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arxiv_dataset-188002307.09005 | Frequency-mixed Single-source Domain Generalization for Medical Image
Segmentation
eess.IV cs.CV
The annotation scarcity of medical image segmentation poses challenges in
collecting sufficient training data for deep learning models. Specifically,
models trained on limited data may not generalize well to other unseen data
domains, resulting in a domain shift issue. Consequently, domain generalization
(DG) is developed to boost the performance of segmentation models on unseen
domains. However, the DG setup requires multiple source domains, which impedes
the efficient deployment of segmentation algorithms in clinical scenarios. To
address this challenge and improve the segmentation model's generalizability,
we propose a novel approach called the Frequency-mixed Single-source Domain
Generalization method (FreeSDG). By analyzing the frequency's effect on domain
discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the
single-source domain. Additionally, self-supervision is constructed in the
domain augmentation to learn robust context-aware representations for the
segmentation task. Experimental results on five datasets of three modalities
demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms
state-of-the-art methods and significantly improves the segmentation model's
generalizability. Therefore, FreeSDG provides a promising solution for
enhancing the generalization of medical image segmentation models, especially
when annotated data is scarce. The code is available at
https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.
| arxiv topic:eess.IV cs.CV |
arxiv_dataset-188012307.09105 | Sampling-based Model Predictive Control Leveraging Parallelizable
Physics Simulations
cs.RO
We present a method for sampling-based model predictive control that makes
use of a generic physics simulator as the dynamical model. In particular, we
propose a Model Predictive Path Integral controller (MPPI), that uses the
GPU-parallelizable IsaacGym simulator to compute the forward dynamics of a
problem. By doing so, we eliminate the need for explicit encoding of robot
dynamics and contacts with objects for MPPI. Since no explicit dynamic modeling
is required, our method is easily extendable to different objects and robots
and allows one to solve complex navigation and contact-rich tasks. We
demonstrate the effectiveness of this method in several simulated and
real-world settings, among which mobile navigation with collision avoidance,
non-prehensile manipulation, and whole-body control for high-dimensional
configuration spaces. This method is a powerful and accessible open-source tool
to solve a large variety of contact-rich motion planning tasks.
| arxiv topic:cs.RO |
arxiv_dataset-188022307.09205 | Learning Dynamic Attribute-factored World Models for Efficient
Multi-object Reinforcement Learning
cs.LG
In many reinforcement learning tasks, the agent has to learn to interact with
many objects of different types and generalize to unseen combinations and
numbers of objects. Often a task is a composition of previously learned tasks
(e.g. block stacking). These are examples of compositional generalization, in
which we compose object-centric representations to solve complex tasks. Recent
works have shown the benefits of object-factored representations and
hierarchical abstractions for improving sample efficiency in these settings. On
the other hand, these methods do not fully exploit the benefits of
factorization in terms of object attributes. In this paper, we address this
opportunity and introduce the Dynamic Attribute FacTored RL (DAFT-RL)
framework. In DAFT-RL, we leverage object-centric representation learning to
extract objects from visual inputs. We learn to classify them in classes and
infer their latent parameters. For each class of object, we learn a class
template graph that describes how the dynamics and reward of an object of this
class factorize according to its attributes. We also learn an interaction
pattern graph that describes how objects of different classes interact with
each other at the attribute level. Through these graphs and a dynamic
interaction graph that models the interactions between objects, we can learn a
policy that can then be directly applied in a new environment by just
estimating the interactions and latent parameters. We evaluate DAFT-RL in three
benchmark datasets and show our framework outperforms the state-of-the-art in
generalizing across unseen objects with varying attributes and latent
parameters, as well as in the composition of previously learned tasks.
| arxiv topic:cs.LG |
arxiv_dataset-188032307.09305 | Stationary equilibria and their stability in a Kuramoto MFG with strong
interaction
math.AP
Recently, R. Carmona, Q. Cormier, and M. Soner proposed a Mean Field Game
(MFG) version of the classical Kuramoto model, which describes synchronization
phenomena in a large population of rational interacting oscillators. The MFG
model exhibits several stationary equilibria, but the characterization of these
equilibria and their ability to capture dynamic equilibria in long time remains
largely open.
In this paper, we demonstrate that, up to a phase translation, there are only
two possible stationary equilibria: the incoherent equilibrium and the
self-organizing equilibrium, given that the interaction parameter is
sufficiently large. Furthermore, we present some local stability properties of
the self-organizing equilibrium.
| arxiv topic:math.AP |
arxiv_dataset-188042307.09405 | Causal effect of chemotherapy received dose intensity on survival
outcome: a retrospective study in osteosarcoma
stat.AP
This study aims to analyse the effects of reducing Received Dose Intensity
(RDI) in chemotherapy treatment for osteosarcoma patients on their survival by
using a novel approach. In this scenario, toxic side effects are risk factors
for mortality and predictors of future exposure levels, introducing
post-assignment confounding.
Chemotherapy administration data from BO03 and BO06 Randomized Clinical
Trials (RCTs) in ostosarcoma are employed to emulate a target trial with three
RDI-based exposure strategies: 1) standard, 2) reduced, and 3) highly-reduced
RDI. Investigations are conducted between subgroups of patients characterised
by poor or good Histological Responses (HRe). Inverse Probability of Treatment
Weighting (IPTW) is first used to transform the original population into a
pseudo-population which mimics the target randomized cohort. Then, a Marginal
Structural Cox Model with effect modification is employed. Conditional Average
Treatment Effects (CATEs) are ultimately measured as the difference between the
Restricted Mean Survival Time of reduced/highly-reduced RDI strategy and the
standard one. Confidence Intervals for CATEs are obtained using a novel
IPTW-based bootstrap procedure.
Significant effect modifications based on HRe were found. Increasing
RDI-reductions led to contrasting trends for poor and good responders: the
higher the reduction, the better (worsen) was the survival in poor (good)
reponders. This study introduces a novel approach to (i) comprehensively
address the challenges related to the analysis of chemotherapy data, (ii)
mitigate the toxicity-treatment-adjustment bias, and (iii) repurpose existing
RCT data for retrospective analyses extending beyond the original trials'
intended scopes.
| arxiv topic:stat.AP |
arxiv_dataset-188052307.09505 | Statistics of magnification for extremely lensed high redshift stars
astro-ph.CO astro-ph.GA hep-ph
Microlensing of stars in strongly lensed galaxies can lead to temporary
extreme magnification factors ($\mu\!>\!1000$), enabling their detection at
high redshifts. Following the discovery of Icarus, several stars at
cosmological distances ($z\!>\!1$) have been observed using the Hubble Space
Telescope (HST) and the James Webb Space Telescope (JWST). This emerging field
of gravitational lensing holds promise to study individual high redshift stars.
Also offers the opportunity to study the substructure in the lens plane with
implications for dark matter models, as more lensed stars are detected and
analysed statistically. Due to the computational demands of simulating
microlensing at large magnification factors, it is important to develop fast
and accurate analytical approximations for the probability of magnification in
such extreme scenarios. In this study, we consider different macro-model
magnification and microlensing surface mass density scenarios and study how the
probability of extreme magnification factors depends on these factors. To
achieve this, we create state of the art large simulations of the microlensing
effect in these scenarios. Through the analysis of these simulations, we derive
analytical scaling relationships that can bypass the need for expensive
numerical simulations. Our results are useful to interpret current observations
of stars at cosmic distances which are extremely magnified and under the
influence of microlenses.
| arxiv topic:astro-ph.CO astro-ph.GA hep-ph |
arxiv_dataset-188062307.09605 | Towards a Rosetta Stone for (meta)data: Learning from natural language
to improve semantic and cognitive interoperability
cs.DB
In order to effectively manage the overwhelming influx of data, it is crucial
to ensure that data is findable, accessible, interoperable, and reusable
(FAIR). While ontologies and knowledge graphs have been employed to enhance
FAIRness, challenges remain regarding semantic and cognitive interoperability.
