyperbolic PDEs sandwiched by a strict-feedback nonlinear ODE and a
linear ODE, where the unknown coefficients are associated with the PDE
in-domain coupling terms and with the input signal of the distal ODE. The
designed output-positive adaptive controller guarantees the positivity of the
output signal that is the furthermost state from the control input as well as
the exponential regulation of the overall plant state to zero. The
effectiveness of the proposed method is illustrated by numerical simulation.
| arxiv topic:math.OC |
arxiv_dataset-191052309.05696 | What are the parities of photon-ring images near a black hole?
gr-qc astro-ph.CO
Light that grazes a black-hole event horizon can loop around one or more
times before escaping again, resulting for distance observers in an infinite
sequence of ever fainter and more delayed images near the black hole shadow. In
the case of the M87 and Sgr A$^*$ back holes, the first of these so-called
photon-ring images have now been observed. A question then arises: are such
images minima, maxima, or saddle-points in the sense of Fermat's principle in
gravitational lensing? or more briefly, the title question above. In the theory
of lensing by weak gravitational fields, image parities are readily found by
considering the time-delay surface (also called the Fermat potential or the
arrival-time surface). In this work, we extend the notion of the time delay
surface to strong gravitational fields and compute the surface for a
Schwarzschild black hole. The time-delay surface is the difference of two
wavefronts, one travelling forward from the source and one travelling backwards
from the observer. Image parities are read off from the topography of the
surface, exactly as in the weak-field regime, but the surface itself is more
complicated. Of the images, furthest from the black hole and similar to the
weak-field limit, are a minimum and a saddle point. The strong field repeats
the pattern, corresponding to light taking one or more loops around the back
hole. In between, there are steeply-rising walls in the time-delay surface,
which can be interpreted as maxima and saddle points that are infinitely
delayed and not observable -- these correspond to light rays taking a U-turn
around the black hole.
| arxiv topic:gr-qc astro-ph.CO |
arxiv_dataset-191062309.05796 | On the degrees of irreducible characters fixed by some field
automorphism in finite groups
math.GR
We prove a variant of the Theorem of Ito-Michler, investigating the
properties of finite groups where a prime number $p$ does not divide the degree
of any irreducible character left invariant by some Galois automorphism
$\sigma$ of order $p$.
| arxiv topic:math.GR |
arxiv_dataset-191072309.05896 | Silicon charge pump operation limit above and below liquid helium
temperature
cond-mat.mes-hall
Semiconductor tunable barrier single-electron pumps can produce output
current of hundreds of picoamperes at sub ppm precision, approaching the
metrological requirement for the direct implementation of the current standard.
Here, we operate a silicon metal-oxide-semiconductor electron pump up to a
temperature of 14 K to understand the temperature effect on charge pumping
accuracy. The uncertainty of the charge pump is tunnel limited below liquid
helium temperature, implying lowering the temperature further does not greatly
suppress errors. Hence, highly accurate charge pumps could be confidently
achieved in a $^4$He cryogenic system, further promoting utilization of the
revised quantum current standard across the national measurement institutes and
industries worldwide.
| arxiv topic:cond-mat.mes-hall |
arxiv_dataset-191082309.05996 | RGB-Guided Resolution Enhancement of IR Images
eess.IV
This paper introduces a novel method for RGB-Guided Resolution Enhancement of
infrared (IR) images called Guided IR Resolution Enhancement (GIRRE). In the
area of single image super resolution (SISR) there exists a wide variety of
algorithms like interpolation methods or neural networks to improve the spatial
resolution of images. In contrast to SISR, even more information can be
gathered on the recorded scene when using multiple cameras. In our setup, we
are dealing with multi image super resolution, especially with stereo super
resolution. We consider a color camera and an IR camera. Current IR sensors
have a very low resolution compared to color sensors so that recent color
sensors take up 100 times more pixels than IR sensors. To this end, GIRRE
increases the spatial resolution of the low-resolution IR image. After that,
the upscaled image is filtered with the aid of the high-resolution color image.
We show that our method achieves an average PSNR gain of 1.2 dB and at best up
to 1.8 dB compared to state-of-the-art methods, which is visually noticeable.
| arxiv topic:eess.IV |
arxiv_dataset-191092309.06096 | iPhonMatchNet: Zero-Shot User-Defined Keyword Spotting Using Implicit
Acoustic Echo Cancellation
eess.AS eess.SP
In response to the increasing interest in human--machine communication across
various domains, this paper introduces a novel approach called iPhonMatchNet,
which addresses the challenge of barge-in scenarios, wherein user speech
overlaps with device playback audio, thereby creating a self-referencing
problem. The proposed model leverages implicit acoustic echo cancellation
(iAEC) techniques to increase the efficiency of user-defined keyword spotting
models, achieving a remarkable 95% reduction in mean absolute error with a
minimal increase in model size (0.13%) compared to the baseline model,
PhonMatchNet. We also present an efficient model structure and demonstrate its
capability to learn iAEC functionality without requiring a clean signal. The
findings of our study indicate that the proposed model achieves competitive
performance in real-world deployment conditions of smart devices.
| arxiv topic:eess.AS eess.SP |
arxiv_dataset-191102309.06196 | Cookiescanner: An Automated Tool for Detecting and Evaluating GDPR
Consent Notices on Websites
cs.CY
The enforcement of the GDPR led to the widespread adoption of consent
notices, colloquially known as cookie banners. Studies have shown that many
website operators do not comply with the law and track users prior to any
interaction with the consent notice, or attempt to trick users into giving
consent through dark patterns. Previous research has relied on manually curated
filter lists or automated detection methods limited to a subset of websites,
making research on GDPR compliance of consent notices tedious or limited. We
present \emph{cookiescanner}, an automated scanning tool that detects and
extracts consent notices via various methods and checks if they offer a decline
option or use color diversion. We evaluated cookiescanner on a random sample of
the top 10,000 websites listed by Tranco. We found that manually curated filter
lists have the highest precision but recall fewer consent notices than our
keyword-based methods. Our BERT model achieves high precision for English
notices, which is in line with previous work, but suffers from low recall due
to insufficient candidate extraction. While the automated detection of decline
options proved to be challenging due to the dynamic nature of many sites,
detecting instances of different colors of the buttons was successful in most
cases. Besides systematically evaluating our various detection techniques, we
have manually annotated 1,000 websites to provide a ground-truth baseline,
which has not existed previously. Furthermore, we release our code and the
annotated dataset in the interest of reproducibility and repeatability.
| arxiv topic:cs.CY |
arxiv_dataset-191112309.06296 | Holographic Entropy Inequalities and Multipartite Entanglement
hep-th quant-ph
We study holographic entropy inequalities and their structural properties by
making use of a judicious grouping of terms into certain multipartite
information quantities. This allows us to recast cumbersome entropic
expressions into much simpler ones which share interestingly rigid structures.
By performing a systematic search over some of these structures, we are able to
discover more than 300 novel entropy inequalities for six parties, thereby
demonstrating that these recastings provide a fruitful generating technique for
uncovering new holographic entropy inequalities. In attempting to interpret the
corresponding sign-definite quantities as correlation measures, we also obtain
a no-go result: the superbalance property of holographic entropy inequalities
turns out to preclude them from being monotonic under partial tracing. In the
process, we also comment on the geometrical significance of multipartite
information quantities and present various structural relations amongst them.
| arxiv topic:hep-th quant-ph |
arxiv_dataset-191122309.06396 | On extensions of number fields with given quadratic algebras and
cohomology
math.GR math.NT
We introduce a criterion on the presentation of finitely presented pro-$p$
groups which allows us to compute their cohomology groups and infer quotients
of mild groups of cohomological dimension strictly larger than two, from
(non-free) mild groups. We interpret these groups as Galois groups over
$p$-rational fields with prescribed ramification and splitting.
| arxiv topic:math.GR math.NT |
arxiv_dataset-191132309.06496 | Level Up: Private Non-Interactive Decision Tree Evaluation using
Levelled Homomorphic Encryption
cs.CR
As machine learning as a service continues gaining popularity, concerns about
privacy and intellectual property arise. Users often hesitate to disclose their
private information to obtain a service, while service providers aim to protect
their proprietary models. Decision trees, a widely used machine learning model,
are favoured for their simplicity, interpretability, and ease of training. In
this context, Private Decision Tree Evaluation (PDTE) enables a server holding
a private decision tree to provide predictions based on a client's private
attributes. The protocol is such that the server learns nothing about the
client's private attributes. Similarly, the client learns nothing about the
server's model besides the prediction and some hyperparameters.
In this paper, we propose two novel non-interactive PDTE protocols,
XXCMP-PDTE and RCC-PDTE, based on two new non-interactive comparison protocols,
XXCMP and RCC. Our evaluation of these comparison operators demonstrates that
our proposed constructions can efficiently evaluate high-precision numbers.
Specifically, RCC can compare 32-bit numbers in under 10 milliseconds.
We assess our proposed PDTE protocols on decision trees trained over UCI
datasets and compare our results with existing work in the field. Moreover, we
evaluate synthetic decision trees to showcase scalability, revealing that
RCC-PDTE can evaluate a decision tree with over 1000 nodes and 16 bits of
precision in under 2 seconds. In contrast, the current state-of-the-art
requires over 10 seconds to evaluate such a tree with only 11 bits of
precision.
| arxiv topic:cs.CR |
arxiv_dataset-191142309.06596 | Flat band localization in twisted bilayer graphene nanoribbons
cond-mat.mes-hall
We analyze the electronic structure of twisted bilayer graphene (TBG)
nanoribbons close to the magic angle. We describe a transition from an
incomplete to a complete moir\'e structure. By considering zigzag and armchair
edge terminations, the low-energy bands are strongly modified, and thus, the
edge flat band localization is sensitive to the type of boundary. By means of a
scaled tight-binding model, we calculate the band structure and find that, for
an armchair configuration, an incomplete moir\'e edge suppresses the edge
localization, while for a zigzag configuration, we find a strong interference
of the edge states with the moir\'e bands. In particular, for the armchair
termination, we observe a competition between the ribbon periodicity and the
graphene monolayers, which we describe with a potential well toy model.
Furthermore, for ribbons with widths of multiple moir\'e cells, the flat bands
of the moir\'es in the bulk are unperturbed as we change the borders. These
results are explained in terms of the strong electronic localization, nearly
Gaussian, in the AA stacking regions, as confirmed by an inverse participation
ratio analysis. Our results demonstrate that the electronic structure of TBG
nanoribbons is sensitive to the edge termination, offering an explanation for
recent experimental results.
| arxiv topic:cond-mat.mes-hall |
arxiv_dataset-191152309.06696 | Fault-Tolerant Spanners against Bounded-Degree Edge Failures: Linearly
More Faults, Almost For Free
cs.DS
We study a new and stronger notion of fault-tolerant graph structures whose
size bounds depend on the degree of the failing edge set, rather than the total
number of faults. For a subset of faulty edges $F \subseteq G$, the
faulty-degree $\deg(F)$ is the largest number of faults in $F$ incident to any
given vertex. We design new fault-tolerant structures with size comparable to
previous constructions, but which tolerate every fault set of small
faulty-degree $\deg(F)$, rather than only fault sets of small size $|F|$. Our
main results are:
- New FT-Certificates: For every $n$-vertex graph $G$ and degree threshold
$f$, one can compute a connectivity certificate $H \subseteq G$ with $|E(H)| =
\widetilde{O}(fn)$ edges that has the following guarantee: for any edge set $F$
with faulty-degree $\deg(F)\leq f$ and every vertex pair $u,v$, it holds that
$u$ and $v$ are connected in $H \setminus F$ iff they are connected in $G
\setminus F$. This bound on $|E(H)|$ is nearly tight. Since our certificates
handle some fault sets of size up to $|F|=O(fn)$, prior work did not imply any
nontrivial upper bound for this problem, even when $f=1$.
- New FT-Spanners: We show that every $n$-vertex graph $G$ admits a
$(2k-1)$-spanner $H$ with $|E(H)| = O_k(f^{1-1/k} n^{1+1/k})$ edges, which
tolerates any fault set $F$ of faulty-degree at most $f$. This bound on
$|E(H)|$ optimal up to its hidden dependence on $k$, and it is close to the
bound of $O_k(|F|^{1/2} n^{1+1/k} + |F|n)$ that is known for the case where the
total number of faults is $|F|$ [Bodwin, Dinitz, Robelle SODA '22]. Our proof
of this theorem is non-constructive, but by following a proof strategy of
Dinitz and Robelle [PODC '20], we show that the runtime can be made polynomial
by paying an additional $\text{polylog } n$ factor in spanner size.
| arxiv topic:cs.DS |
arxiv_dataset-191162309.06796 | Computing solubility and thermodynamics properties of H2O2 in water
cond-mat.mtrl-sci cond-mat.soft
Hydrogen peroxide plays a key role in many environmental and industrial
chemical processes. We performed classical Molecular Dynamics and Continuous
Fractional Component Monte Carlo simulations to calculate thermodynamic
properties of H2O2 in aqueous solutions. The quality of the available force
fields for H2O2 developed by Orabi & English, and by Cordeiro was
systematically evaluated. To assess which water force field is suitable for
predicting properties of H2O2 in aqueous solutions, four water force fields
were used, namely the TIP3P, TIP4P/2005, TIP5P-E, and a modified TIP3P force
field. While the computed densities of pure H2O2 in the temperature range of
253-353 K using the force field by Orabi & English are in excellent agreement
with experimental results, the densities using the force field by Cordeiro are
underestimated by 3%. The TIP4P/2005 force field in combination with the H2O2
force field developed by Orabi & English can predict the densities of H2O2
aqueous solution for the whole range of H2O2 mole fractions in very good
agreement with experimental results. The TIP4P/2005 force field in combination
with either of the H2O2 force fields can predict the viscosities of H2O2
aqueous solutions for the whole range of H2O2 mole fractions in good agreement
with experimental results. The diffusion coefficients for H2O2 and water
molecules using the TIP4P/2005 force field with either of the H2O2 force fields
are almost constant for the whole range of H2O2 mole fractions. The Cordeiro
force field for H2O2 in combination with either of the water force fields can
predict the Henry coefficients of H2O2 in water in better agreement with
experimental values than the force field by Orabi & English.
| arxiv topic:cond-mat.mtrl-sci cond-mat.soft |
arxiv_dataset-191172309.06896 | Domain-Aware Augmentations for Unsupervised Online General Continual
Learning
cs.LG
Continual Learning has been challenging, especially when dealing with
unsupervised scenarios such as Unsupervised Online General Continual Learning
(UOGCL), where the learning agent has no prior knowledge of class boundaries or
task change information. While previous research has focused on reducing
forgetting in supervised setups, recent studies have shown that self-supervised
learners are more resilient to forgetting. This paper proposes a novel approach
that enhances memory usage for contrastive learning in UOGCL by defining and
using stream-dependent data augmentations together with some implementation
tricks. Our proposed method is simple yet effective, achieves state-of-the-art
results compared to other unsupervised approaches in all considered setups, and
reduces the gap between supervised and unsupervised continual learning. Our
domain-aware augmentation procedure can be adapted to other replay-based
methods, making it a promising strategy for continual learning.
| arxiv topic:cs.LG |
arxiv_dataset-191182309.06996 | Dynamics Reflects Quantum Phase Transition of Rabi Model
quant-ph
As the simplest and most fundamental model describing the interaction between
light and matter, a breakdown in the rotating wave approximation of the Rabi
model leads to phase transition versus coupling strength when the frequency of
the qubit greatly surpasses that of the oscillator. Besides the phase
transition revealed in the ground state, we show that the dynamics of physical
quantities can reflect such a phase transition for this model. In addition to
the excitation of the bosonic field in the ground state, we show that the
witness of inseparability (entanglement), mutual information, quantum Fisher
information, and the variance of cavity quadrature can be employed to detect
the phase transition in quench. We also reveal the negative impact of
temperature on checking the phase transition by quench. This model can be
implemented using trapped ions, superconducting artificial atoms coupled
bosonic modes, and quantum simulations. By reflecting the phase transition in a
fundamental quantum optics model without imposing the thermodynamic limit, this
work offers an idea to explore phase transitions by non-equilibrium process for
open quantum systems.
| arxiv topic:quant-ph |
arxiv_dataset-191192309.07096 | Computational limits to the legibility of the imaged human brain
q-bio.NC cs.CV eess.IV
Our knowledge of the organisation of the human brain at the population-level
is yet to translate into power to predict functional differences at the
individual-level, limiting clinical applications, and casting doubt on the
generalisability of inferred mechanisms. It remains unknown whether the
difficulty arises from the absence of individuating biological patterns within
the brain, or from limited power to access them with the models and compute at
our disposal. Here we comprehensively investigate the resolvability of such
patterns with data and compute at unprecedented scale. Across 23 810 unique
participants from UK Biobank, we systematically evaluate the predictability of
25 individual biological characteristics, from all available combinations of
structural and functional neuroimaging data. Over 4526 GPU hours of
computation, we train, optimize, and evaluate out-of-sample 700 individual
predictive models, including fully-connected feed-forward neural networks of
demographic, psychological, serological, chronic disease, and functional
connectivity characteristics, and both uni- and multi-modal 3D convolutional
neural network models of macro- and micro-structural brain imaging. We find a
marked discrepancy between the high predictability of sex (balanced accuracy
99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean
absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art
performance, and the surprisingly low predictability of other characteristics.
Neither structural nor functional imaging predicted psychology better than the
coincidence of chronic disease (p<0.05). Serology predicted chronic disease
(p<0.05) and was best predicted by it (p<0.001), followed by structural
neuroimaging (p<0.05). Our findings suggest either more informative imaging or
more powerful models are needed to decipher individual level characteristics
from the human brain.
| arxiv topic:q-bio.NC cs.CV eess.IV |
arxiv_dataset-191202309.07196 | Attention-based Dynamic Graph Convolutional Recurrent Neural Network for
Traffic Flow Prediction in Highway Transportation
cs.LG cs.AI cs.GR
As one of the important tools for spatial feature extraction, graph
convolution has been applied in a wide range of fields such as traffic flow
prediction. However, current popular works of graph convolution cannot
guarantee spatio-temporal consistency in a long period. The ignorance of
correlational dynamics, convolutional locality and temporal comprehensiveness
would limit predictive accuracy. In this paper, a novel Attention-based Dynamic
Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve
traffic flow prediction in highway transportation. Three temporal resolutions
of data sequence are effectively integrated by self-attention to extract
characteristics; multi-dynamic graphs and their weights are dynamically created
to compliantly combine the varying characteristics; a dedicated gated kernel
emphasizing highly relative nodes is introduced on these complete graphs to
reduce overfitting for graph convolution operations. Experiments on two public
datasets show our work better than state-of-the-art baselines, and case studies
of a real Web system prove practical benefit in highway transportation.
| arxiv topic:cs.LG cs.AI cs.GR |
arxiv_dataset-191212309.07296 | Beamforming Design and Performance Evaluation for RIS-aided Localization
using LEO Satellite Signals
eess.SP
The growing availability of low-Earth orbit (LEO) satellites, coupled with
the anticipated widespread deployment of reconfigurable intelligent surfaces
(RISs), opens up promising prospects for new localization paradigms. This paper
studies RIS-aided localization using LEO satellite signals. The Cram\'er-Rao
bound of the considered localization problem is derived, based on which an
optimal RIS beamforming design that minimizes the derived bound is proposed.
Numerical results demonstrate the superiority of the proposed beamforming
scheme over benchmark alternatives, while also revealing that the synergy
between LEO satellites and RISs holds the promise of achieving localization
accuracy at the meter or even sub-meter level.
| arxiv topic:eess.SP |
arxiv_dataset-191222309.07396 | DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning
in the Debiasing Perspective
cs.CL cs.AI
Several prior studies have suggested that word frequency biases can cause the
Bert model to learn indistinguishable sentence embeddings. Contrastive learning
schemes such as SimCSE and ConSERT have already been adopted successfully in
unsupervised sentence embedding to improve the quality of embeddings by
reducing this bias. However, these methods still introduce new biases such as
sentence length bias and false negative sample bias, that hinders model's
ability to learn more fine-grained semantics. In this paper, we reexamine the
challenges of contrastive sentence embedding learning from a debiasing
perspective and argue that effectively eliminating the influence of various
biases is crucial for learning high-quality sentence embeddings. We think all
those biases are introduced by simple rules for constructing training data in
contrastive learning and the key for contrastive learning sentence embedding is
to mimic the distribution of training data in supervised machine learning in
unsupervised way. We propose a novel contrastive framework for sentence
embedding, termed DebCSE, which can eliminate the impact of these biases by an
inverse propensity weighted sampling method to select high-quality positive and
negative pairs according to both the surface and semantic similarity between
sentences. Extensive experiments on semantic textual similarity (STS)
benchmarks reveal that DebCSE significantly outperforms the latest
state-of-the-art models with an average Spearman's correlation coefficient of
80.33% on BERTbase.
| arxiv topic:cs.CL cs.AI |
arxiv_dataset-191232309.07496 | Comparison of Middlewares in Edge-to-Edge and Edge-to-Cloud
Communication for Distributed ROS2 Systems
cs.RO
The increased data transmission and number of devices involved in
communications among distributed systems make it challenging yet significantly
necessary to have an efficient and reliable networking middleware. In robotics
and autonomous systems, the wide application of ROS\,2 brings the possibility
of utilizing various networking middlewares together with DDS in ROS\,2 for
better communication among edge devices or between edge devices and the cloud.
However, there is a lack of comprehensive communication performance comparison
of integrating these networking middlewares with ROS\,2. In this study, we
provide a quantitative analysis for the communication performance of utilized
networking middlewares including MQTT and Zenoh alongside DDS in ROS\,2 among a
multiple host system. For a complete and reliable comparison, we calculate the
latency and throughput of these middlewares by sending distinct amounts and
types of data through different network setups including Ethernet, Wi-Fi, and
4G. To further extend the evaluation to real-world application scenarios, we
assess the drift error (the position changes) over time caused by these
networking middlewares with the robot moving in an identical square-shaped
path. Our results show that CycloneDDS performs better under Ethernet while
Zenoh performs better under Wi-Fi and 4G. In the actual robot test, the robot
moving trajectory drift error over time (96\,s) via Zenoh is the smallest. It
is worth noting we have a discussion of the CPU utilization of these networking
middlewares and the performance impact caused by enabling the security feature
in ROS\,2 at the end of the paper.
| arxiv topic:cs.RO |
arxiv_dataset-191242309.07596 | Quantum toroidal algebras and solvable structures in gauge/string theory
hep-th math-ph math.MP math.QA math.RT
This is a review article on the quantum toroidal algebras, focusing on their
roles in various solvable structures of 2d conformal field theory,
supersymmetric gauge theory, and string theory. Using $\mathcal{W}$-algebras as
our starting point, we elucidate the interconnection of affine Yangians,
quantum toroidal algebras, and double affine Hecke algebras.
Our exploration delves into the representation theory of the quantum toroidal
algebra of $\mathfrak{gl}_1$ in full detail, highlighting its connections to
partitions, $\mathcal{W}$-algebras, Macdonald functions, and the notion of
intertwiners. Further, we also discuss integrable models constructed on Fock
spaces and associated $\mathcal{R}$-matrices, both for the affine Yangian and
the quantum toroidal algebra of $\mathfrak{gl}_1$.
The article then demonstrates how quantum toroidal algebras serve as a
unifying algebraic framework that bridges different areas in physics. Notably,
we cover topological string theory and supersymmetric gauge theories with eight
supercharges, incorporating the AGT duality. Drawing upon the representation
theory of the quantum toroidal algebra of $\mathfrak{gl}_1$, we provide a
rather detailed review of its role in the algebraic formulations of topological
vertex and $qq$-characters. Additionally, we briefly touch upon the corner
vertex operator algebras and quiver quantum toroidal algebras.
| arxiv topic:hep-th math-ph math.MP math.QA math.RT |
arxiv_dataset-191252309.07696 | Steady-state entanglement production in a quantum thermal machine with
continuous feedback control
quant-ph
Quantum thermal machines can generate steady-state entanglement by harvesting
spontaneous interactions with local environments. However, using minimal
resources and control, the entanglement is typically very noisy. Here, we study
entanglement generation in a two-qubit quantum thermal machine in the presence
of a continuous feedback protocol. Each qubit is measured continuously and the
outcomes are used for real-time feedback to control the local
system-environment interactions. We show that there exists an ideal operation
regime where the quality of entanglement is significantly improved, to the
extent that it can violate standard Bell inequalities and uphold quantum
teleportation. In particular, we find, for ideal operation, that the heat
current across the system is proportional to the entanglement concurrence.
Finally, we investigate the robustness of entanglement production when the
machine operates away from the ideal conditions.
| arxiv topic:quant-ph |
arxiv_dataset-191262309.07796 | For A More Comprehensive Evaluation of 6DoF Object Pose Tracking
cs.CV
Previous evaluations on 6DoF object pose tracking have presented obvious
limitations along with the development of this area. In particular, the
evaluation protocols are not unified for different methods, the widely-used
YCBV dataset contains significant annotation error, and the error metrics also
may be biased. As a result, it is hard to fairly compare the methods, which has
became a big obstacle for developing new algorithms. In this paper we
contribute a unified benchmark to address the above problems. For more accurate
annotation of YCBV, we propose a multi-view multi-object global pose refinement
method, which can jointly refine the poses of all objects and view cameras,
resulting in sub-pixel sub-millimeter alignment errors. The limitations of
previous scoring methods and error metrics are analyzed, based on which we
introduce our improved evaluation methods. The unified benchmark takes both
YCBV and BCOT as base datasets, which are shown to be complementary in scene
categories. In experiments, we validate the precision and reliability of the
proposed global pose refinement method with a realistic semi-synthesized
dataset particularly for YCBV, and then present the benchmark results unifying
learning&non-learning and RGB&RGBD methods, with some finds not discovered in
previous studies.
| arxiv topic:cs.CV |
arxiv_dataset-191272309.07896 | Dark Matter Direct Detection in $t$-channel mediator models
hep-ph
We perform a comprehensive study of the Direct Detection phenomenology of
singlet Dark Matter $t$-channel portal models. For that purpose, we present a
complete one-loop matching onto a Heavy Dark-Matter Effective Field Theory,
leading to a complete computation of the loop induced direct detection
cross-section for both scalar and fermionic Dark Matter candidates. The results
are compared with current and future bounds from Direct Detection experiments,
as well as with the requirement of the correct Dark Matter relic density.
| arxiv topic:hep-ph |
arxiv_dataset-191282309.07996 | Efficient online update of model predictive control in embedded systems
using first-order methods
eess.SY cs.SY math.OC
Model Predictive Control (MPC) is typically characterized for being
computationally demanding, as it requires solving optimization problems online;
a particularly relevant point when considering its implementation in embedded
systems. To reduce the computational burden of the optimization algorithm, most
solvers perform as many offline operations as possible, typically performing
the computation and factorization of its expensive matrices offline and then
storing them in the embedded system. This improves the efficiency of the
solver, with the disadvantage that online changes on some of the ingredients of
the MPC formulation require performing these expensive computations online.