We explore how English facilitates reliable communication of terms and
statements, and transfer our findings to a framework of ontologies and
knowledge graphs, while treating terms and statements as minimal information
units. We categorize statement types based on their predicates, recognizing the
limitations of modeling non-binary predicates with multiple triples, which
negatively impacts interoperability. Terms are associated with different frames
of reference, and different operations require different schemata. Term
mappings and schema crosswalks are therefore vital for semantic
interoperability. We propose a machine-actionable Rosetta Stone Framework for
(meta)data, which uses reference terms and schemata as an interlingua to
minimize mappings and crosswalks. Modeling statements rather than a
human-independent reality ensures cognitive familiarity and thus better
interoperability of data structures. We extend this Rosetta modeling paradigm
to reference schemata, resulting in simple schemata with a consistent structure
across statement types, empowering domain experts to create their own schemata
using the Rosetta Editor, without requiring knowledge of semantics. The Editor
also allows specifying textual and graphical display templates for each schema,
delivering human-readable data representations alongside machine-actionable
data structures. The Rosetta Query Builder derives queries based on completed
input forms and the information from corresponding reference schemata. This
work sets the conceptual ground for the Rosetta Stone Framework that we plan to
develop in the future.
| arxiv topic:cs.DB |
arxiv_dataset-188072307.09705 | CValues: Measuring the Values of Chinese Large Language Models from
Safety to Responsibility
cs.CL
With the rapid evolution of large language models (LLMs), there is a growing
concern that they may pose risks or have negative social impacts. Therefore,
evaluation of human values alignment is becoming increasingly important.
Previous work mainly focuses on assessing the performance of LLMs on certain
knowledge and reasoning abilities, while neglecting the alignment to human
values, especially in a Chinese context. In this paper, we present CValues, the
first Chinese human values evaluation benchmark to measure the alignment
ability of LLMs in terms of both safety and responsibility criteria. As a
result, we have manually collected adversarial safety prompts across 10
scenarios and induced responsibility prompts from 8 domains by professional
experts. To provide a comprehensive values evaluation of Chinese LLMs, we not
only conduct human evaluation for reliable comparison, but also construct
multi-choice prompts for automatic evaluation. Our findings suggest that while
most Chinese LLMs perform well in terms of safety, there is considerable room
for improvement in terms of responsibility. Moreover, both the automatic and
human evaluation are important for assessing the human values alignment in
different aspects. The benchmark and code is available on ModelScope and
Github.
| arxiv topic:cs.CL |
arxiv_dataset-188082307.09805 | Zero-field spin waves in YIG nano-waveguides
cond-mat.mes-hall
Spin-wave based transmission and processing of information is a promising
emerging nano-technology that can help overcome limitations of traditional
electronics based on the transfer of electrical charge. Among the most
important challenges for this technology is the implementation of spin-wave
devices that can operate without the need for an external bias magnetic field.
Here we experimentally demonstrate that this can be achieved using
sub-micrometer wide spin-wave waveguides fabricated from ultrathin films of
low-loss magnetic insulator - Yttrium Iron Garnet (YIG). We show that these
waveguides exhibit a highly stable single-domain static magnetic configuration
at zero field and support long-range propagation of spin waves with gigahertz
frequencies. The experimental results are supported by micromagnetic
simulations, which additionally provide information for optimization of
zero-field guiding structures. Our findings create the basis for the
development of energy-efficient zero-field spin-wave devices and circuits.
| arxiv topic:cond-mat.mes-hall |
arxiv_dataset-188092307.09905 | PyTAG: Challenges and Opportunities for Reinforcement Learning in
Tabletop Games
cs.AI
In recent years, Game AI research has made important breakthroughs using
Reinforcement Learning (RL). Despite this, RL for modern tabletop games has
gained little to no attention, even when they offer a range of unique
challenges compared to video games. To bridge this gap, we introduce PyTAG, a
Python API for interacting with the Tabletop Games framework (TAG). TAG
contains a growing set of more than 20 modern tabletop games, with a common API
for AI agents. We present techniques for training RL agents in these games and
introduce baseline results after training Proximal Policy Optimisation
algorithms on a subset of games. Finally, we discuss the unique challenges
complex modern tabletop games provide, now open to RL research through PyTAG.
| arxiv topic:cs.AI |
arxiv_dataset-188102307.10005 | Alzheimer's Disease Detection from Spontaneous Speech and Text: A review
eess.AS cs.SD
In the past decade, there has been a surge in research examining the use of
voice and speech analysis as a means of detecting neurodegenerative diseases
such as Alzheimer's. Many studies have shown that certain acoustic features can
be used to differentiate between normal aging and Alzheimer's disease, and
speech analysis has been found to be a cost-effective method of detecting
Alzheimer's dementia. The aim of this review is to analyze the various
algorithms used in speech-based detection and classification of Alzheimer's
disease. A literature survey was conducted using databases such as Web of
Science, Google Scholar, and Science Direct, and articles published from
January 2020 to the present were included based on keywords such as
``Alzheimer's detection'', "speech," and "natural language processing." The
ADReSS, Pitt corpus, and CCC datasets are commonly used for the analysis of
dementia from speech, and this review focuses on the various acoustic and
linguistic feature engineering-based classification models drawn from 15
studies.
Based on the findings of this study, it appears that a more accurate model
for classifying Alzheimer's disease can be developed by considering both
linguistic and acoustic data. The review suggests that speech signals can be a
useful tool for detecting dementia and may serve as a reliable biomarker for
efficiently identifying Alzheimer's disease.
| arxiv topic:eess.AS cs.SD |
arxiv_dataset-188112307.10105 | Percolation on hypergraphs and the hard-core model
math.PR cs.DS math.CO
We prove tight bounds on the site percolation threshold for $k$-uniform
hypergraphs of maximum degree $\Delta$ and for $k$-uniform hypergraphs of
maximum degree $\Delta$ in which any pair of edges overlaps in at most $r$
vertices. The hypergraphs that achieve these bounds are hypertrees, but unlike
in the case of graphs, there are many different $k$-uniform, $\Delta$-regular
hypertrees. Determining the extremal tree for a given $k, \Delta, r$ involves
an optimization problem, and our bounds arise from a convex relaxation of this
problem.
By combining our percolation bounds with the method of disagreement
percolation we obtain improved bounds on the uniqueness threshold for the
hard-core model on hypergraphs satisfying the same constraints. Our uniqueness
conditions imply exponential weak spatial mixing, and go beyond the known
bounds for rapid mixing of local Markov chains and existence of efficient
approximate counting and sampling algorithms. Our results lead to natural
conjectures regarding the aforementioned algorithmic tasks, based on the
intuition that uniqueness thresholds for the extremal hypertrees for
percolation determine computational thresholds.
| arxiv topic:math.PR cs.DS math.CO |
arxiv_dataset-188122307.10205 | Alleviating the Effect of Data Imbalance on Adversarial Training
cs.LG cs.CR cs.CV
In this paper, we study adversarial training on datasets that obey the
long-tailed distribution, which is practical but rarely explored in previous
works. Compared with conventional adversarial training on balanced datasets,
this process falls into the dilemma of generating uneven adversarial examples
(AEs) and an unbalanced feature embedding space, causing the resulting model to
exhibit low robustness and accuracy on tail data. To combat that, we
theoretically analyze the lower bound of the robust risk to train a model on a
long-tailed dataset to obtain the key challenges in addressing the
aforementioned dilemmas. Based on it, we propose a new adversarial training
framework -- Re-balancing Adversarial Training (REAT). This framework consists
of two components: (1) a new training strategy inspired by the effective number
to guide the model to generate more balanced and informative AEs; (2) a
carefully constructed penalty function to force a satisfactory feature space.
Evaluation results on different datasets and model structures prove that REAT
can effectively enhance the model's robustness and preserve the model's clean
accuracy. The code can be found in https://github.com/GuanlinLee/REAT.
| arxiv topic:cs.LG cs.CR cs.CV |
arxiv_dataset-188132307.10305 | Tapestry of Time and Actions: Modeling Human Activity Sequences using
Temporal Point Process Flows
cs.CV cs.LG
Human beings always engage in a vast range of activities and tasks that
demonstrate their ability to adapt to different scenarios. Any human activity
can be represented as a temporal sequence of actions performed to achieve a
certain goal. Unlike the time series datasets extracted from electronics or
machines, these action sequences are highly disparate in their nature -- the
time to finish a sequence of actions can vary between different persons.