This article presents an efficient algorithm for the factorization of the key
matrix used in several first-order optimization methods applied to linear MPC
formulations, allowing its prediction model and cost function matrices to be
updated online at the expense of a small computational cost. We show results
comparing the proposed approach with other solvers from the literature applied
to a linear time-varying system.
| arxiv topic:eess.SY cs.SY math.OC |
arxiv_dataset-191292309.08096 | GelSplitter: Tactile Reconstruction from Near Infrared and Visible
Images
cs.RO
The GelSight-like visual tactile (VT) sensor has gained popularity as a
high-resolution tactile sensing technology for robots, capable of measuring
touch geometry using a single RGB camera. However, the development of
multi-modal perception for VT sensors remains a challenge, limited by the mono
camera. In this paper, we propose the GelSplitter, a new framework approach the
multi-modal VT sensor with synchronized multi-modal cameras and resemble a more
human-like tactile receptor. Furthermore, we focus on 3D tactile reconstruction
and implement a compact sensor structure that maintains a comparable size to
state-of-the-art VT sensors, even with the addition of a prism and a near
infrared (NIR) camera. We also design a photometric fusion stereo neural
network (PFSNN), which estimates surface normals of objects and reconstructs
touch geometry from both infrared and visible images. Our results demonstrate
that the accuracy of RGB and NIR fusion is higher than that of RGB images
alone. Additionally, our GelSplitter framework allows for a flexible
configuration of different camera sensor combinations, such as RGB and thermal
imaging.
| arxiv topic:cs.RO |
arxiv_dataset-191302309.08196 | ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
cs.CV
Few-shot object detection (FSOD) identifies objects from extremely few
annotated samples. Most existing FSOD methods, recently, apply the two-stage
learning paradigm, which transfers the knowledge learned from abundant base
classes to assist the few-shot detectors by learning the global features.
However, such existing FSOD approaches seldom consider the localization of
objects from local to global. Limited by the scarce training data in FSOD, the
training samples of novel classes typically capture part of objects, resulting
in such FSOD methods cannot detect the completely unseen object during testing.
To tackle this problem, we propose an Extensible Co-Existing Attention (ECEA)
module to enable the model to infer the global object according to the local
parts. Essentially, the proposed module continuously learns the extensible
ability on the base stage with abundant samples and transfers it to the novel
stage, which can assist the few-shot model to quickly adapt in extending local
regions to co-existing regions. Specifically, we first devise an extensible
attention mechanism that starts with a local region and extends attention to
co-existing regions that are similar and adjacent to the given local region. We
then implement the extensible attention mechanism in different feature scales
to progressively discover the full object in various receptive fields.
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA
module can assist the few-shot detector to completely predict the object
despite some regions failing to appear in the training samples and achieve the
new state of the art compared with existing FSOD methods.
| arxiv topic:cs.CV |
arxiv_dataset-191312309.08296 | Exploring jet transport coefficients by elastic and radiative
scatterings in the strongly interacting quark-gluon plasma
hep-ph nucl-th
We investigate the interaction of leading jet partons within a strongly
interacting quark-gluon plasma (sQGP) medium, using the effective dynamical
quasiparticle model (DQPM). The DQPM offers a description of the sQGP's
non-perturbative nature at finite temperature $T$ and baryon chemical potential
$\mu_B$ through a propagator representation of massive off-shell partons
(quarks and gluons). These partons are characterized by spectral functions with
$T,\mu_B$ dependent masses and widths, adjusted to reproduce the lattice
Quantum Chromodynamics (lQCD) equation-of-state (EoS) for the QGP in
thermodynamic equilibrium. Our focus lies on examining the jet transport
coefficients by elastic scattering in sQGP, specifically the transverse
momentum transfer squared per unit length denoted as $\hat{q}$, within the QGP.
Furthermore, we investigate the dependence of these coefficients on both the
medium temperature $T$ and the jet parton energy. By studying the jet transport
coefficients and their relationship to temperature and parton energy, we aim to
gain insights into the dynamics of jet propagation in the strongly interacting
quark-gluon plasma medium.
| arxiv topic:hep-ph nucl-th |
arxiv_dataset-191322309.08396 | Resource Optimization Using A Step-by-step Scheme in Wireless Sensing
and Localization Networks
cs.IT math.IT
Due to the lack of wireless spectrum resources, people are focusing on the
versatile wireless networks. Wireless localization and target sensing both rely
on precise extraction of parameters such as signal amplitude, propagation delay
and Doppler shift from the received signals. Due to the high multi-path
resolution and strong penetration of UWB signals, both localization and sensing
can be achieved through the same UWB waveform. Practical networks are often
resource-constrained, in order to improve the accuracy of integrated networks,
we need to optimize the allocation of resources in the networks. Considering
the complexity of the multi-slot networks, this paper derives the Fisher
Information Matrix (FIM) expressions for single-slot and dual-slot integrated
sensing and localization (ISAL) networks respectively, and proposes two
resource optimization schemes, namely step-by-step scheme and integrated
scheme. The numerical results show that: (i) for the sensing-resource-deficient
networks with relatively uniform node distribution, the energy allocated to
each step in the step-by-step scheme satisfies the relationship: energy for
clock offset < energy for radar localization < energy for target sensing. (ii)
In the multi-slot ISAL networks, the system will allocate more energy to the
time slots where the networks are relatively sensing-resource-deficient. (iii)
The step-by-step scheme is more suitable for the sensing-resource-abundant
networks, while the integrated scheme is more suitable for the
sensing-resource-deficient networks.
| arxiv topic:cs.IT math.IT |
arxiv_dataset-191332309.08496 | Particle Dynamics on Test Papapetrou Fields of Vacuum Spacetimes
gr-qc
In this paper we examine the dynamics of particles subjected to test
Papapetrou fields of vacuum spacetimes. The staring point of our analysis is
based on fundamental electrodynamics which emerge from spacetime isometries of
a Kerr and Schwarzschild black holes. Taking into account Killing vectors which
satisfy Maxwell equations we evaluate the corresponding electric and magnetic
fields -- Papapetrou fields -- by fixing proper frames of reference in each
spacetime. A timelike observer is considered for the case of a Schwarzschild
spacetime while a locally non-rotating (LNR) frame of reference is fixed for a
Kerr black hole. In order to probe for the effect of such electromagnetic
fields we study the motion of charged test particles in the equatorial plane of
both spacetimes. For the case of a Schwarzschild black hole we show that
massive/charged test particles may populate the unstable photon sphere for a
given domain of the parametric space. Restricting ourselves to orbits with LNR
initial conditions for the case of a Kerr black hole we show that there is an
explicit deviation between orbits of neutral and charged particles in the case
of repulsive configurations. For critical charge-mass ratios $\zeta_*$ test
particles can be found in the Kerr retrograde photon sphere thus assigning a
physical signature to Papapetrou fields.
| arxiv topic:gr-qc |
arxiv_dataset-191342309.08596 | Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion
cs.CV cs.GR cs.RO
Event cameras offer many advantages over standard cameras due to their
distinctive principle of operation: low power, low latency, high temporal
resolution and high dynamic range. Nonetheless, the success of many downstream
visual applications also hinges on an efficient and effective scene
representation, where Neural Radiance Field (NeRF) is seen as the leading
candidate. Such promise and potential of event cameras and NeRF inspired recent
works to investigate on the reconstruction of NeRF from moving event cameras.
However, these works are mainly limited in terms of the dependence on dense and
low-noise event streams, as well as generalization to arbitrary contrast
threshold values and camera speed profiles. In this work, we propose Robust
e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving
event cameras under various real-world conditions, especially from sparse and
noisy events generated under non-uniform motion. It consists of two key
components: a realistic event generation model that accounts for various
intrinsic parameters (e.g. time-independent, asymmetric threshold and
refractory period) and non-idealities (e.g. pixel-to-pixel threshold
variation), as well as a complementary pair of normalized reconstruction losses
that can effectively generalize to arbitrary speed profiles and intrinsic
parameter values without such prior knowledge. Experiments on real and novel
realistically simulated sequences verify our effectiveness. Our code, synthetic
dataset and improved event simulator are public.
| arxiv topic:cs.CV cs.GR cs.RO |
arxiv_dataset-191352309.08696 | RIFL: A Reliable Link Layer Network Protocol for Data Center
Communication
cs.NI
More and more latency-sensitive services and applications are being deployed
into the data center. Performance can be limited by the high latency of the
network interconnect. Because the conventional network stack is designed not
only for LAN, but also for WAN, it carries a great amount of redundancy that is
not required in a data center network. This paper introduces the concept of a
three-layer protocol stack that can fulfill the exact demands of data center
network communications. The detailed design and implementation of the first
layer of the stack, which we call RIFL, is presented. A novel low latency
in-band hop-by-hop re-transmission protocol is proposed and adopted in RIFL,
which guarantees lossless transmission in a data center environment.
Experimental results show that RIFL achieves 110 nanoseconds point-to-point
latency on 10-meter Active Optical Cables, at a line rate of 112 Gbps. RIFL is
a multi-lane protocol with scalable throughput up to multi-hundred gigabits per
second. It can be the enabler of low latency, high throughput, flexible,
scalable, and lossless data center networks.
| arxiv topic:cs.NI |
arxiv_dataset-191362309.08796 | Towards Robust and Efficient Communications for Urban Air Mobility
eess.SP
For the realization of the future urban air mobility, reliable information
exchange based on robust and efficient communication between all airspace
participants will be one of the key factors to ensure safe operations.
Especially in dense urban scenarios, the direct and fast information exchange
between drones based on Drone-to-Drone communications is a promising technology
for enabling reliable collision avoidance systems. However, to mitigate
collisions and to increase overall reliability, unmanned aircraft still lack a
redundant, higher-level safety net to coordinate and monitor traffic, as is
common in today's civil aviation. In addition, direct and fast information
exchange based on ad hoc communication is needed to cope with the very short
reaction times required to avoid collisions and to cope with the the high
traffic densities. Therefore, we are developing a \ac{d2d} communication and
surveillance system, called DroneCAST, which is specifically tailored to the
requirements of a future urban airspace and will be part of a multi-link
approach. In this work we discuss challenges and expected safety-critical
applications that will have to rely on communications for \ac{uam} and present
our communication concept and necessary steps towards DroneCAST. As a first
step towards an implementation, we equipped two drones with hardware prototypes
of the experimental communication system and performed several flights around
the model city to evaluate the performance of the hardware and to demonstrate
different applications that will rely on robust and efficient communications.
| arxiv topic:eess.SP |
arxiv_dataset-191372309.08896 | Graph-based Decentralized Task Allocation for Multi-Robot Target
Localization
cs.RO cs.MA
We introduce a new approach to address the task allocation problem in a
system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs)
and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or
\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}
aggregates information from neighbors in the multi-robot system, with the aim
of achieving joint optimality in the target localization efficiency.Being
decentralized, our method is highly robust and adaptable to situations where
collaborators may change over time, ensuring the continuity of the mission. We
also proposed heterogeneity-aware preprocessing to let all the different types
of robots collaborate with a uniform model.The experimental results demonstrate
the effectiveness and scalability of the proposed approach in a range of
simulated scenarios. The model can allocate targets' positions close to the
expert algorithm's result, with a median spatial gap less than a unit length.
This approach can be used in multi-robot systems deployed in search and rescue
missions, environmental monitoring, and disaster response.
| arxiv topic:cs.RO cs.MA |
arxiv_dataset-191382309.08996 | An analogue of Ramanujan's identity for Bernoulli-Carlitz numbers
math.NT math.CA
In his second notebook, Ramanujan discovered the following identity for the
special values of $\zeta(s)$ at the odd positive integers
\begin{equation*}\begin{aligned}\alpha^{-m}\,\left\{\dfrac{1}{2}\,\zeta(2m + 1)
+ \sum_{n = 1}^{\infty}\dfrac{n^{-2m - 1}}{e^{2\alpha n} - 1}\right\} &-(-
\beta)^{-m}\,\left\{\dfrac{1}{2}\,\zeta(2m + 1) + \sum_{n =
1}^{\infty}\dfrac{n^{-2m - 1}}{e^{2\beta n} - 1}\right\}\nonumber
&=2^{2m}\sum_{k = 0}^{m + 1}\dfrac{\left(-1\right)^{k-1}B_{2k}\,B_{2m -
2k+2}}{\left(2k\right)!\left(2m -2k+2\right)!}\,\alpha^{m - k + 1}\beta^k
\label{(1.2)},\end{aligned} \end{equation*} where $ \alpha $ and $ \beta $ are
positive numbers such that $ \alpha\beta = \pi^2 $ and $ m $ is a positive
integer. As shown by Berndt in the viewpoint of general transformation of
analytic Eisenstein series, it is a natural companion of Euler's famous formula
for even zeta values. In this note, we prove an analogue of the above
Ramanujan's identity in the functions fields setting, which involves the
Bernoulli-Carlitz numbers.
| arxiv topic:math.NT math.CA |
arxiv_dataset-191392309.09096 | On $p$-nonsingular systems of equations over solvable groups
math.GR
Any group that has a subnormal series, in which all factors are abelian and
all except the last one are $p'$-torsion-free, can be embedded into a group
with a subnormal series of the same length, with the same properties and such
that any $p$-nonsingular system of equations over this group is solvable in
this group itself. This helps us to prove that the minimal order of a
metabelian group, over which there is a unimodular equation that is unsolvable
in metabelian groups, is 42.
| arxiv topic:math.GR |
arxiv_dataset-191402309.09196 | Efficient Pyramid Channel Attention Network for Pathological Myopia
Recognition
cs.CV
Pathological myopia (PM) is the leading ocular disease for impaired vision
worldwide. Clinically, the characteristic of pathology distribution in PM is
global-local on the fundus image, which plays a significant role in assisting
clinicians in diagnosing PM. However, most existing deep neural networks
focused on designing complex architectures but rarely explored the pathology
distribution prior of PM. To tackle this issue, we propose an efficient pyramid
channel attention (EPCA) module, which fully leverages the potential of the
clinical pathology prior of PM with pyramid pooling and multi-scale context
fusion. Then, we construct EPCA-Net for automatic PM recognition based on
fundus images by stacking a sequence of EPCA modules. Moreover, motivated by
the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained
natural image models for PM recognition by freezing them and treating the EPCA
and other attention modules as adapters. In addition, we construct a PM
recognition benchmark termed PM-fundus by collecting fundus images of PM from
publicly available datasets. The comprehensive experiments demonstrate the
superiority of our EPCA-Net over state-of-the-art methods in the PM recognition
task. The results also show that our method based on the
pretraining-and-finetuning paradigm achieves competitive performance through
comparisons to part of previous methods based on traditional fine-tuning
paradigm with fewer tunable parameters, which has the potential to leverage
more natural image foundation models to address the PM recognition task in
limited medical data regime.
| arxiv topic:cs.CV |
arxiv_dataset-191412309.09296 | Model-based Subsampling for Knowledge Graph Completion
cs.CL cs.AI cs.LG
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing
overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However,
current subsampling approaches consider only frequencies of queries that
consist of entities and their relations. Thus, the existing subsampling
potentially underestimates the appearance probabilities of infrequent queries
even if the frequencies of their entities or relations are high. To address
this problem, we propose Model-based Subsampling (MBS) and Mixed Subsampling
(MIX) to estimate their appearance probabilities through predictions of KGE
models. Evaluation results on datasets FB15k-237, WN18RR, and YAGO3-10 showed
that our proposed subsampling methods actually improved the KG completion
performances for popular KGE models, RotatE, TransE, HAKE, ComplEx, and
DistMult.
| arxiv topic:cs.CL cs.AI cs.LG |
arxiv_dataset-191422309.09396 | Optimality Conditions for Interval-Valued Optimization Problems on
Riemannian Manifolds Under a Total Order Relation
math.OC math.DG
This article explores fundamental properties of convex interval-valued
functions defined on Riemannian manifolds. The study employs generalized
Hukuhara directional differentiability to derive KKT-type optimality conditions
for an interval-valued optimization problem on Riemannian manifolds. Based on
type of functions involved in optimization problems, we consider the following
cases:
1. objective function as well as constraints are real-valued;
2. objective function is interval-valued, and constraints are real-valued;
3. objective function as well as constraints are interval-valued.
The whole theory is justified with the help of examples. The order relation
that we use throughout the paper is a total order relation defined on the
collection of all closed and bounded intervals in $\mathbb{R}$.
| arxiv topic:math.OC math.DG |
arxiv_dataset-191432309.09496 | CLIP-based Synergistic Knowledge Transfer for Text-based Person
Retrieval
cs.CV cs.AI
Text-based Person Retrieval (TPR) aims to retrieve the target person images
given a textual query. The primary challenge lies in bridging the substantial
gap between vision and language modalities, especially when dealing with
limited large-scale datasets. In this paper, we introduce a CLIP-based
Synergistic Knowledge Transfer (CSKT) approach for TPR. Specifically, to
explore the CLIP's knowledge on input side, we first propose a Bidirectional
Prompts Transferring (BPT) module constructed by text-to-image and
image-to-text bidirectional prompts and coupling projections. Secondly, Dual
Adapters Transferring (DAT) is designed to transfer knowledge on output side of
Multi-Head Attention (MHA) in vision and language. This synergistic two-way
collaborative mechanism promotes the early-stage feature fusion and efficiently
exploits the existing knowledge of CLIP. CSKT outperforms the state-of-the-art
approaches across three benchmark datasets when the training parameters merely
account for 7.4% of the entire model, demonstrating its remarkable efficiency,
effectiveness and generalization.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-191442309.09596 | Self-morphing of elastic bilayers induced by mismatch strain:
deformation simulation and bio-inspired design
math.NA cs.NA
The process of self-morphing in curved surfaces found in nature, such as with
the growth of flowers and leaves, has generated interest in the study of
self-morphing bilayers, which has been used in many soft robots or switchers.
However, previous research has primarily focused on materials or bilayer
fabrication technologies. The self-morphing mechanism and process have been
rarely investigated, despite their importance. This study proposed a new
deformation simulation method for self-morphing bilayers based on a
checkerboard-based discrete differential geometry approach. This new method
achieved higher efficiency than traditional finite element methods while still
maintaining accuracy. It was also effective in handling complex finite strain
situations. Finally, the simulation model was used to design three
self-morphing bilayers inspired by folding flowers, spiral grass, and conical
seashells. These designs further prove the effectiveness of the proposed
method. The results of this study propose a good method for predicting
deformation and designing self-morphing bilayers and provide a useful viewpoint
for using geometrical methods to solve mechanical problems.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-191452309.09696 | GHNet:Learning GNSS Heading from Velocity Measurements
cs.RO
By utilizing global navigation satellite system (GNSS) position and velocity
measurements, the fusion between the GNSS and the inertial navigation system
provides accurate and robust navigation information. When considering land
vehicles,like autonomous ground vehicles,off-road vehicles or mobile robots,a
GNSS-based heading angle measurement can be obtained and used in parallel to
the position measurement to bound the heading angle drift. Yet, at low vehicle
speeds (less than 2m/s) such a model-based heading measurement fails to provide
satisfactory performance. This paper proposes GHNet, a deep-learning framework
capable of accurately regressing the heading angle for vehicles operating at
low speeds. We demonstrate that GHNet outperforms the current model-based
approach for simulation and experimental datasets.
| arxiv topic:cs.RO |
arxiv_dataset-191462309.09796 | On a conjecture of Ram\'{\i}rez Alfons\'{\i}n and Ska{\l}ba II
math.NT
Let $1<c<d$ be two relatively prime integers and $g_{c,d}=cd-c-d$. We
confirm, by employing the Hardy--Littlewood method, a 2020 conjecture of
Ram\'{\i}rez Alfons\'{\i}n and Ska{\l}ba which states that $$#\left\{p\le
g_{c,d}:p\in \mathcal{P}, ~p=cx+dy,~x,y\in \mathbb{Z}_{\geqslant0}\right\}\sim
\frac{1}{2}\pi\left(g_{c,d}\right) \quad (\text{as}~c\rightarrow\infty),$$
where $\mathcal{P}$ is the set of primes, $\mathbb{Z}_{\geqslant0}$ is the set
of nonnegative integers and $\pi(t)$ denotes the number of primes not exceeding
$t$.
| arxiv topic:math.NT |
arxiv_dataset-191472309.09896 | Existence and Morse Index of two free boundary embedded geodesics on
Riemannian 2-disks with convex boundary
math.DG math.AP
We prove that a free boundary curve shortening flow on closed surfaces with a
strictly convex boundary remains noncollapsed for a finite time in the sense of
the reflected chord-arc profile introduced by Langford-Zhu. This shows that
such flow converges to free boundary embedded geodesic in infinite time, or
shrinks to a round half-point on the boundary. As a consequence, we prove the
existence of two free boundary embedded geodesics on a Riemannian $2$-disk with
a strictly convex boundary. Moreover, we prove that there exists a simple
closed geodesic with Morse Index $1$ and $2$. This settles the free boundary
analog of Grayson's theorem.
| arxiv topic:math.DG math.AP |
arxiv_dataset-191482309.09996 | Improving Speech Recognition for African American English With Audio
Classification
eess.AS cs.CL cs.LG cs.SD
Automatic speech recognition (ASR) systems have been shown to have large
quality disparities between the language varieties they are intended or
expected to recognize. One way to mitigate this is to train or fine-tune models
with more representative datasets. But this approach can be hindered by limited
in-domain data for training and evaluation. We propose a new way to improve the
robustness of a US English short-form speech recognizer using a small amount of
out-of-domain (long-form) African American English (AAE) data. We use CORAAL,
YouTube and Mozilla Common Voice to train an audio classifier to approximately
output whether an utterance is AAE or some other variety including Mainstream
American English (MAE). By combining the classifier output with coarse
geographic information, we can select a subset of utterances from a large
corpus of untranscribed short-form queries for semi-supervised learning at
scale. Fine-tuning on this data results in a 38.5% relative word error rate
disparity reduction between AAE and MAE without reducing MAE quality.
| arxiv topic:eess.AS cs.CL cs.LG cs.SD |
arxiv_dataset-191492309.10096 | Tracing the Galactic Disk with Planetary Nebulae using Gaia DR3
astro-ph.GA
We study the population of Galactic planetary Nebulae (PNe) and their central
stars (CSs) through the analysis of their heliocentric distances and Galactic
distribution. Distances are obtained by means of a revised statistical scale,
based on an astrometrically-defined sample of CSs parallaxes from Gaia DR3 as
calibrators. The statistical scale is applied to infer distances of a
significant number (~850) of Galactic PNe, for which we deliver a new catalog
of PN distances. By adopting a circular velocity curve of the Galaxy, we also
derive 3D peculiar velocities from DR3 proper motions and published radial
velocities of a large sample (~300) of PN CSs. We date PN progenitors based
both on the best chemical abundances culled from the literature and on CS
kinematic properties, finding a confirmation of the first method with the
second. The slope of the radial oxygen gradient of the Galactic Disk traced by
the complete PNe sample amounts to -0.0144 +/- 0.00385 [dex/kpc]. Furthermore,
by distinguishing between PNe with old (> 7.5 Gyr) and young (< 1 Gyr)
progenitors, we estimate the gradient to be respectively -0.0121 +/- 0.00465
and -0.022 +/- 0.00758 [dex/kpc], thus disclosing a mild steepening since
Galaxy formation, with a slope change of 0.01 dex. These results are in broad
agreement with previous PN studies, but now based on DR3 Gaia analysis, and
also in agreement with what traced by most other Galactic probes.
| arxiv topic:astro-ph.GA |
arxiv_dataset-191502309.10196 | On the Minimum Distance, Minimum Weight Codewords, and the Dimension of
Projective Reed-Muller Codes
cs.IT math.CO math.IT
We give an alternative proof of the formula for the minimum distance of a
projective Reed-Muller code of an arbitrary order. It leads to a complete
characterization of the minimum weight codewords of a projective Reed-Muller
code. This is then used to determine the number of minimum weight codewords of
a projective Reed-Muller code. Various formulas for the dimension of a
projective Reed-Muller code, and their equivalences are also discussed.
| arxiv topic:cs.IT math.CO math.IT |
arxiv_dataset-191512309.10296 | Minimum-length chain embedding for the phase unwrapping problem on
D-Wave's advantage architecture
eess.IV
With the current progress of quantum computing, quantum annealing is being
introduced as a powerful method to solve hard computational problems. In this
paper, we study the potential capability of quantum annealing in solving the
phase unwrapping problem, an instance of hard computational problems. To solve
the phase unwrapping problem using quantum annealing, we deploy the D-Wave
Advantage machine which is currently the largest available quantum annealer.
The structure of this machine, however, is not compatible with our problem
graph structure. Consequently, the problem graph needs to be mapped onto the
target (Pegasus) graph, and this embedding significantly affects the quality of
the results. Based on our experiment and also D-Wave's reports, the lower chain
lengths can result in a better performance of quantum annealing. In this paper,
we propose a new embedding algorithm that has the lowest possible chain length
for embedding the graph of the phase unwrapping problem onto the Pegasus graph.