Therefore, understanding the dynamics of these sequences is essential for many
downstream tasks such as activity length prediction, goal prediction, next
action recommendation, etc. Existing neural network-based approaches that learn
a continuous-time activity sequence (or CTAS) are limited to the presence of
only visual data or are designed specifically for a particular task, i.e.,
limited to next action or goal prediction. In this paper, we present ProActive,
a neural marked temporal point process (MTPP) framework for modeling the
continuous-time distribution of actions in an activity sequence while
simultaneously addressing three high-impact problems -- next action prediction,
sequence-goal prediction, and end-to-end sequence generation. Specifically, we
utilize a self-attention module with temporal normalizing flows to model the
influence and the inter-arrival times between actions in a sequence. In
addition, we propose a novel addition over the ProActive model that can handle
variations in the order of actions, i.e., different methods of achieving a
given goal. We demonstrate that this variant can learn the order in which the
person or actor prefers to do their actions. Extensive experiments on sequences
derived from three activity recognition datasets show the significant accuracy
boost of ProActive over the state-of-the-art in terms of action and goal
prediction, and the first-ever application of end-to-end action sequence
generation.
| arxiv topic:cs.CV cs.LG |
arxiv_dataset-188142307.10405 | Generative Visual Question Answering
cs.CV cs.AI
Multi-modal tasks involving vision and language in deep learning continue to
rise in popularity and are leading to the development of newer models that can
generalize beyond the extent of their training data. The current models lack
temporal generalization which enables models to adapt to changes in future
data. This paper discusses a viable approach to creating an advanced Visual
Question Answering (VQA) model which can produce successful results on temporal
generalization. We propose a new data set, GenVQA, utilizing images and
captions from the VQAv2 and MS-COCO dataset to generate new images through
stable diffusion. This augmented dataset is then used to test a combination of
seven baseline and cutting edge VQA models. Performance evaluation focuses on
questions mirroring the original VQAv2 dataset, with the answers having been
adjusted to the new images. This paper's purpose is to investigate the
robustness of several successful VQA models to assess their performance on
future data distributions. Model architectures are analyzed to identify common
stylistic choices that improve generalization under temporal distribution
shifts. This research highlights the importance of creating a large-scale
future shifted dataset. This data can enhance the robustness of VQA models,
allowing their future peers to have improved ability to adapt to temporal
distribution shifts.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-188152307.10505 | Analytic Solution for the Revised Helicity Evolution at Small $x$ and
Large $N_c$: New Resummed Gluon-Gluon Polarized Anomalous Dimension and
Intercept
hep-ph nucl-th
We construct an exact analytic solution of the revised small-$x$ helicity
evolution equations derived recently. The equations we solve are obtained in
the large-$N_c$ limit (with $N_c$ the number of quark colors) and are
double-logarithmic (summing powers of $\alpha_s \ln^2(1/x)$ with $\alpha_s$ the
strong coupling constant and $x$ the Bjorken $x$ variable). Our solution
provides small-$x$, large-$N_c$ expressions for the flavor-singlet quark and
gluon helicity parton distribution functions (PDFs) and for the $g_1$ structure
function, with their leading small-$x$ asymptotics given by \begin{align}
\Delta \Sigma (x, Q^2) \sim \Delta G (x, Q^2)
\sim g_1 (x, Q^2) \sim \left( \frac{1}{x} \right)^{\alpha_h} , \notag
\end{align} where the exact analytic expression we obtain for the intercept
$\alpha_h$ can be approximated by $\alpha_h = 3.66074 \, \sqrt{\frac{\alpha_s
\, N_c}{2 \pi}}$. Our solution also yields an all-order (in $\alpha_s$)
resummed small-$x$ anomalous dimension $\Delta \gamma_{GG} (\omega)$ which
agrees with all the existing fixed-order calculations (to three loops).
Notably, our anomalous dimension is different from that obtained in the
infrared evolution equation framework developed earlier by Bartels, Ermolaev,
and Ryskin (BER), with the disagreement starting at four loops. Despite the
previously reported agreement at two decimal points based on the numerical
solution of the same equations, the intercept of our large-$N_c$ helicity
evolution and that of BER disagree beyond that precision, with the BER
intercept at large $N_c$ given by a different analytic expression from ours
with the numerical value of $\alpha_h^{BER} = 3.66394 \, \sqrt{\frac{\alpha_s
\, N_c}{2 \pi}}$. We speculate on the origin of this disagreement.
| arxiv topic:hep-ph nucl-th |
arxiv_dataset-188162307.10605 | Model order reduction with novel discrete empirical interpolation
methods in space-time
math.NA cs.NA
This work proposes novel techniques for the efficient numerical simulation of
parameterized, unsteady partial differential equations. Projection-based
reduced order models (ROMs) such as the reduced basis method employ a
(Petrov-)Galerkin projection onto a linear low-dimensional subspace. In
unsteady applications, space-time reduced basis (ST-RB) methods have been
developed to achieve a dimension reduction both in space and time, eliminating
the computational burden of time marching schemes. However, nonaffine
parameterizations dilute any computational speedup achievable by traditional
ROMs. Computational efficiency can be recovered by linearizing the nonaffine
operators via hyper-reduction, such as the empirical interpolation method in
matrix form. In this work, we implement new hyper-reduction techniques
explicitly tailored to deal with unsteady problems and embed them in a ST-RB
framework. For each of the proposed methods, we develop a posteriori error
bounds. We run numerical tests to compare the performance of the proposed ROMs
against high-fidelity simulations, in which we combine the finite element
method for space discretization on 3D geometries and the Backward Euler time
integrator. In particular, we consider a heat equation and an unsteady Stokes
equation. The numerical experiments demonstrate the accuracy and computational
efficiency our methods retain with respect to the high-fidelity simulations.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-188172307.10705 | TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and
Lane Segmentation in Self-Driving Cars
cs.CV cs.LG
Semantic segmentation is a common task in autonomous driving to understand
the surrounding environment. Driveable Area Segmentation and Lane Detection are
particularly important for safe and efficient navigation on the road. However,
original semantic segmentation models are computationally expensive and require
high-end hardware, which is not feasible for embedded systems in autonomous
vehicles. This paper proposes a lightweight model for the driveable area and
lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate
and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K
dataset and compare it with modern models. Experimental results show that our
TwinLiteNet performs similarly to existing approaches, requiring significantly
fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score
of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task
with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000.
Furthermore, TwinLiteNet can run in real-time on embedded devices with limited
computing power, especially since it achieves 60FPS on Jetson Xavier NX, making
it an ideal solution for self-driving vehicles. Code is available:
url{https://github.com/chequanghuy/TwinLiteNet}.
| arxiv topic:cs.CV cs.LG |
arxiv_dataset-188182307.10805 | Communication-Efficient Split Learning via Adaptive Feature-Wise
Compression
cs.DC cs.AI cs.LG
This paper proposes a novel communication-efficient split learning (SL)
framework, named SplitFC, which reduces the communication overhead required for
transmitting intermediate feature and gradient vectors during the SL training
process. The key idea of SplitFC is to leverage different dispersion degrees
exhibited in the columns of the matrices. SplitFC incorporates two compression
strategies: (i) adaptive feature-wise dropout and (ii) adaptive feature-wise
quantization. In the first strategy, the intermediate feature vectors are
dropped with adaptive dropout probabilities determined based on the standard
deviation of these vectors. Then, by the chain rule, the intermediate gradient
vectors associated with the dropped feature vectors are also dropped. In the
second strategy, the non-dropped intermediate feature and gradient vectors are
quantized using adaptive quantization levels determined based on the ranges of
the vectors. To minimize the quantization error, the optimal quantization
levels of this strategy are derived in a closed-form expression. Simulation
results on the MNIST, CIFAR-100, and CelebA datasets demonstrate that SplitFC
outperforms state-of-the-art SL frameworks by significantly reducing
communication overheads while maintaining high accuracy.
| arxiv topic:cs.DC cs.AI cs.LG |
arxiv_dataset-188192307.10905 | A new metric on the contactomorphism group of orderable contact
manifolds
math.SG
We introduce a pseudo-metric on the contactomorphism group of any contact
manifold $(M,\xi)$ with a cooriented contact structure $\xi$. It is the contact
analogue of a corresponding semi-norm in Hofer's geometry, and on certain
classes of contact manifolds, its lift to the universal cover can be viewed as
a continuous version of the integer valued bi-invariant metric introduced by
Fraser, Polterovich, and Rosen. We show that it is non-degenerate if and only
if $(M,\xi)$ is strongly orderable and that its metric topology agrees with the
interval topology introduced by Chernov and Nemirovski. In particular, the
interval topology is Hausdorff whenever it is non-trivial, which answers a
question of Chernov and Nemirovski. We discuss analogous results for isotopy
classes of Legendrians and universal covers.
| arxiv topic:math.SG |
arxiv_dataset-188202307.11005 | Integrating Pretrained ASR and LM to Perform Sequence Generation for
Spoken Language Understanding
cs.CL cs.SD eess.AS
There has been an increased interest in the integration of pretrained speech
recognition (ASR) and language models (LM) into the SLU framework. However,
prior methods often struggle with a vocabulary mismatch between pretrained
models, and LM cannot be directly utilized as they diverge from its NLU
formulation. In this study, we propose a three-pass end-to-end (E2E) SLU system
that effectively integrates ASR and LM subnetworks into the SLU formulation for
sequence generation tasks. In the first pass, our architecture predicts ASR
transcripts using the ASR subnetwork. This is followed by the LM subnetwork,
which makes an initial SLU prediction. Finally, in the third pass, the
deliberation subnetwork conditions on representations from the ASR and LM
subnetworks to make the final prediction. Our proposed three-pass SLU system
shows improved performance over cascaded and E2E SLU models on two benchmark
SLU datasets, SLURP and SLUE, especially on acoustically challenging
utterances.
| arxiv topic:cs.CL cs.SD eess.AS |
arxiv_dataset-188212307.11105 | Technical Challenges of Deploying Reinforcement Learning Agents for Game
Testing in AAA Games
cs.SE cs.AI cs.LG
Going from research to production, especially for large and complex software
systems, is fundamentally a hard problem. In large-scale game production, one
of the main reasons is that the development environment can be very different
from the final product. In this technical paper we describe an effort to add an
experimental reinforcement learning system to an existing automated game
testing solution based on scripted bots in order to increase its capacity. We
report on how this reinforcement learning system was integrated with the aim to
increase test coverage similar to [1] in a set of AAA games including
Battlefield 2042 and Dead Space (2023). The aim of this technical paper is to
show a use-case of leveraging reinforcement learning in game production and
cover some of the largest time sinks anyone who wants to make the same journey
for their game may encounter. Furthermore, to help the game industry to adopt
this technology faster, we propose a few research directions that we believe
will be valuable and necessary for making machine learning, and especially
reinforcement learning, an effective tool in game production.
| arxiv topic:cs.SE cs.AI cs.LG |
arxiv_dataset-188222307.11205 | Mean Flow and Turbulence in Unsteady Canopy Layers
physics.flu-dyn
Non-stationarity is the rule in the atmospheric boundary layer (ABL). Under
such conditions, the flow may experience departures from equilibrium with the
underlying surface stress, misalignment of shear stresses and strain rates, and
three-dimensionality in turbulence statistics. Existing ABL flow theories are
primarily established for statistically stationary flow conditions and cannot
predict such behaviors. Motivated by this knowledge gap, this study analyzes
the impact of time-varying pressure gradients on mean flow and turbulence over
urban-like surfaces. A series of large-eddy simulations of pulsatile flow over
cuboid arrays is performed, programmatically varying the oscillation amplitude
$\alpha$ and forcing frequency $\omega$. The analysis focuses on both
longtime-averaged and phase-dependent flow dynamics. Inspection of
longtime-averaged velocity profiles reveals that the aerodynamic roughness
length $z_0$ increases with $\alpha$ and $\omega$, whereas the displacement
height $d$ appears to be insensitive to these parameters. In terms of
phase-averaged flow statistics, it is found that $\alpha$ primarily controls
the oscillation amplitude of the streamwise velocity and Reynolds stresses, but
has a negligible impact on their wall-normal structure. On the other hand,
$\omega$ determines the size of the region affected by the unsteady forcing,
which identifies the so-called Stokes layer thickness $\delta_s$. Within the
Stokes layer, phase-averaged resolved Reynolds stress profiles feature
substantial variations during the pulsatile cycle, and the turbulence is out of
equilibrium with the mean flow. Two phenomenological models have been proposed
that capture the influence of flow unsteadiness on $z_0$ and $\delta_s$,
respectively.
| arxiv topic:physics.flu-dyn |
arxiv_dataset-188232307.11305 | Quantum Software Analytics: Opportunities and Challenges
cs.SE
Quantum computing systems depend on the principles of quantum mechanics to
perform multiple challenging tasks more efficiently than their classical
counterparts. In classical software engineering, the software life cycle is
used to document and structure the processes of design, implementation, and
maintenance of software applications. It helps stakeholders understand how to
build an application. In this paper, we summarize a set of software analytics
topics and techniques in the development life cycle that can be leveraged and
integrated into quantum software application development. The results of this
work can assist researchers and practitioners in better understanding the
quantum-specific emerging development activities, challenges, and opportunities
in the next generation of quantum software.
| arxiv topic:cs.SE |
arxiv_dataset-188242307.11405 | Statistical analysis for a penalized EM algorithm in high-dimensional
mixture linear regression model
math.ST stat.TH
The expectation-maximization (EM) algorithm and its variants are widely used
in statistics. In high-dimensional mixture linear regression, the model is
assumed to be a finite mixture of linear regression and the number of
predictors is much larger than the sample size. The standard EM algorithm,
which attempts to find the maximum likelihood estimator, becomes infeasible for
such model. We devise a group lasso penalized EM algorithm and study its
statistical properties. Existing theoretical results of regularized EM
algorithms often rely on dividing the sample into many independent batches and
employing a fresh batch of sample in each iteration of the algorithm. Our
algorithm and theoretical analysis do not require sample-splitting, and can be
extended to multivariate response cases. The proposed methods also have
encouraging performances in numerical studies.
| arxiv topic:math.ST stat.TH |
arxiv_dataset-188252307.11505 | Data-Driven Cooperative Adaptive Cruise Control for Unknown Nonlinear
Vehicle Platoons
eess.SY cs.RO cs.SY math.OC
This paper studies cooperative adaptive cruise control (CACC) for vehicle
platoons with consideration of the unknown nonlinear vehicle dynamics that are
normally ignored in the literature. A unified data-driven CACC design is
proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and
human-driven vehicles (HVs). The CACC leverages online-collected sufficient
data samples of vehicle accelerations, spacing and relative velocities. The
data-driven control design is formulated as a semidefinite program (SDP) that
can be solved efficiently using off-the-shelf solvers. The efficacy and
advantage of the proposed CACC are demonstrated through a comparison with the
classic adaptive cruise control (ACC) method on a platoon of pure AVs and a
mixed platoon under a representative aggressive driving profile.
| arxiv topic:eess.SY cs.RO cs.SY math.OC |
arxiv_dataset-188262307.11605 | Homogenisation of nonlinear Dirichlet problems in randomly perforated
domains under minimal assumptions on the size of perforations
math.AP math.PR
In this paper we study the convergence of integral functionals with
$q$-growth in a randomly perforated domain of $\mathbb R^n$, with $1<q<n$.
Under the assumption that the perforations are small balls whose centres and
radii are generated by a \emph{stationary short-range marked point process}, we
obtain in the critical-scaling limit an averaged analogue of the nonlinear
capacitary term obtained by Ansini and Braides in the deterministic periodic
case \cite{Ansini-Braides}. In analogy to the random setting introduced by
Giunti, H\"ofer, and Vel\'azquez \cite{Giunti-Hofer-Velasquez} to study the
Poisson equation, we only require that the random radii have finite
$(n-q)$-moment. This assumption on the one hand ensures that the expectation of
the nonlinear $q$-capacity of the spherical holes is finite, and hence that the
limit problem is well defined. On the other hand, it does not exclude the
presence of balls with large radii, that can cluster up. We show however that
the critical rescaling of the perforations is sufficient to ensure that no
percolating-like structures appear in the limit.
| arxiv topic:math.AP math.PR |
arxiv_dataset-188272307.11705 | Small Sample Inference for Two-way Capture Recapture Experiments
stat.ME math.ST stat.AP stat.TH
The properties of the generalized Waring distribution defined on the non
negative integers are reviewed. Formulas for its moments and its mode are
given. A construction as a mixture of negative binomial distributions is also
presented. Then we turn to the Petersen model for estimating the population
size $N$ in a two-way capture recapture experiment. We construct a Bayesian
model for $N$ by combining a Waring prior with the hypergeometric distribution
for the number of units caught twice in the experiment. Credible intervals for
$N$ are obtained using quantiles of the posterior, a generalized Waring
distribution. The standard confidence interval for the population size
constructed using the asymptotic variance of Petersen estimator and .5 logit
transformed interval are shown to be special cases of the generalized Waring
credible interval. The true coverage of this interval is shown to be bigger
than or equal to its nominal converage in small populations, regardless of the
capture probabilities. In addition, its length is substantially smaller than
that of the .5 logit transformed interval. Thus the proposed generalized Waring
credible interval appears to be the best way to quantify the uncertainty of the
Petersen estimator for populations size.
| arxiv topic:stat.ME math.ST stat.AP stat.TH |
arxiv_dataset-188282307.11805 | Geometrogenesis in GFT: An Analysis
physics.hist-ph gr-qc
In this paper I introduce the idea of geometrogenesis as suggested in the
group field theory literature and I offer a criticism of it. Geometrogenesis in
the context of GFT is the idea that what we observe as the big bang is nothing
else but a phase transition from a non-geometric phase of the universe to a
geometric one which is the one we live in and the one to which the spacetime
concepts apply. GFT offers the machinery to speak about geometric and
non-geometric phases, but I argue that there are serious conceptual issues that
threaten the viability of the idea. Some of these issues are directly related
to the foundations of GFT and are concerned with the fact that it isn't clear
what GFT amounts to and how to understand it. The other main source of trouble
has to do with geometrogenesis itself and its conceptual underpinnings as it is
unclear whether it requires the addition of an extra temporal or quasitemporal
dimension which is unwanted and problematic.
| arxiv topic:physics.hist-ph gr-qc |
arxiv_dataset-188292307.11905 | Characterising the Hierarchy of Multi-time Quantum Processes with
Classical Memory
quant-ph
Memory is the fundamental form of temporal complexity: when present but
uncontrollable, it manifests as non-Markovian noise; conversely, if
controllable, memory can be a powerful resource for information processing.