The obtained results using this embedding strongly outperform the results of
Auto-embedding provided by D-Wave. Besides the phase unwrapping problem, this
embedding can be used to embed any subset of our problem graph to the Pegasus
graph.
| arxiv topic:eess.IV |
arxiv_dataset-191522309.10396 | Poster: Control-Flow Integrity in Low-end Embedded Devices
cs.CR
Embedded, smart, and IoT devices are increasingly popular in numerous
everyday settings. Since lower-end devices have the most strict cost
constraints, they tend to have few, if any, security features. This makes them
attractive targets for exploits and malware. Prior research proposed various
security architectures for enforcing security properties for
resource-constrained devices, e.g., via Remote Attestation (RA). Such
techniques can (statically) verify software integrity of a remote device and
detect compromise. However, run-time (dynamic) security, e.g., via Control-Flow
Integrity (CFI), is hard to achieve. This work constructs an architecture that
ensures integrity of software execution against run-time attacks, such as
Return-Oriented Programming (ROP). It is built atop a recently proposed CASU --
a low-cost active Root-of-Trust (RoT) that guarantees software immutability. We
extend CASU to support a shadow stack and a CFI monitor to mitigate run-time
attacks. This gives some confidence that CFI can indeed be attained even on
low-end devices, with minimal hardware overhead.
| arxiv topic:cs.CR |
arxiv_dataset-191532309.10496 | Note on general functional flows in equilibrium systems
cond-mat.stat-mech hep-th
We study the response of generating functionals to a variation of parameters
(couplings) in equilibrium systems i.e. in quantum field theory (QFT) and
equilibrium statistical mechanics. These parameters can be either physical ones
such as coupling constants or artificial ones which are intentionally
introduced such as the renormalization scale in field theories. We first derive
general functional flow equations for the generating functional
(grand-canonical potential) $W[J]$ of the connected diagrams. Then, we obtain
functional flow equations for the one-particle irreducible ($1$PI) vertex
functional (canonical potential) $\Gamma[\phi]$ by performing the Legendre
transformation. By taking the functional derivatives of the flow equations, we
can obtain an infinite hierarchical equations for the $1$PI vertices. We also
point out that a Callan-Symanzik type equation holds among the vertices when
partition function is invariant under some changes of the parameters. After
discussing general aspects of parameter response, we apply our formalism to
several examples and reproduce the well-known functional flow equations. Our
response theory provides us a systematic and general way to obtain various
functional flow equations in equilibrium systems.
| arxiv topic:cond-mat.stat-mech hep-th |
arxiv_dataset-191542309.10596 | Defining, measuring, and modeling passenger's in-vehicle experience and
acceptance of automated vehicles
cs.HC
Automated vehicle acceptance (AVA) has been measured mostly subjectively by
questionnaires and interviews, with a main focus on drivers inside automated
vehicles (AVs). To ensure that AVs are widely accepted by the public, ensuring
the acceptance by both drivers and passengers is key. The in-vehicle experience
of passengers will determine the extent to which AVs will be accepted by
passengers. A comprehensive understanding of potential assessment methods to
measure the passenger experience in AVs is needed to improve the in-vehicle
experience of passengers and thereby the acceptance. The present work provides
an overview of assessment methods that were used to measure a driver's
behavior, and cognitive and emotional states during (automated) driving. The
results of the review have shown that these assessment methods can be
classified by type of data-collection method (e.g., questionnaires, interviews,
direct input devices, sensors), object of their measurement (i.e., perception,
behavior, state), time of measurement, and degree of objectivity of the data
collected. A conceptual model synthesizes the results of the literature review,
formulating relationships between the factors constituting the in-vehicle
experience and AVA acceptance. It is theorized that the in-vehicle experience
influences the intention to use, with intention to use serving as predictor of
actual use. The model also formulates relationships between actual use and
well-being. A combined approach of using both subjective and objective
assessment methods is needed to provide more accurate estimates for AVA, and
advance the uptake and use of AVs.
| arxiv topic:cs.HC |
arxiv_dataset-191552309.10696 | Nuclear descent from the fission barrier in the presence of long--range
memory effects
nucl-th
We have investigated the peculiarities of nuclear descent from a parabolic
fission barrier within a generalized Langevin equation with power--law
$f(t-t')=(|t-t'|/\tau)^{-\alpha}$ memory function. We have observed much
stronger slowing down of the nuclear descent in the presence of long--range
memory effects, caused by the power--law memory function at $0<\alpha<1$, than
in the presence of short--range memory effects, generated by exponential
$f(t-t')={\rm exp}(-|t-t'|/\tau)$ memory function. At a specific value of the
exponent $\alpha=1/2$ of the power--law memory function, it turned out possible
to find analytically the trajectory of the descent and demonstrate that the
long--range memory effects give rise to complex time oscillations of nuclear
shape, becoming more frequent and damped with the correlation time $\tau$. We
have found fairly long ($>10^{-20}~{\rm s}$) times of the descent of $^{\rm
236}{\rm U}$ at the values of the correlation time $\tau \sim [10^{-24}\div
10^{-23}]~{\rm s}$.
| arxiv topic:nucl-th |
arxiv_dataset-191562309.10796 | Heuristic Search for Path Finding with Refuelling
cs.RO cs.AI
This paper considers a generalization of the Path Finding (PF) problem with
refuelling constraints referred to as the Gas Station Problem (GSP). Similar to
PF, given a graph where vertices are gas stations with known fuel prices, and
edge costs are the gas consumption between the two vertices, GSP seeks a
minimum-cost path from the start to the goal vertex for a robot with a limited
gas tank and a limited number of refuelling stops. While GSP is polynomial-time
solvable, it remains a challenge to quickly compute an optimal solution in
practice since it requires simultaneously determine the path, where to make the
stops, and the amount to refuel at each stop. This paper develops a heuristic
search algorithm called Refuel A$^*$ (RF-A$^*$) that iteratively constructs
partial solution paths from the start to the goal guided by a heuristic while
leveraging dominance rules for pruning during planning. RF-A$^*$ is guaranteed
to find an optimal solution and often runs 2 to 8 times faster than the
existing approaches in large city maps with several hundreds of gas stations.
| arxiv topic:cs.RO cs.AI |
arxiv_dataset-191572309.10896 | PLVS: A SLAM System with Points, Lines, Volumetric Mapping, and 3D
Incremental Segmentation
cs.CV cs.RO
This document presents PLVS: a real-time system that leverages sparse SLAM,
volumetric mapping, and 3D unsupervised incremental segmentation. PLVS stands
for Points, Lines, Volumetric mapping, and Segmentation. It supports RGB-D and
Stereo cameras, which may be optionally equipped with IMUs. The SLAM module is
keyframe-based, and extracts and tracks sparse points and line segments as
features. Volumetric mapping runs in parallel with respect to the SLAM
front-end and generates a 3D reconstruction of the explored environment by
fusing point clouds backprojected from keyframes. Different volumetric mapping
methods are supported and integrated in PLVS. We use a novel reprojection error
to bundle-adjust line segments. This error exploits available depth information
to stabilize the position estimates of line segment endpoints. An incremental
and geometric-based segmentation method is implemented and integrated for RGB-D
cameras in the PLVS framework. We present qualitative and quantitative
evaluations of the PLVS framework on some publicly available datasets. The
appendix details the adopted stereo line triangulation method and provides a
derivation of the Jacobians we used for line error terms. The software is
available as open-source.
| arxiv topic:cs.CV cs.RO |
arxiv_dataset-191582309.10996 | Hamilton Lie algebroids over Dirac structures and sigma models
math.DG hep-th math-ph math.MP math.SG
We propose a Hamiltonian Lie algebroid and a momentum section over a Dirac
structure as a generalization of a Hamiltonian Lie algebroid over a
pre-symplectic manifold and one over a Poisson manifold. A Hamiltonian Lie
algebroid and a momentum section are generalizations of a Hamiltonian G-space
and a momentum map over a symplectic manifold. We show some properties of a new
Hamiltonian Lie algebroid, and construct the mechanics with this structure as
an application, which are sigma models called the gauged Poisson sigma model
and the gauged Dirac sigma model.
| arxiv topic:math.DG hep-th math-ph math.MP math.SG |
arxiv_dataset-191592309.11096 | Delays in Reinforcement Learning
cs.LG
Delays are inherent to most dynamical systems. Besides shifting the process
in time, they can significantly affect their performance. For this reason, it
is usually valuable to study the delay and account for it. Because they are
dynamical systems, it is of no surprise that sequential decision-making
problems such as Markov decision processes (MDP) can also be affected by
delays. These processes are the foundational framework of reinforcement
learning (RL), a paradigm whose goal is to create artificial agents capable of
learning to maximise their utility by interacting with their environment.
RL has achieved strong, sometimes astonishing, empirical results, but delays
are seldom explicitly accounted for. The understanding of the impact of delay
on the MDP is limited. In this dissertation, we propose to study the delay in
the agent's observation of the state of the environment or in the execution of
the agent's actions. We will repeatedly change our point of view on the problem
to reveal some of its structure and peculiarities. A wide spectrum of delays
will be considered, and potential solutions will be presented. This
dissertation also aims to draw links between celebrated frameworks of the RL
literature and the one of delays.
| arxiv topic:cs.LG |
arxiv_dataset-191602309.11196 | When to Trust AI: Advances and Challenges for Certification of Neural
Networks
cs.LG cs.AI cs.CR cs.SC
Artificial intelligence (AI) has been advancing at a fast pace and it is now
poised for deployment in a wide range of applications, such as autonomous
systems, medical diagnosis and natural language processing. Early adoption of
AI technology for real-world applications has not been without problems,
particularly for neural networks, which may be unstable and susceptible to
adversarial examples. In the longer term, appropriate safety assurance
techniques need to be developed to reduce potential harm due to avoidable
system failures and ensure trustworthiness. Focusing on certification and
explainability, this paper provides an overview of techniques that have been
developed to ensure safety of AI decisions and discusses future challenges.
| arxiv topic:cs.LG cs.AI cs.CR cs.SC |
arxiv_dataset-191612309.11296 | On the monotonicity of non-local perimeter of convex bodies
math.MG math.FA
Under mild assumptions on the kernel $K\ge0$, the non-local $K$-perimeter
$P_K$ satisfies the monotonicity property on nested convex bodies, i.e., if
$A\subset B\subset\mathbb{R}^n$ are two convex bodies, then $P_K(A)\le P_K(B)$.
In this note, we prove quantitative lower bounds on the difference of the
$K$-perimeters of $A$ and $B$ in terms of their Hausdorff distance, provided
that $K$ satisfies suitable symmetry properties.
| arxiv topic:math.MG math.FA |
arxiv_dataset-191622309.11396 | Convergence rate of numerical scheme for SDEs with a distributional
drift in Besov space
math.PR cs.NA math.NA
This paper is concerned with numerical solutions of one-dimensional SDEs with
the drift being a generalised function, in particular belonging to the
Holder-Zygmund space $C^{-\gamma}$ of negative order $-\gamma<0$ in the spacial
variable. We design an Euler-Maruyama numerical scheme and prove its
convergence, obtaining an upper bound for the strong $L^1$ convergence rate. We
finally implement the scheme and discuss the results obtained.
| arxiv topic:math.PR cs.NA math.NA |
arxiv_dataset-191632309.11496 | Unveiling dark forces with measurements of the Large Scale Structure of
the Universe
astro-ph.CO hep-ph
Cosmology offers opportunities to test Dark Matter independently of its
interactions with the Standard Model. We study the imprints of long-range
forces acting solely in the dark sector on the distribution of galaxies, the
so-called Large Scale Structure (LSS). We derive the strongest constraint on
such forces from a combination of Planck and BOSS data. Along the way we
consistently develop, for the first time, the Effective Field Theory of LSS in
the presence of new dynamics in the dark sector. We forecast that future
surveys will improve the current bound by an order of magnitude.
| arxiv topic:astro-ph.CO hep-ph |
arxiv_dataset-191642309.11596 | Connectivity properties of the Schur-Horn map for real Grassmannians
math.DG
To any $V$ in the Grassmannian ${\rm Gr}_k({\mathbb R}^n)$ of $k$-dimensional
vector subspaces in ${\mathbb R}^n$ one can associate the diagonal entries of
the ($n\times n$) matrix corresponding to the orthogonal projection of
${\mathbb R}^n$ to $V$. One obtains a map ${\rm Gr}_k({\mathbb R}^n)\to
{\mathbb R}^n$ (the Schur-Horn map). The main result of this paper is a
criterion for pre-images of vectors in ${\mathbb R}^n$ to be connected. This
will allow us to deduce connectivity criteria for a certain class of subspaces
of the real Stiefel manifold which arise naturally in frame theory. We extend
in this way results of Cahill, Mixon, and Strawn.
| arxiv topic:math.DG |
arxiv_dataset-191652309.11696 | LLM-based Medical Assistant Personalization with Short- and Long-Term
Memory Coordination
cs.CL
Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable
proficiency in comprehending and generating natural language. On the other
hand, medical assistants hold the potential to offer substantial benefits for
individuals. However, the exploration of LLM-based personalized medical
assistant remains relatively scarce. Typically, patients converse differently
based on their background and preferences which necessitates the task of
enhancing user-oriented medical assistant. While one can fully train an LLM for
this objective, the resource consumption is unaffordable. Prior research has
explored memory-based methods to enhance the response with aware of previous
mistakes for new queries during a dialogue session. We contend that a mere
memory module is inadequate and fully training an LLM can be excessively
costly. In this study, we propose a novel computational bionic memory
mechanism, equipped with a parameter-efficient fine-tuning (PEFT) schema, to
personalize medical assistants.
| arxiv topic:cs.CL |
arxiv_dataset-191662309.11796 | A monotonicity formula for minimal connections
math.DG
For Hermitian connections on a Hermitian complex line bundle over a
Riemannian manifold, we can define the ``volume", which can be considered to be
the ``mirror" of the standard volume for submanifolds. We call the critical
points minimal connections.
In this paper, (1) we prove monotonicity formulas for minimal connections
with respect to some versions of volume functionals, which would be important
in bubbling analysis. As a corollary, we obtain the vanishing theorem for
minimal connections in the odd-dimensional case.
(2) We see that the formal ``large radius limit" of the defining equation of
minimal connections is that of Yang-Mills connections. Then the existence
theorem of minimal connections is proved for a ``sufficiently large" metric.