Memory effects arise from/are transmitted via interactions between a system and
its environment; as such, they can be either classical or quantum. From a
practical standpoint, quantum processes with classical memory promise near-term
applicability: they are more powerful than their memoryless counterpart, yet at
the same time can be controlled over significant timeframes without being
spoiled by decoherence. However, despite practical and foundational value,
apart from simple two-time scenarios, the distinction between quantum and
classical memory remains unexplored. Here, we analyse multi-time quantum
processes with memory mechanisms that transmit only classical information
forward in time. Complementing this analysis, we also study two related -- but
simpler to characterise -- sets of processes that could also be considered to
have classical memory from a structural perspective, and demonstrate that these
lead to remarkably distinct phenomena in the multi-time setting. Subsequently,
we systematically stratify the full hierarchy of memory effects in quantum
mechanics, many levels of which collapse in the two-time setting, making our
results genuinely multi-time phenomena.
| arxiv topic:quant-ph |
arxiv_dataset-188302307.12005 | A Cascade Transformer-based Model for 3D Dose Distribution Prediction in
Head and Neck Cancer Radiotherapy
eess.IV physics.med-ph
Radiation therapy is the primary method used to treat cancer in the clinic.
Its goal is to deliver a precise dose to the planning target volume (PTV) while
protecting the surrounding organs at risk (OARs). However, the traditional
workflow used by dosimetrists to plan the treatment is time-consuming and
subjective, requiring iterative adjustments based on their experience. Deep
learning methods can be used to predict dose distribution maps to address these
limitations. The study proposes a cascade model for organs at risk segmentation
and dose distribution prediction. An encoder-decoder network has been developed
for the segmentation task, in which the encoder consists of transformer blocks,
and the decoder uses multi-scale convolutional blocks. Another cascade
encoder-decoder network has been proposed for dose distribution prediction
using a pyramid architecture. The proposed model has been evaluated using an
in-house head and neck cancer dataset of 96 patients and OpenKBP, a public head
and neck cancer dataset of 340 patients. The segmentation subnet achieved 0.79
and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the
existing baselines. The dose distribution prediction subnet outperformed the
winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVH
scores, respectively. The predicted dose maps showed good coincidence with
ground truth, with a superiority after linking with the auxiliary segmentation
task. The proposed model outperformed state-of-the-art methods, especially in
regions with low prescribed doses.
| arxiv topic:eess.IV physics.med-ph |
arxiv_dataset-188312307.12105 | $W$-boson Mass Anomaly from High-Dimensional Scalar Multiplets
hep-ph
In light of the recently discovered $W$-boson mass anomaly by the CDF
Collaboration, we discuss two distinct mechanisms that could possibly explain
this anomaly through the introduction of high-dimensional $SU(2)_L$ scalar
multiplets. The first mechanism is the tree-level $W$-boson mass correction
induced by the vacuum expectation values of one or more $SU(2)_L$ scalar
multiplets with odd dimensions of $n\geq 3$ and zero hypercharge of $Y=0$ in
order to avoid the strong constraint from measurements of the $Z$-boson mass.
The second mechanism is to consider the one-loop level $W$-boson mass
correction from a complex multiplet. In particular, we focus on the case with
an additional scalar octuplet with $Y=7/2$. As a result, we find that both
mechanisms can explain the $W$-boson mass anomaly without violating any other
theoretical or experimental constraints.
| arxiv topic:hep-ph |
arxiv_dataset-188322307.12205 | Observation of spin polarons in a frustrated moir\'e Hubbard system
cond-mat.str-el cond-mat.mes-hall
The electron's kinetic energy plays a pivotal role in magnetism. While
virtual electron hopping promotes antiferromagnetism in an insulator, the real
process usually favors ferromagnetism. But in kinetically frustrated systems,
such as hole doped triangular lattice Mott insulators, real hopping has been
shown to favor antiferromagnetism. Kinetic frustration has also been predicted
to induce a new quasiparticle -- a bound state of the doped hole and a spin
flip called a spin polaron -- at intermediate magnetic fields, which could form
an unusual metallic state. However, the direct experimental observation of spin
polarons has remained elusive. Here we report the observation of spin polarons
in triangular lattice MoTe2/WSe2 moir\'e bilayers by the reflective magnetic
circular dichroism measurements. We identify a spin polaron phase at lattice
filling factor between 0.8-1 and magnetic field between 2-4 T; it is separated
from the fully spin polarized phase by a metamagnetic transition. We determine
that the spin polaron is a spin-3/2 particle and its binding energy is
commensurate to the kinetic hopping energy. Our results open the door for
exploring spin polaron pseudogap metals, spin polaron pairing and other new
phenomena in triangular lattice moir\'e materials.
| arxiv topic:cond-mat.str-el cond-mat.mes-hall |
arxiv_dataset-188332307.12305 | On the Manipulability of Maximum Vertex-Weighted Bipartite $b$-matching
Mechanisms
cs.GT
In this paper, we study the Maximum Vertex-weighted $b$-Matching (MVbM)
problem on bipartite graphs in a new game-theoretical environment. In contrast
to other game-theoretical settings, we consider the case in which the value of
the tasks is public and common to every agent so that the private information
of every agent consists of edges connecting them to the set of tasks. In this
framework, we study three mechanisms. Two of these mechanisms, namely $\MB$ and
$\MD$, are optimal but not truthful, while the third one, $\MG$, is truthful
but sub-optimal. Albeit these mechanisms are induced by known algorithms, we
show $\MB$ and $\MD$ are the best possible mechanisms in terms of Price of
Anarchy and Price of Stability, while $\MG$ is the best truthful mechanism in
terms of approximated ratio. Furthermore, we characterize the Nash Equilibria
of $\MB$ and $\MD$ and retrieve sets of conditions under which $\MB$ acts as a
truthful mechanism, which highlights the differences between $\MB$ and $\MD$.
Finally, we extend our results to the case in which agents' capacity is part of
their private information.
| arxiv topic:cs.GT |
arxiv_dataset-188342307.12405 | Optimal Control of Multiclass Fluid Queueing Networks: A Machine
Learning Approach
cs.LG
We propose a machine learning approach to the optimal control of multiclass
fluid queueing networks (MFQNETs) that provides explicit and insightful control
policies. We prove that a threshold type optimal policy exists for MFQNET
control problems, where the threshold curves are hyperplanes passing through
the origin. We use Optimal Classification Trees with hyperplane splits (OCT-H)
to learn an optimal control policy for MFQNETs. We use numerical solutions of
MFQNET control problems as a training set and apply OCT-H to learn explicit
control policies. We report experimental results with up to 33 servers and 99
classes that demonstrate that the learned policies achieve 100\% accuracy on
the test set. While the offline training of OCT-H can take days in large
networks, the online application takes milliseconds.
| arxiv topic:cs.LG |
arxiv_dataset-188352307.12505 | Optimizing parameter search for community detection in time evolving
networks of complex systems
q-bio.NC nlin.AO physics.data-an
Network representations have been effectively employed to analyze complex
systems across various areas and applications, leading to the development of
network science as a core tool to study systems with multiple components and
complex interactions. There is a growing interest in understanding the temporal
dynamics of complex networks to decode the underlying dynamic processes through
the temporal changes in network structure. Community detection algorithms,
which are specialized clustering algorithms, have been instrumental in studying
these temporal changes. They work by grouping nodes into communities based on
the structure and intensity of network connections over time aiming to maximize
modularity of the network partition. However, the performance of these
algorithms is highly influenced by the selection of resolution parameters of
the modularity function used, which dictate the scale of the represented
network, both in size of communities and the temporal resolution of dynamic
structure. The selection of these parameters has often been subjective and
heavily reliant on the characteristics of the data used to create the network
structure. Here, we introduce a method to objectively determine the values of
the resolution parameters based on the elements of self-organization. We
propose two key approaches: (1) minimization of the biases in spatial scale
network characterization and (2) maximization of temporal scale-freeness. We
demonstrate the effectiveness of these approaches using benchmark network
structures as well as real-world datasets. To implement our method, we also
provide an automated parameter selection software package that can be applied
to a wide range of complex systems.
| arxiv topic:q-bio.NC nlin.AO physics.data-an |
arxiv_dataset-188362307.12605 | On the complexity of Pareto-optimal and envy-free lotteries
cs.GT cs.CC
We study the classic problem of dividing a collection of indivisible
resources in a fair and efficient manner among a set of agents having varied
preferences. Pareto optimality is a standard notion of economic efficiency,
which states that it should be impossible to find an allocation that improves
some agent's utility without reducing any other's. On the other hand, a
fundamental notion of fairness in resource allocation settings is that of
envy-freeness, which renders an allocation to be fair if every agent (weakly)
prefers her own bundle over that of any other agent's bundle. Unfortunately, an
envy-free allocation may not exist if we wish to divide a collection of
indivisible items. Introducing randomness is a typical way of circumventing the
non-existence of solutions, and therefore, allocation lotteries, i.e.,
distributions over allocations have been explored while relaxing the notion of
fairness to ex-ante envy freeness.
We consider a general fair division setting with $n$ agents and a family of
admissible $n$-partitions of an underlying set of items. Every agent is endowed
with partition-based utilities, which specify her cardinal utility for each
bundle of items in every admissible partition. In such fair division instances,
Cole and Tao (2021) have proved that an ex-ante envy-free and Pareto-optimal
allocation lottery is always guaranteed to exist. We strengthen their result
while examining the computational complexity of the above total problem and
establish its membership in the complexity class PPAD. Furthermore, for
instances with a constant number of agents, we develop a polynomial-time
algorithm to find an ex-ante envy-free and Pareto-optimal allocation lottery.
On the negative side, we prove that maximizing social welfare over ex-ante
envy-free and Pareto-optimal allocation lotteries is NP-hard.
| arxiv topic:cs.GT cs.CC |
arxiv_dataset-188372307.12705 | Phonon damping in a 2D superfluid: insufficiency of Fermi's golden rule
at low temperature
cond-mat.quant-gas
It is generally accepted that the phonon gas of a superfluid always enters a
weak coupling regime at sufficiently low temperatures, whatever the strength of
the interactions between the underlying particles (constitutive of the
superfluid). Thus, in this limit, we should always be able to calculate the
damping rate of thermal phonons by applying Fermi's golden rule to the
Hamiltonian $H_3$ of cubic phonon-phonon coupling taken from quantum
hydrodynamics, at least in the case of a convex acoustic branch and in the
collisionless regime (where the eigenfrequency of the considered phonons
remains much greater than the gas thermalization rate). Using the many-body
Green's function method, we predict that, unexpectedly, this is not true in two
dimensions, contrary to the three-dimensional case. We confirm this prediction
with classical phonon-field simulations and a non-perturbative theory in $H_3$,
where the fourth order is regularized by hand, giving a complex energy to the
virtual phonons of the four-phonon collisional processes. For a weakly
interacting fluid and a phonon mode in the long-wavelength limit, we predict a
damping rate about three times lower than that of the golden rule.
| arxiv topic:cond-mat.quant-gas |
arxiv_dataset-188382307.12805 | Unraveling Quantum Coherences Mediating Primary Charge Transfer
Processes in Photosystem II Reaction Center
physics.chem-ph cond-mat.other quant-ph
Photosystem II (PSII) reaction center is a unique protein-chromophore complex
that is capable of efficiently separating electronic charges across the
membrane after photoexcitation. In the PSII reaction center, the primary
energy- and charge-transfer (CT) processes occur on comparable ultrafast
timescales, which makes it extremely challenging to understand the fundamental
mechanism responsible for the near-unity quantum efficiency of the transfer.
Here, we elucidate the role of quantum coherences in the ultrafast energy and
CT in the PSII reaction center by performing two-dimensional (2D) electronic
spectroscopy at the cryogenic temperature of 20 K, which captures the distinct
underlying quantum coherences. Specifically, we uncover the electronic and
vibrational coherences along with their lifetimes during the primary ultrafast
processes of energy and CT. We also examine the functional role of the observed
quantum coherences. To gather further insight, we construct a structure-based
excitonic model that provided evidence for coherent energy and CT at low
temperature in the 2D electronic spectra. The principles, uncovered by this
combination of experimental and theoretical analyses, could provide valuable
guidelines for creating artificial photosystems with exploitation of
system-bath coupling and control of coherences to optimize the photon
conversion efficiency to specific functions.
| arxiv topic:physics.chem-ph cond-mat.other quant-ph |
arxiv_dataset-188392307.12905 | On Logic Gates with Complex Numbers
quant-ph math-ph math.MP
Logic gates can be written in terms of complex differential operators, where
the inputs and outputs are holomorphic functions with several variables. Using
the polar representation of complex numbers, we arrive at an immediate
connection between the oscillatory behavior of the system and logic gates. We
discuss the universality of this formalism in a variety of computing systems.
| arxiv topic:quant-ph math-ph math.MP |
arxiv_dataset-188402307.13005 | IteraTTA: An interface for exploring both text prompts and audio priors
in generating music with text-to-audio models
eess.AS cs.AI cs.HC cs.SD
Recent text-to-audio generation techniques have the potential to allow novice
users to freely generate music audio. Even if they do not have musical
knowledge, such as about chord progressions and instruments, users can try
various text prompts to generate audio. However, compared to the image domain,
gaining a clear understanding of the space of possible music audios is
difficult because users cannot listen to the variations of the generated audios
simultaneously. We therefore facilitate users in exploring not only text
prompts but also audio priors that constrain the text-to-audio music generation
process. This dual-sided exploration enables users to discern the impact of
different text prompts and audio priors on the generation results through
iterative comparison of them. Our developed interface, IteraTTA, is
specifically designed to aid users in refining text prompts and selecting
favorable audio priors from the generated audios. With this, users can
progressively reach their loosely-specified goals while understanding and
exploring the space of possible results. Our implementation and discussions
highlight design considerations that are specifically required for
text-to-audio models and how interaction techniques can contribute to their
effectiveness.
| arxiv topic:eess.AS cs.AI cs.HC cs.SD |
arxiv_dataset-188412307.13105 | Framework for Automatic PCB Marking Detection and Recognition for
Hardware Assurance
eess.IV
A Bill of Materials (BoM) is a list of all components on a printed circuit
board (PCB). Since BoMs are useful for hardware assurance, automatic BoM
extraction (AutoBoM) is of great interest to the government and electronics
industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB
text and logos must be utilized. In this study, we discuss the challenges
associated with automatic PCB marking extraction and propose 1) a plan for
collecting salient PCB marking data, and 2) a framework for incorporating this
data for automatic PCB assurance. Given the proposed dataset plan and
framework, subsequent future work, implications, and open research
possibilities are detailed.
| arxiv topic:eess.IV |
arxiv_dataset-188422307.13205 | Text-oriented Modality Reinforcement Network for Multimodal Sentiment
Analysis from Unaligned Multimodal Sequences
cs.MM
Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from
text, visual, and acoustic modalities. Previous works have focused on
representation learning and feature fusion strategies. However, most of these
efforts ignored the disparity in the semantic richness of different modalities
and treated each modality in the same manner. That may lead to strong
modalities being neglected and weak modalities being overvalued. Motivated by
these observations, we propose a Text-oriented Modality Reinforcement Network
(TMRN), which focuses on the dominance of the text modality in MSA. More
specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to
make full interaction for text/acoustic and text/visual pairs, and a Text-Gated
Self-Attention (TGSA) module to guide the self-reinforcement of the other two
modalities. Furthermore, we present an adaptive fusion mechanism to decide the
proportion of different modalities involved in the fusion process. Finally, we
combine the feature matrices into vectors to get the final representation for
the downstream tasks. Experimental results show that our TMRN outperforms the
state-of-the-art methods on two MSA benchmarks.
| arxiv topic:cs.MM |
arxiv_dataset-188432307.13305 | Inclusive photon multiplicity at forward pseudorapidities in pp and
p$-$Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV with ALICE
nucl-ex
Global observables such as the pseudorapidity distributions of particle
multiplicities in the final state are crucial to shed light into the physics
processes involved in hadronic collisions. In proton$-$lead (p$-$Pb) collisions
at Large Hadron Collider (LHC) energies, such measurements provide an important
baseline to understand lead$-$lead (Pb$-$Pb) results by disentangling hot
nuclear matter effects from the ones due to the cold nuclear matter.