(3) We can consider deformed Donaldson-Thomas (dDT) connections on
$G_2$-manifolds as ``mirrors" of calibrated (associative) submanifolds. We show
that dDT connections are minimal connections, just as calibrated submanifolds
are minimal submanifolds. By the argument specific to dDT connections, we
obtain the stronger monotonicity formulas and vanishing theorem for dDT
connections than in (1).
| arxiv topic:math.DG |
arxiv_dataset-191672309.11896 | Focal Inferential Infusion Coupled with Tractable Density Discrimination
for Implicit Hate Detection
cs.CL cs.CY
Although pretrained large language models (PLMs) have achieved
state-of-the-art on many natural language processing (NLP) tasks, they lack an
understanding of subtle expressions of implicit hate speech. Various attempts
have been made to enhance the detection of implicit hate by augmenting external
context or enforcing label separation via distance-based metrics. Combining
these two approaches, we introduce FiADD, a novel Focused Inferential Adaptive
Density Discrimination framework. FiADD enhances the PLM finetuning pipeline by
bringing the surface form/meaning of an implicit hate speech closer to its
implied form while increasing the inter-cluster distance among various labels.
We test FiADD on three implicit hate datasets and observe significant
improvement in the two-way and three-way hate classification tasks. We further
experiment on the generalizability of FiADD on three other tasks, detecting
sarcasm, irony, and stance, in which surface and implied forms differ, and
observe similar performance improvements. Consequently, we analyze the
generated latent space to understand its evolution under FiADD, which
corroborates the advantage of employing FiADD for implicit hate speech
detection.
| arxiv topic:cs.CL cs.CY |
arxiv_dataset-191682309.11996 | Design and performance of the field cage for the XENONnT experiment
hep-ex physics.ins-det
The precision in reconstructing events detected in a dual-phase time
projection chamber depends on an homogeneous and well understood electric field
within the liquid target. In the XENONnT TPC the field homogeneity is achieved
through a double-array field cage, consisting of two nested arrays of field
shaping rings connected by an easily accessible resistor chain. Rather than
being connected to the gate electrode, the topmost field shaping ring is
independently biased, adding a degree of freedom to tune the electric field
during operation. Two-dimensional finite element simulations were used to
optimize the field cage, as well as its operation. Simulation results were
compared to ${}^{83m}\mathrm{Kr}$ calibration data. This comparison indicates
an accumulation of charge on the panels of the TPC which is constant over time,
as no evolution of the reconstructed position distribution of events is
observed. The simulated electric field was then used to correct the charge
signal for the field dependence of the charge yield. This correction resolves
the inconsistent measurement of the drift electron lifetime when using
different calibrations sources and different field cage tuning voltages.
| arxiv topic:hep-ex physics.ins-det |
arxiv_dataset-191692309.12096 | Chaotic von Zeipel-Lidov-Kozai Oscillations of Binary System around
Rotating Supermassive Black Hole
gr-qc
In this paper, we investigate the dynamics of a binary system that orbits a
rotating supermassive black hole. Our approach employs Fermi-Walker transport
to construct a local inertial reference frame, and to set up a Newtonian binary
system. We consider a scenario in which a circular geodesic observer is
positioned around a Kerr black hole, and thereby derive the equations of motion
governing the binary system. To eliminate the interaction terms between the
center of mass (CM) of the binary and its relative coordinates, we introduce a
small acceleration for the observer. This adjustment leads to the CM closely
following the observer's orbit, deviating from a circular geodesic. Here, we
first focus on elucidating the stability conditions in a hierarchical triple
system. Subsequently, we discuss the phenomenon of von Zeipel-Lidov-Kozai
(vZLK) oscillations, which manifest when the binary system is compact and the
initial inclination exceeds a critical angle. In hard binary systems, these
oscillations exhibit regular behavior, while in soft binary systems, they
exhibit a chaotic character, characterized by irregular periods and amplitudes,
albeit remaining stable. Additionally, we observe an orbital flip under
circumstances of large initial inclination. As for the motion of the CM, we
observe deviations from a purely circular orbit that transform into stable yet
chaotic oscillations characterized by minute amplitude variations.
| arxiv topic:gr-qc |
arxiv_dataset-191702309.12196 | Free probability via entropic optimal transport
math.PR math.FA math.OA
Let $\mu$ and $\nu$ be probability measures on $\mathbb{R}$ with compact
support, and let $\mu \boxplus \nu$ denote their additive free convolution. We
show that for $z \in \mathbb{R}$ greater than the sum of essential suprema of
$\mu$ and $\nu$, we have \begin{equation*} \int_{-\infty}^\infty \log(z - x)
\mu \boxplus \nu (\mathrm{d}x) = \sup_{\Pi} \left\{ \mathbf{E}_\Pi[\log(z -
(X+Y)] - H(\Pi|\mu \otimes \nu) \right\}, \end{equation*} where the supremum is
taken over all couplings $\Pi$ of the probability measures $\mu$ and $\nu$, and
$H(\Pi|\mu \otimes \nu)$ denotes the relative entropy of a coupling $\Pi$
against product measure. We prove similar formulas for the multiplicative free
convolution $\mu \boxtimes \nu$ and the free compression $[\mu]_\tau$ of
probability measures, as well as for multivariate free operations. Thus the
integrals of a log-potential against the fundamental measure operations of free
probability may be formulated in terms of entropic optimal transport problems.
The optimal couplings in these variational descriptions of the free probability
operations can be computed explicitly, and from these we can then deduce the
standard $R$- and $S$-transform descriptions of additive and multiplicative
free convolution. We use our optimal transport formulations to derive new
inequalities relating free and classical operations on probability measures,
such as the inequality \begin{equation*} \int_{-\infty}^\infty \log(z - x) \mu
\boxplus \nu (\mathrm{d}x) \geq \int_{-\infty}^{\infty} \log(z-x) \mu \ast \nu(
\mathrm{d}x) \end{equation*} relating free and classical convolution. Our
approach is based on applying a large deviation principle on the symmetric
group to the quadrature formulas of Marcus, Spielman and Srivastava.
| arxiv topic:math.PR math.FA math.OA |
arxiv_dataset-191712309.12296 | A rigorous model reduction for the anisotropic-scattering transport
process
math-ph math.MP
In this letter, we propose a reduced-order model to bridge the particle
transport mechanics and the macroscopic fluid dynamics in the highly scattered
regime. A rigorous mathematical derivation and a concise physical
interpretation are presented for an anisotropic-scattering transport process
with arbitrary order of scattering kernel. The prediction of the theoretical
model perfectly agrees with the numerical experiments. A clear picture of the
diffusion physics is revealed for the neutral particle transport in the
asymptotic optically thick regime.
| arxiv topic:math-ph math.MP |
arxiv_dataset-191722309.12396 | A Phenomenon Resembling Early Superhumps in a New SU UMa-Type Dwarf Nova
with a 2-Hour Orbital Period
astro-ph.SR
We investigate K2BS5, an optical transient that we identified in Campaign 13
of the Kepler/K2 archives by the "K2 Background Survey", and classify it as a
new SU UMa-type dwarf nova. Using the light curve generated from Kepler's
long-cadence observation mode, we analyze the dwarf nova during quiescence and
superoutburst. Following 20 days of quiescence at the start of the observation,
the system entered a superoutburst lasting 12 days, after which it experienced
at least one rebrightening. K2BS5 clearly meets the criteria for an SU UMa
star, but at the peak of the superoutburst, it also shows double-wave
oscillations consistent with the spectroscopic orbital period, a phenomenon
that closely resembles early superhumps in WZ Sge stars. While we do not
classify K2BS5 as a WZ Sge system, we discuss how this phenomenon could
complicate efforts to use the suspected detection of early superhumps to
distinguish SU UMa-type dwarf novae from the recently recognized class of
long-orbital-period WZ Sge systems.
| arxiv topic:astro-ph.SR |
arxiv_dataset-191732309.12496 | Optical Photon Simulation with Mitsuba3
physics.comp-ph hep-ex
Optical photon propagation is an embarrassingly parallel operation, well
suited to acceleration on GPU devices. Rendering of images employs similar
techniques -- for this reason, a pipeline to offload optical photon propagation
from Geant4 to the industry-standard open-source renderer Mitsuba3 has been
devised. With the creation of a dedicated plugin for single point multi-source
emission, we find a photon propagation rate of $2\times10^{5}$ photons per
second per CPU thread using LLVM and $1.2\times10^{6}$ photons per second per
GPU using CUDA. This represents a speed-up of 70 on CPU and 400 on GPU over
Geant4 and is competitive with other similar applications. The potential for
further applications is discussed.
| arxiv topic:physics.comp-ph hep-ex |
arxiv_dataset-191742309.12596 | Movable Antenna-Empowered AirComp
eess.SP
A novel over-the-air computation (AirComp) framework, empowered by the
incorporation of movable antennas (MAs), is proposed to significantly enhance
computation accuracy. Within this framework, the joint optimization of transmit
power control, antenna positioning, and receive combining is investigated. An
efficient method is proposed to tackle the problem of computation mean-squared
error (MSE) minimization, capitalizing on the approach of alternating
optimization. Numerical results are provided to substantiate the superior MSE
performance of the proposed framework, which establish its clear advantage over
benchmark systems employing conventional fixed-position antennas (FPAs).
| arxiv topic:eess.SP |
arxiv_dataset-191752309.12696 | Counterfactual Conservative Q Learning for Offline Multi-agent
Reinforcement Learning
cs.AI
Offline multi-agent reinforcement learning is challenging due to the coupling
effect of both distribution shift issue common in offline setting and the high
dimension issue common in multi-agent setting, making the action
out-of-distribution (OOD) and value overestimation phenomenon excessively
severe. Tomitigate this problem, we propose a novel multi-agent offline RL
algorithm, named CounterFactual Conservative Q-Learning (CFCQL) to conduct
conservative value estimation. Rather than regarding all the agents as a high
dimensional single one and directly applying single agent methods to it, CFCQL
calculates conservative regularization for each agent separately in a
counterfactual way and then linearly combines them to realize an overall
conservative value estimation. We prove that it still enjoys the
underestimation property and the performance guarantee as those single agent
conservative methods do, but the induced regularization and safe policy
improvement bound are independent of the agent number, which is therefore
theoretically superior to the direct treatment referred to above, especially
when the agent number is large. We further conduct experiments on four
environments including both discrete and continuous action settings on both
existing and our man-made datasets, demonstrating that CFCQL outperforms
existing methods on most datasets and even with a remarkable margin on some of
them.
| arxiv topic:cs.AI |
arxiv_dataset-191762309.12796 | Alteration of skeletal muscle energy metabolism assessed by 31P MRS in
clinical routine, part 1: Advanced Quality Control pipeline
eess.SP
Background: Implementing a standardized 31P-MRS dynamic acquisition protocol
to evaluate skeletal muscle energy metabolism and monitor muscle
fatigability1,2, while being compatible with various longitudinal clinical
studies on diversified patient cohorts, requires a high level of technicality
and expertise. Furthermore, processing data to obtain reliable results also
demands a great degree of expertise from the operator. In this two-part
article, we present an advanced quality control approach for data acquired
using a dynamic 31P-MRS protocol. The aim is to provide decision support to the
operator in order to assist in data processing and obtain reliable results
based on objective criteria. We present first in part one, an advanced data
quality control (QC) approach of a dynamic 31P-MRS protocol. Part two is an
impact study demonstrating the added value of the QC approach to explore
clinical results derived from two patient populations with significant fatigue:
COVID19 and multiple sclerosis (MS). Experimental: 31P-MRS was performed on a
3T clinical MRI in 175 subjects from clinical and healthy control populations
conducted in a University Hospital. An advanced data QC Score (QCS) was
developed using multiple objective criteria. The criteria were based on current
recommendations from the literature enriched by new proposals based on clinical
experience. The QCS was designed to indicate valid and corrupt data and guide
necessary objective data editing to extract as much valid physiological data as
possible. Dynamic acquisitions using an MR-compatible ergometer ran over a
rest(40s), exercise(2min), and a recovery phase(6min). Results: Using QCS
enabled rapid identification of subjects with data anomalies allowing the user
to correct the data series or reject them partially or entirely as well as
identify fully valid datasets. Overall, the use of the QCS resulted in the
automatic classification of 45% of the subjects including 58 participants that
had data with no criterion violation and 21 participants with violations that
resulted in the rejection of all dynamic data. The remaining datasets were
inspected manually with guidance allowing acceptance of full datasets from an
additional 80 participants and recovery phase data from an additional 16
subjects. Overall, more anomalies occurred with patient data (35% of datasets)
compared to healthy controls (15% of datasets). Conclusion: This paper
describes typical difficulties encountered during the dynamic acquisition of
31P-MRS. Based on these observations, a standardized data quality control
pipeline was created and implemented in both healthy and patient populations.
The QC scoring ensures a standardized data rejection procedure and rigorous
objective analysis of dynamic 31P-MRS data obtained from patients. The
contribution of this methodology contributes to efforts made to standardize the
practices of the 31P-MRS that has been underway for a decade, with the ultimate
goal of making it an empowered tool for clinical research.
| arxiv topic:eess.SP |
arxiv_dataset-191772309.12896 | Rheological Investigation of The Network Structure in Mixed Gels of
Kappa and Iota Carrageenan
cond-mat.soft
Carrageenans comprise linear sulfated high molecular weight polysaccharides
obtained from seaweeds and are routinely used in food and home/personal care
industries. Various kinds of carrageenans differ from others based on the ester
sulfate group location on the polysaccharide chains. Pure and mixed systems of
Kappa Carrageenan and Iota Carrageenan undergo a three-dimensional gel network
structure formation or dissociation with a change in temperature. During the
sol-gel and gel-sol transitions, the Carrageenan systems pass through a unique
critical gel state, where dynamic moduli are scale-invariant owing to the
self-similar structure of the three-dimensional network. In this work, we
obtain the critical gel state associated with pure and mixed systems of Kappa
and Iota Carrageenan during cooling and heating by exploring the material
behavior for a range of frequencies. Interestingly, on the one hand, the mixed
gels show a higher critical sol-gel transition temperature compared to the pure
systems at equal individual concentrations. On the other hand, the low
temperature moduli of mixed gels are closer to that of Kappa Carrageenan when
the concentration of the same is more than half in the mixture. The rheological
measurements demonstrate that the Kappa Carrageenan strongly affects the nature
of aggregation of double helices of Iota Carrageenan, but Iota Carrageenan does
not have a significant influence on that of Kappa Carrageenan. These results
suggest an associative, interactive network formation between Kappa and Iota
Carrageenan in the mixture, such that the gel behavior is predominantly
influenced by Kappa Carrageenan.
| arxiv topic:cond-mat.soft |
arxiv_dataset-191782309.12996 | Point Cloud Network: An Order of Magnitude Improvement in Linear Layer
Parameter Count
cs.LG cs.CV cs.NE
This paper introduces the Point Cloud Network (PCN) architecture, a novel
implementation of linear layers in deep learning networks, and provides
empirical evidence to advocate for its preference over the Multilayer
Perceptron (MLP) in linear layers. We train several models, including the
original AlexNet, using both MLP and PCN architectures for direct comparison of
linear layers (Krizhevsky et al., 2012). The key results collected are model
parameter count and top-1 test accuracy over the CIFAR-10 and CIFAR-100
datasets (Krizhevsky, 2009). AlexNet-PCN16, our PCN equivalent to AlexNet,
achieves comparable efficacy (test accuracy) to the original architecture with
a 99.5% reduction of parameters in its linear layers. All training is done on
cloud RTX 4090 GPUs, leveraging pytorch for model construction and training.
Code is provided for anyone to reproduce the trials from this paper.
| arxiv topic:cs.LG cs.CV cs.NE |
arxiv_dataset-191792309.13096 | Econometric Model Using Arbitrage Pricing Theory and Quantile Regression
to Estimate the Risk Factors Driving Crude Oil Returns
q-fin.ST
This work adopts a novel approach to determine the risk and return of crude
oil stocks by employing Arbitrage Pricing Theory (APT) and Quantile Regression
(QR).The APT identifies the underlying risk factors likely to impact crude oil
returns.Subsequently, QR estimates the relationship between the factors and the
returns across different quantiles of the distribution. The West Texas
Intermediate (WTI) crude oil price is used in this study as a benchmark for
crude oil prices. WTI price fluctuations can have a significant impact on the
performance of crude oil stocks and, subsequently, the global economy.To
determine the proposed models stability, various statistical measures are used
in this study.The results show that changes in WTI returns can have varying
effects depending on market conditions and levels of volatility. The study
highlights the impact of structural discontinuities on returns, which can be
caused by changes in the global economy and the demand for crude oil.The
inclusion of pandemic, geopolitical, and inflation-related explanatory
variables add uniqueness to this study as it considers current global events
that can affect crude oil returns.Findings show that the key factors that pose
major risks to returns are industrial production, inflation, the global price
of energy, the shape of the yield curve, and global economic policy
uncertainty.This implies that while making investing decisions in WTI futures,
investors should pay particular attention to these elements
| arxiv topic:q-fin.ST |
arxiv_dataset-191802309.13196 | ClusterFormer: Clustering As A Universal Visual Learner
cs.CV
This paper presents CLUSTERFORMER, a universal vision model that is based on
the CLUSTERing paradigm with TransFORMER. It comprises two novel designs: 1.
recurrent cross-attention clustering, which reformulates the cross-attention
mechanism in Transformer and enables recursive updates of cluster centers to
facilitate strong representation learning; and 2. feature dispatching, which
uses the updated cluster centers to redistribute image features through
similarity-based metrics, resulting in a transparent pipeline. This elegant
design streamlines an explainable and transferable workflow, capable of
tackling heterogeneous vision tasks (i.e., image classification, object
detection, and image segmentation) with varying levels of clustering
granularity (i.e., image-, box-, and pixel-level). Empirical results
demonstrate that CLUSTERFORMER outperforms various well-known specialized
architectures, achieving 83.41% top-1 acc. over ImageNet-1K for image
classification, 54.2% and 47.0% mAP over MS COCO for object detection and
instance segmentation, 52.4% mIoU over ADE20K for semantic segmentation, and
55.8% PQ over COCO Panoptic for panoptic segmentation. For its efficacy, we
hope our work can catalyze a paradigm shift in universal models in computer
vision.
| arxiv topic:cs.CV |
arxiv_dataset-191812309.13296 | Interactive Content Diversity and User Exploration in Online Movie
Recommenders: A Field Experiment
cs.HC cs.IR
Recommender systems often struggle to strike a balance between matching
users' tastes and providing unexpected recommendations. When recommendations
are too narrow and fail to cover the full range of users' preferences, the
system is perceived as useless. Conversely, when the system suggests too many
items that users don't like, it is considered impersonal or ineffective. To
better understand user sentiment about the breadth of recommendations given by
a movie recommender, we conducted interviews and surveys and found out that
many users considered narrow recommendations to be useful, while a smaller
number explicitly wanted greater breadth. Additionally, we designed and ran an
online field experiment with a larger user group, evaluating two new interfaces
designed to provide users with greater access to broader recommendations. We
looked at user preferences and behavior for two groups of users: those with
higher initial movie diversity and those with lower diversity. Among our
findings, we discovered that different level of exploration control and users'
subjective preferences on interfaces are more predictive of their satisfaction
with the recommender.
| arxiv topic:cs.HC cs.IR |
arxiv_dataset-191822309.13396 | EquiCity Game: A mathematical serious game for participatory design of
spatial configurations
cs.CY cs.HC
We propose mechanisms for a mathematical social-choice game that is designed
to mediate decision-making processes for city planning, urban area
redevelopment, and architectural design (massing) of urban housing complexes.
The proposed game is effectively a multi-player generative configurator
equipped with automated appraisal/scoring mechanisms for revealing the
aggregate impact of alternatives; featuring a participatory digital process to
support transparent and inclusive decision-making processes in spatial design
for ensuring an equitable balance of sustainable development goals. As such,
the game effectively empowers a group of decision-makers to reach a fair
consensus by mathematically simulating many rounds of trade-offs between their
decisions, with different levels of interest or control over various types of
investments. Our proposed gamified design process encompasses decision-making
about the most idiosyncratic aspects of a site related to its heritage status
and cultural significance to the physical aspects such as balancing access to
sunlight and the right to sunlight of the neighbours of the site, ensuring
coherence of the entire configuration with regards to a network of desired
closeness ratings, the satisfaction of a programme of requirements, and
intricately balancing individual development goals in conjunction with communal
goals and environmental design codes. The game is developed fully based on an
algebraic computational process on our own digital twinning platform, using
open geospatial data and open-source computational tools such as NumPy. The
mathematical process consists of a Markovian design machine for balancing the
decisions of actors, a massing configurator equipped with Fuzzy Logic and
Multi-Criteria Decision Analysis, algebraic graph-theoretical accessibility
evaluators, and automated solar-climatic evaluators using geospatial
computational geometry.
| arxiv topic:cs.CY cs.HC |
arxiv_dataset-191832309.13496 | Stratosphere: Finding Vulnerable Cloud Storage Buckets
cs.CR cs.NI
Misconfigured cloud storage buckets have leaked hundreds of millions of
medical, voter, and customer records. These breaches are due to a combination
of easily-guessable bucket names and error-prone security configurations,
which, together, allow attackers to easily guess and access sensitive data. In
this work, we investigate the security of buckets, finding that prior studies
have largely underestimated cloud insecurity by focusing on simple,
easy-to-guess names. By leveraging prior work in the password analysis space,
we introduce Stratosphere, a system that learns how buckets are named in
practice in order to efficiently guess the names of vulnerable buckets. Using
Stratosphere, we find wide-spread exploitation of buckets and vulnerable
configurations continuing to increase over the years. We conclude with
recommendations for operators, researchers, and cloud providers.
| arxiv topic:cs.CR cs.NI |
arxiv_dataset-191842309.13596 | Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data
Collection to Model Development
cs.CV
Advanced Driver-Assistance Systems (ADAS) have successfully integrated
learning-based techniques into vehicle perception and decision-making. However,
their application in 3D lane detection for effective driving environment
perception is hindered by the lack of comprehensive LiDAR datasets. The sparse
nature of LiDAR point cloud data prevents an efficient manual annotation
process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane
dataset that comprises 20k frames of surround-view LiDAR point clouds with
enriched semantic annotation. Unlike existing datasets confined to a frontal
perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the
ego vehicle, capturing complex lane patterns in both urban and highway
environments. We leverage the geometric traits of lane lines and the intrinsic
spatial attributes of LiDAR data to design a simple yet effective automatic
annotation pipeline for generating finer lane labels. To propel future
research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet,
incorporating the spatial geometry learning of the LiDAR point cloud into
Bird's Eye View (BEV) based lane identification. Experimental results indicate
that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D
lane detection task on the K-Lane dataset and our LiSV-3DLane.
| arxiv topic:cs.CV |
arxiv_dataset-191852309.13696 | Performance Evaluation of Equal-Weight Portfolio and Optimum Risk
Portfolio on Indian Stocks
q-fin.PM cs.LG
Designing an optimum portfolio for allocating suitable weights to its
constituent assets so that the return and risk associated with the portfolio
are optimized is a computationally hard problem. The seminal work of Markowitz
that attempted to solve the problem by estimating the future returns of the
stocks is found to perform sub-optimally on real-world stock market data. This
is because the estimation task becomes extremely challenging due to the
stochastic and volatile nature of stock prices. This work illustrates three
approaches to portfolio design minimizing the risk, optimizing the risk, and
assigning equal weights to the stocks of a portfolio. Thirteen critical sectors
listed on the National Stock Exchange (NSE) of India are first chosen. Three
portfolios are designed following the above approaches choosing the top ten
stocks from each sector based on their free-float market capitalization. The
portfolios are designed using the historical prices of the stocks from Jan 1,
2017, to Dec 31, 2022. The portfolios are evaluated on the stock price data
from Jan 1, 2022, to Dec 31, 2022. The performances of the portfolios are
compared, and the portfolio yielding the higher return for each sector is
identified.
| arxiv topic:q-fin.PM cs.LG |
arxiv_dataset-191862309.13796 | Using Z3 to Verify Inferences in Fragments of Linear Logic
cs.LO
Linear logic is a substructural logic proposed as a refinement of classical
and intuitionistic logics, with applications in programming languages, game
semantics, and quantum physics. We present a template for Gentzen-style linear
logic sequents that supports verification of logic inference rules using
automatic theorem proving. Specifically, we use the Z3 Theorem Prover [8] to
check targeted inference rules based on a set of inference rules that are
presumed to be valid. To demonstrate the approach, we apply it to validate
several derived inference rules for two different fragments of linear logic:
MLL+Mix (Multiplicative Linear Logic extended with a Mix rule) and MILL
(Multiplicative Intuitionistic Linear Logic).
| arxiv topic:cs.LO |
arxiv_dataset-191872309.13896 | Follow-ups Also Matter: Improving Contextual Bandits via Post-serving
Contexts
cs.LG stat.ML
Standard contextual bandit problem assumes that all the relevant contexts are
observed before the algorithm chooses an arm. This modeling paradigm, while
useful, often falls short when dealing with problems in which valuable
additional context can be observed after arm selection. For example, content
recommendation platforms like Youtube, Instagram, Tiktok also observe valuable
follow-up information pertinent to the user's reward after recommendation
(e.g., how long the user stayed, what is the user's watch speed, etc.). To
improve online learning efficiency in these applications, we study a novel
contextual bandit problem with post-serving contexts and design a new
algorithm, poLinUCB, that achieves tight regret under standard assumptions.