Multiplicity measurements can also put constraints on theoretical models
describing the initial stages of the collision, e.g., to what degree the
nucleon and the nuclei interact as dilute (partons) or dense (CGC-like) fields.
The study of inclusive photon multiplicity aims to provide complementary
measurements to those obtained with charged particles.
In these proceedings, the pseudorapidity distributions of inclusive photons
at forward pseudorapidity ($2.3 < \eta_{\rm lab} < 3.9$) in pp and p$-$Pb
collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV are presented. The data samples
were collected using the Photon Multiplicity Detector (PMD) of ALICE. The
multiplicity dependence of photon production in p$-$Pb collisions is presented
and a comparison with charged-particle distributions measured at
mid-pseudorapidity is shown. The results are also compared with predictions
from Monte Carlo event generators.
| arxiv topic:nucl-ex |
arxiv_dataset-188442307.13405 | Towards Bridging the Digital Language Divide
cs.CL cs.AI
It is a well-known fact that current AI-based language technology -- language
models, machine translation systems, multilingual dictionaries and corpora --
focuses on the world's 2-3% most widely spoken languages. Recent research
efforts have attempted to expand the coverage of AI technology to
`under-resourced languages.' The goal of our paper is to bring attention to a
phenomenon that we call linguistic bias: multilingual language processing
systems often exhibit a hardwired, yet usually involuntary and hidden
representational preference towards certain languages. Linguistic bias is
manifested in uneven per-language performance even in the case of similar test
conditions. We show that biased technology is often the result of research and
development methodologies that do not do justice to the complexity of the
languages being represented, and that can even become ethically problematic as
they disregard valuable aspects of diversity as well as the needs of the
language communities themselves. As our attempt at building diversity-aware
language resources, we present a new initiative that aims at reducing
linguistic bias through both technological design and methodology, based on an
eye-level collaboration with local communities.
| arxiv topic:cs.CL cs.AI |
arxiv_dataset-188452307.13505 | Minimizing Cost Register Automata over a Field
cs.FL
Weighted automata (WA) are an extension of finite automata that define
functions from words to values in a given semiring. An alternative
deterministic model, called Cost Register Automata (CRA), was introduced by
Alur et al. It enriches deterministic finite automata with a finite number of
registers, which store values, updated at each transition using the operations
of the semiring. It is known that CRA with register updates defined by linear
maps have the same expressiveness as WA. Previous works have studied the
register minimization problem: given a function computable by a WA and an
integer k, is it possible to realize it using a CRA with at most k registers?
In this paper, we solve this problem for CRA over a field with linear register
updates, using the notion of linear hull, an algebraic invariant of WA
introduced recently by Bell and Smertnig. We then generalise the approach to
solve a more challenging problem, that consists in minimizing simultaneously
the number of states and that of registers. In addition, we also lift our
results to the setting of CRA with affine updates. Last, while the linear hull
was recently shown to be computable by Bell and Smertnig, no complexity bounds
were given. To fill this gap, we provide two new algorithms to compute
invariants of WA. This allows us to show that the register (resp.
state-register) minimization problem can be solved in 2-ExpTime (resp. in
NExpTime).
| arxiv topic:cs.FL |
arxiv_dataset-188462307.13605 | Isogeometric analysis of insoluble surfactant spreading on a thin film
math.NA cs.NA
In this paper we tackle the problem of surfactant spreading on a thin liquid
film in the framework of isogeometric analysis. We consider a mathematical
model that describes this phenomenon as an initial boundary value problem
(IBVP) that includes two coupled fourth order partial differential equations
(PDEs), one for the film height and one for the surfactant concentration. In
order to solve this problem numerically, it is customary to transform it into a
mixed problem that includes at most second order PDEs. However, the
higher-order continuity of the approximation functions in Isogeometric Analysis
(IGA) allows us to deal with the weak form of the fourth order PDEs directly,
without the need of resorting to mixed methods. We demonstrate numerically that
the IGA solution is able to reproduce results obtained before with mixed
approaches. Complex phenomena such as Marangoni-driven fingering instabilities
triggered by perturbations are easily captured.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-188472307.13705 | Control and Monitoring of Artificial Intelligence Algorithms
cs.LG cs.AI
This paper elucidates the importance of governing an artificial intelligence
model post-deployment and overseeing potential fluctuations in the distribution
of present data in contrast to the training data. The concepts of data drift
and concept drift are explicated, along with their respective foundational
distributions. Furthermore, a range of metrics is introduced, which can be
utilized to scrutinize the model's performance concerning potential temporal
variations.
| arxiv topic:cs.LG cs.AI |
arxiv_dataset-188482307.13805 | Piezo-resistive pressure sensor based on CVD-grown ZnO nanowires on
Polyethylene Tetrathalate substrate
physics.ins-det cond-mat.mtrl-sci physics.app-ph
Recent developments in the domain of electronic materials and devices have
attracted the interest of researchers toward flexible and printable electronic
components like organic transistors, printable electrodes and sensors. Zinc
Oxide (ZnO) nanowires (NWs) possess a number of excellent properties like high
mobility, large exciton binding energy and the direct-band gap in addition to
large piezoelectric coefficients. Here, we report on flexible piezo-resistive
sensor based on Indium tin oxide (ITO)-coated Polyethylene tetrathalate (PET)
substrate. The device shows sensitivity in terms of change in resistance from
100 {\Omega} to 2.4 K{\Omega} at an applied potential of 5V upon bending from
flat to 95 degrees. The 1-D nanowire flexible device in its flat state shows
saturated output current. We observed ten folds enhanced variation as compared
to previous reports. Improved sensitivity was observed in our experiments due
to fewer defects in CVD-grown NWs as compared to others where hydrothermally
grown nanowires were used. The methodology of device fabrication reported here
requires less time and enables efficient devices for the realization of
flexible and wearable technology.
| arxiv topic:physics.ins-det cond-mat.mtrl-sci physics.app-ph |
arxiv_dataset-188492307.13905 | Reinforcement Learning for Sequential Decoding of Generalized LDPC Codes
cs.IT math.IT
In this work, we propose reinforcement learning (RL) for sequential decoding
of moderate length generalized low-density parity-check (GLDPC) codes. Here,
sequential decoding refers to scheduling all the generalized constraint nodes
(GCNs) and single parity-check nodes (SPCNs) of a GLDPC code serially in each
iteration. A GLDPC decoding environment is modeled as a finite Markov decision
process (MDP) in which the state-space comprises of all possible sequences of
hard-decision values of the variables nodes (VNs) connected to the scheduled
GCN or SPCN, and the action-space of the MDP consists of all possible actions
(GCN and SPCN scheduling). The goal of RL is to determine an optimized
scheduling policy, i.e., one that results in a decoded codeword by minimizing
the complexity of the belief propagation (BP) decoder. For training, we
consider the proportion of correct bits at the output of the GCN or SPCN as a
reward once it is scheduled. The expected rewards for scheduling all the
GCNs/SPCNs in the code's Tanner graph are earned via BP decoding during the RL
phase. The proposed RL-based decoding scheme is shown to significantly
outperform the standard BP flooding decoder, as well as a sequential decoder in
which the GCNs/SPCNs are scheduled randomly.
| arxiv topic:cs.IT math.IT |
arxiv_dataset-188502307.14005 | Unsupervised extraction of local and global keywords from a single text
cs.CL cs.DL
We propose an unsupervised, corpus-independent method to extract keywords
from a single text. It is based on the spatial distribution of words and the
response of this distribution to a random permutation of words. As compared to
existing methods (such as e.g. YAKE) our method has three advantages. First, it
is significantly more effective at extracting keywords from long texts. Second,
it allows inference of two types of keywords: local and global. Third, it
uncovers basic themes in texts. Additionally, our method is
language-independent and applies to short texts. The results are obtained via
human annotators with previous knowledge of texts from our database of
classical literary works (the agreement between annotators is from moderate to
substantial). Our results are supported via human-independent arguments based
on the average length of extracted content words and on the average number of
nouns in extracted words. We discuss relations of keywords with higher-order
textual features and reveal a connection between keywords and chapter
divisions.