Core to our technical proof is a robustified and generalized version of the
well-known Elliptical Potential Lemma (EPL), which can accommodate noise in
data. Such robustification is necessary for tackling our problem, and we
believe it could also be of general interest. Extensive empirical tests on both
synthetic and real-world datasets demonstrate the significant benefit of
utilizing post-serving contexts as well as the superior performance of our
algorithm over the state-of-the-art approaches.
| arxiv topic:cs.LG stat.ML |
arxiv_dataset-191882309.13996 | First-principles calculation of orbital Hall effect by Wannier
interpolation: Role of orbital dependence of the anomalous position
cond-mat.mes-hall cond-mat.mtrl-sci
The position operator in a Bloch representation acquires a gauge correction
in the momentum space on top of the canonical position, which is called the
anomalous position. We show that the anomalous position is generally
orbital-dependent and thus plays a crucial role in the description of the
intrinsic orbital Hall effect in terms of Wannier basis. We demonstrate this
from the first-principles calculation of orbital Hall conductivities of
transition metals by Wannier interpolation. Our results show that consistent
treatment of the velocity operator by adding the additional term originating
from the anomalous position predicts the orbital Hall conductivities different
from those obtained by considering only the group velocity. We find the
difference is crucial in several metals. For example, we predict the negative
sign of the orbital Hall conductivities for elements in the groups X and XI
such as Cu, Ag, Au, and Pd, for which the previous studies predicted the
positive sign. Our work suggests the importance of consistently describing the
spatial dependence of basis functions by first-principles methods as it is
fundamentally missing in the tight-binding approximation.
| arxiv topic:cond-mat.mes-hall cond-mat.mtrl-sci |
arxiv_dataset-191892309.14096 | Tracking Control for a Spherical Pendulum via Curriculum Reinforcement
Learning
cs.LG cs.RO
Reinforcement Learning (RL) allows learning non-trivial robot control laws
purely from data. However, many successful applications of RL have relied on
ad-hoc regularizations, such as hand-crafted curricula, to regularize the
learning performance. In this paper, we pair a recent algorithm for
automatically building curricula with RL on massively parallelized simulations
to learn a tracking controller for a spherical pendulum on a robotic arm via
RL. Through an improved optimization scheme that better respects the
non-Euclidean task structure, we allow the method to reliably generate
curricula of trajectories to be tracked, resulting in faster and more robust
learning compared to an RL baseline that does not exploit this form of
structured learning. The learned policy matches the performance of an optimal
control baseline on the real system, demonstrating the potential of curriculum
RL to jointly learn state estimation and control for non-linear tracking tasks.
| arxiv topic:cs.LG cs.RO |
arxiv_dataset-191902309.14196 | Learning Restricted Boltzmann Machines with greedy quantum search
quant-ph cs.LG
Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected
graphical models with visible and latent nodes, playing an important role in
statistics and machine learning. The task of structure learning for RBMs
involves inferring the underlying graph by using samples from the visible
nodes. Specifically, learning the two-hop neighbors of each visible node allows
for the inference of the graph structure. Prior research has addressed the
structure learning problem for specific classes of RBMs, namely ferromagnetic
and locally consistent RBMs. In this paper, we extend the scope to the quantum
computing domain and propose corresponding quantum algorithms for this problem.
Our study demonstrates that the proposed quantum algorithms yield a polynomial
speedup compared to the classical algorithms for learning the structure of
these two classes of RBMs.
| arxiv topic:quant-ph cs.LG |
arxiv_dataset-191912309.14296 | Rapid Quantification of Dynamic and Spall Strength of Metals Across
Strain Rates
cond-mat.mtrl-sci
The response of metals and their microstructures under extreme dynamic
conditions can be markedly different from that under quasistatic conditions.
Traditionally, high strain rates and shock stresses are measured using
cumbersome and expensive methods such as the Kolsky bar or large spall
experiments. These methods are low throughput and do not facilitate
high-fidelity microstructure-property linkages. In this work, we combine two
powerful small-scale testing methods, custom nanoindentation, and laser-driven
micro-flyer shock, to measure the dynamic and spall strength of metals. The
nanoindentation system is configured to test samples from quasistatic to
dynamic strain rate regimes (10$^{-3}$ s$^{-1}$ to 10$^{+4}$ s$^{-1}$). The
laser-driven micro-flyer shock system can test samples through impact loading
between 10$^{+5}$ s$^{-1}$ to 10$^{+7}$ s$^{-1}$ strain rates, triggering spall
failure. The model material used for testing is Magnesium alloys, which are
lightweight, possess high-specific strengths and have historically been
challenging to design and strengthen due to their mechanical anisotropy. Here,
we modulate their microstructure by adding or removing precipitates to
demonstrate interesting upticks in strain rate sensitivity and evolution of
dynamic strength. At high shock loading rates, we unravel an interesting
paradigm where the spall strength of these materials converges, but the failure
mechanisms are markedly different. Peak aging, considered to be a standard
method to strengthen metallic alloys, causes catastrophic failure, faring much
worse than solutionized alloys. Our high throughput testing framework not only
quantifies strength but also teases out unexplored failure mechanisms at
extreme strain rates, providing valuable insights for the rapid design and
improvement of metals for extreme environments.
| arxiv topic:cond-mat.mtrl-sci |
arxiv_dataset-191922309.14396 | Guess & Sketch: Language Model Guided Transpilation
cs.SE cs.LG cs.PL
Maintaining legacy software requires many software and systems engineering
hours. Assembly code programs, which demand low-level control over the computer
machine state and have no variable names, are particularly difficult for humans
to analyze. Existing conventional program translators guarantee correctness,
but are hand-engineered for the source and target programming languages in
question. Learned transpilation, i.e. automatic translation of code, offers an
alternative to manual re-writing and engineering efforts. Automated symbolic
program translation approaches guarantee correctness but struggle to scale to
longer programs due to the exponentially large search space. Their rigid
rule-based systems also limit their expressivity, so they can only reason about
a reduced space of programs. Probabilistic neural language models (LMs) produce
plausible outputs for every input, but do so at the cost of guaranteed
correctness. In this work, we leverage the strengths of LMs and symbolic
solvers in a neurosymbolic approach to learned transpilation for assembly code.
Assembly code is an appropriate setting for a neurosymbolic approach, since
assembly code can be divided into shorter non-branching basic blocks amenable
to the use of symbolic methods. Guess & Sketch extracts alignment and
confidence information from features of the LM then passes it to a symbolic
solver to resolve semantic equivalence of the transpilation input and output.
We test Guess & Sketch on three different test sets of assembly transpilation
tasks, varying in difficulty, and show that it successfully transpiles 57.6%
more examples than GPT-4 and 39.6% more examples than an engineered transpiler.
We also share a training and evaluation dataset for this task.
| arxiv topic:cs.SE cs.LG cs.PL |
arxiv_dataset-191932309.14496 | Era Splitting: Invariant Learning for Decision Trees
cs.LG cs.AI cs.CE
Real-life machine learning problems exhibit distributional shifts in the data
from one time to another or from one place to another. This behavior is beyond
the scope of the traditional empirical risk minimization paradigm, which
assumes i.i.d. distribution of data over time and across locations. The
emerging field of out-of-distribution (OOD) generalization addresses this
reality with new theory and algorithms which incorporate "environmental", or
"era-wise" information into the algorithms. So far, most research has been
focused on linear models and/or neural networks . In this research we develop
two new splitting criteria for decision trees, which allow us to apply ideas
from OOD generalization research to decision tree models, namely, gradient
boosting decision trees (GBDTs). The new splitting criteria use era-wise
information associated with the data to grow tree-based models that are optimal
across all disjoint eras in the data, instead of optimal over the entire data
set pooled together, which is the default setting. In this paper, two new
splitting criteria are defined and analyzed theoretically. Effectiveness is
tested on four experiments, ranging from simple, synthetic to complex,
real-world applications. In particular we cast the OOD domain-adaptation
problem in the context of financial markets, where the new models out-perform
state-of-the-art GBDT models on the Numerai data set. The new criteria are
incorporated into the Scikit-Learn code base and made freely available online.
| arxiv topic:cs.LG cs.AI cs.CE |
arxiv_dataset-191942309.14596 | Model averaging: A shrinkage perspective
math.ST stat.TH
Model averaging (MA), a technique for combining estimators from a set of
candidate models, has attracted increasing attention in machine learning and
statistics. In the existing literature, there is an implicit understanding that
MA can be viewed as a form of shrinkage estimation that draws the response
vector towards the subspaces spanned by the candidate models. This paper
explores this perspective by establishing connections between MA and shrinkage
in a linear regression setting with multiple nested models. We first
demonstrate that the optimal MA estimator is the best linear estimator with
monotonically non-increasing weights in a Gaussian sequence model. The Mallows
MA (MMA), which estimates weights by minimizing the Mallows' $C_p$ over the
unit simplex, can be viewed as a variation of the sum of a set of positive-part
Stein estimators. Indeed, the latter estimator differs from the MMA only in
that its optimization of Mallows' $C_p$ is within a suitably relaxed weight
set. Motivated by these connections, we develop a novel MA procedure based on a
blockwise Stein estimation. The resulting Stein-type MA estimator is
asymptotically optimal across a broad parameter space when the variance is
known. Numerical results support our theoretical findings. The connections
established in this paper may open up new avenues for investigating MA from
different perspectives. A discussion on some topics for future research
concludes the paper.
| arxiv topic:math.ST stat.TH |
arxiv_dataset-191952309.14696 | On Deterministically Approximating Total Variation Distance
cs.DS math.PR
Total variation distance (TV distance) is an important measure for the
difference between two distributions. Recently, there has been progress in
approximating the TV distance between product distributions: a deterministic
algorithm for a restricted class of product distributions (Bhattacharyya,
Gayen, Meel, Myrisiotis, Pavan and Vinodchandran 2023) and a randomized
algorithm for general product distributions (Feng, Guo, Jerrum and Wang 2023).
We give a deterministic fully polynomial-time approximation algorithm (FPTAS)
for the TV distance between product distributions. Given two product
distributions $\mathbb{P}$ and $\mathbb{Q}$ over $[q]^n$, our algorithm
approximates their TV distance with relative error $\varepsilon$ in time
$O\bigl( \frac{qn^2}{\varepsilon} \log q \log \frac{n}{\varepsilon
\Delta_{\text{TV}}(\mathbb{P},\mathbb{Q}) } \bigr)$.
Our algorithm is built around two key concepts: 1) The likelihood ratio as a
distribution, which captures sufficient information to compute the TV distance.
2) We introduce a metric between likelihood ratio distributions, called the
minimum total variation distance. Our algorithm computes a sparsified
likelihood ratio distribution that is close to the original one w.r.t. the new
metric. The approximated TV distance can be computed from the sparsified
likelihood ratio.
Our technique also implies deterministic FPTAS for the TV distance between
Markov chains.
| arxiv topic:cs.DS math.PR |
arxiv_dataset-191962309.14796 | Forgetting-aware Linear Bias for Attentive Knowledge Tracing
cs.AI
Knowledge Tracing (KT) aims to track proficiency based on a question-solving
history, allowing us to offer a streamlined curriculum. Recent studies actively
utilize attention-based mechanisms to capture the correlation between questions
and combine it with the learner's characteristics for responses. However, our
empirical study shows that existing attention-based KT models neglect the
learner's forgetting behavior, especially as the interaction history becomes
longer. This problem arises from the bias that overprioritizes the correlation
of questions while inadvertently ignoring the impact of forgetting behavior.
This paper proposes a simple-yet-effective solution, namely Forgetting-aware
Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite
its simplicity, FoLiBi is readily equipped with existing attentive KT models by
effectively decomposing question correlations with forgetting behavior. FoLiBi
plugged with several KT models yields a consistent improvement of up to 2.58%
in AUC over state-of-the-art KT models on four benchmark datasets.
| arxiv topic:cs.AI |
arxiv_dataset-191972309.14896 | Hermitian K-theory of Grassmannians
math.KT math.AG math.CT math.RT
We compute the additive structure of the Hermitian $K$-theory spectrum of an
even-dimensional Grassmannian over a base field $k$ of characteristic zero in
terms of the Hermitian $K$-theory of $X$, using certain symmetries on Young
diagrams. The result is a direct sum of copies of the $K$-theory of the base
field and copies of the $GW$-theory of the base field, indexed by
\emph{asymmetric} and \emph{symmetric} Young diagrams, respectively.
| arxiv topic:math.KT math.AG math.CT math.RT |
arxiv_dataset-191982309.14996 | Implementation-Oblivious Transparent Checkpoint-Restart for MPI
cs.DC
This work presents experience with traditional use cases of checkpointing on
a novel platform. A single codebase (MANA) transparently checkpoints production
workloads for major available MPI implementations: "develop once, run
everywhere". The new platform enables application developers to compile their
application against any of the available standards-compliant MPI
implementations, and test each MPI implementation according to performance or
other features.
| arxiv topic:cs.DC |
arxiv_dataset-191992309.15096 | Fixing the NTK: From Neural Network Linearizations to Exact Convex
Programs
cs.LG stat.ML
Recently, theoretical analyses of deep neural networks have broadly focused
on two directions: 1) Providing insight into neural network training by SGD in
the limit of infinite hidden-layer width and infinitesimally small learning
rate (also known as gradient flow) via the Neural Tangent Kernel (NTK), and 2)
Globally optimizing the regularized training objective via cone-constrained
convex reformulations of ReLU networks. The latter research direction also
yielded an alternative formulation of the ReLU network, called a gated ReLU
network, that is globally optimizable via efficient unconstrained convex
programs. In this work, we interpret the convex program for this gated ReLU
network as a Multiple Kernel Learning (MKL) model with a weighted data masking
feature map and establish a connection to the NTK. Specifically, we show that
for a particular choice of mask weights that do not depend on the learning
targets, this kernel is equivalent to the NTK of the gated ReLU network on the
training data. A consequence of this lack of dependence on the targets is that
the NTK cannot perform better than the optimal MKL kernel on the training set.
By using iterative reweighting, we improve the weights induced by the NTK to
obtain the optimal MKL kernel which is equivalent to the solution of the exact
convex reformulation of the gated ReLU network. We also provide several
numerical simulations corroborating our theory. Additionally, we provide an
analysis of the prediction error of the resulting optimal kernel via
consistency results for the group lasso.
| arxiv topic:cs.LG stat.ML |
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