| arxiv topic:cs.CL cs.DL |
arxiv_dataset-188512307.14105 | Active Robot Vision for Distant Object Change Detection: A Lightweight
Training Simulator Inspired by Multi-Armed Bandits
cs.RO
In ground-view object change detection, the recently emerging mapless
navigation has great potential to navigate a robot to objects distantly
detected (e.g., books, cups, clothes) and acquire high-resolution object
images, to identify their change states (no-change/appear/disappear). However,
naively performing full journeys for every distant object requires huge
sense/plan/action costs, proportional to the number of objects and the
robot-to-object distance. To address this issue, we explore a new map-based
active vision problem in this work: ``Which journey should the robot select
next?" However, the feasibility of the active vision framework remains unclear;
Since distant objects are only uncertainly recognized, it is unclear whether
they can provide sufficient cues for action planning. This work presents an
efficient simulator for feasibility testing, to accelerate the early-stage R&D
cycles (e.g., prototyping, training, testing, and evaluation). The proposed
simulator is designed to identify the degree of difficulty that a robot vision
system (sensors/recognizers/planners/actuators) would face when applied to a
given environment (workspace/objects). Notably, it requires only one real-world
journey experience per distant object to function, making it suitable for an
efficient R&D cycle. Another contribution of this work is to present a new
lightweight planner inspired by the traditional multi-armed bandit problem.
Specifically, we build a lightweight map-based planner on top of the mapless
planner, which constitutes a hierarchical action planner. We verified the
effectiveness of the proposed framework using a semantically non-trivial
scenario ``sofa as bookshelf".
| arxiv topic:cs.RO |
arxiv_dataset-188522307.14205 | Braneworld sum rules and positive tension branes in a massive gravity
hep-th
By taking advantage of the braneworld sum rules, we explore the feasibility
of constructing a flat 3-brane scenario consisting solely of positive tension
branes in a 5D extension of the Lorentz-violating massive gravity. It is found
that the theory supports three distinct brane configurations, one of which is
exactly what we expected, consisting solely of two positive tension branes. The
cosmological problem of Randall-Sundrum-1 model and the gauge hierarchy problem
can be solved in this model simultaneously. Furthermore, the analysis of linear
perturbations reveals that the tensor, vector and scalar modes are all massive
and share the same mass spectrum, except that the ground state of vector mode
is absent. Moreover, the tensor and vector modes are robust, but the scalar
mode is ghost-like. Interestingly, even though the Kaluza-Klein gravitons have
an extremely small mass splitting scale, an estimation of the effective
gravitational potential and production of these gravitons on the brane
indicates that the phenomenology of the present model is equivalent to that of
the 6D ADD model.
| arxiv topic:hep-th |
arxiv_dataset-188532307.14305 | Automatically Evaluating Opinion Prevalence in Opinion Summarization
cs.CL
When faced with a large number of product reviews, it is not clear that a
human can remember all of them and weight opinions representatively to write a
good reference summary. We propose an automatic metric to test the prevalence
of the opinions that a summary expresses, based on counting the number of
reviews that are consistent with each statement in the summary, while
discrediting trivial or redundant statements. To formulate this opinion
prevalence metric, we consider several existing methods to score the factual
consistency of a summary statement with respect to each individual source
review. On a corpus of Amazon product reviews, we gather multiple human
judgments of the opinion consistency, to determine which automatic metric best
expresses consistency in product reviews. Using the resulting opinion
prevalence metric, we show that a human authored summary has only slightly
better opinion prevalence than randomly selected extracts from the source
reviews, and previous extractive and abstractive unsupervised opinion
summarization methods perform worse than humans. We demonstrate room for
improvement with a greedy construction of extractive summaries with twice the
opinion prevalence achieved by humans. Finally, we show that preprocessing
source reviews by simplification can raise the opinion prevalence achieved by
existing abstractive opinion summarization systems to the level of human
performance.
| arxiv topic:cs.CL |
arxiv_dataset-188542307.14405 | Rescuing leptogenesis parameter space of inverse seesaw
hep-ph
In a pure inverse seesaw framework, achieving a substantial lepton asymmetry
that can be converted into the observed baryon asymmetry of the Universe is
extremely challenging. The difficulty arises primarily due to two reasons, (i)
partial cancellation of the lepton asymmetries associated with the components
of a pseudo-Dirac pair, and (ii) strong wash out caused by the inverse decays.
In this work we offer two possible resolutions to overcome the above mentioned
challenges considering a (3,3) ISS framework. Our first proposal is based on
the assumption of a non-standard cosmological era in the pre-BBN epoch, that
triggers a faster expansion of the Universe, thereby reducing the washout by
several orders of magnitude. The second proposition is an alternative of first
which considers a quasi-degenerate mass spectrum for the singlet heavy
neutrinos, resulting into a larger order of lepton asymmetry that survives the
impact of strong washout to account for the observed BAU. The viable parameters
space, as obtained can be tested at present and future Lepton Flavour Violation
experiments {\it e.g.} MEG and MEG II.}
| arxiv topic:hep-ph |
arxiv_dataset-188552307.14505 | SPICE Modeling of Memcomputing Logic Gates
cs.ET cond-mat.dis-nn
Memcomputing logic gates generalize the traditional Boolean logic gates for
operation in the reverse direction. According to the literature, this
functionality enables the efficient solution of computationally-intensive
problems including factorization and NP-complete problems. To approach the
deployment of memcomputing gates in hardware, this paper introduces SPICE
models of memcomputing logic gates following their original definition. Using
these models, we demonstrate the behavior of single gates as well as small
self-organizing circuits. We also correct some inconsistencies in the prior
literature. Importantly, the correct schematics of dynamic correction module is
reported here for the first time. Our work makes memcomputing more accessible
to those who are interested in this emerging computing technology.
| arxiv topic:cs.ET cond-mat.dis-nn |
arxiv_dataset-188562307.14605 | Clustering based Point Cloud Representation Learning for 3D Analysis
cs.CV cs.AI
Point cloud analysis (such as 3D segmentation and detection) is a challenging
task, because of not only the irregular geometries of many millions of
unordered points, but also the great variations caused by depth, viewpoint,
occlusion, etc. Current studies put much focus on the adaption of neural
networks to the complex geometries of point clouds, but are blind to a
fundamental question: how to learn an appropriate point embedding space that is
aware of both discriminative semantics and challenging variations? As a
response, we propose a clustering based supervised learning scheme for point
cloud analysis. Unlike current de-facto, scene-wise training paradigm, our
algorithm conducts within-class clustering on the point embedding space for
automatically discovering subclass patterns which are latent yet representative
across scenes. The mined patterns are, in turn, used to repaint the embedding
space, so as to respect the underlying distribution of the entire training
dataset and improve the robustness to the variations. Our algorithm is
principled and readily pluggable to modern point cloud segmentation networks
during training, without extra overhead during testing. With various 3D network
architectures (i.e., voxel-based, point-based, Transformer-based, automatically
searched), our algorithm shows notable improvements on famous point cloud
segmentation datasets (i.e.,2.0-2.6% on single-scan and 2.0-2.2% multi-scan of
SemanticKITTI, 1.8-1.9% on S3DIS, in terms of mIoU). Our algorithm also
demonstrates utility in 3D detection, showing 2.0-3.4% mAP gains on KITTI.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-188572307.14705 | High Dynamic Range Imaging via Visual Attention Modules
cs.CV
Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography
has seen profound changes recently. To be more specific, such methods try to
reconstruct the lost luminosity of the real world caused by the limitation of
regular cameras from the Low Dynamic Range (LDR) images. Additionally, although
the State-Of-The-Art methods in this topic perform well, they mainly
concentrate on combining different exposures and have less attention to
extracting the informative parts of the images. Thus, this paper aims to
introduce a new model capable of incorporating information from the most
visible areas of each image extracted by a visual attention module (VAM), which
is a result of a segmentation strategy. In particular, the model, based on a
deep learning architecture, utilizes the extracted areas to produce the final
HDR image. The results demonstrate that our method outperformed most of the
State-Of-The-Art algorithms.
| arxiv topic:cs.CV |
arxiv_dataset-188582307.14805 | Linear Termination over N is Undecidable
cs.LO
Recently it was shown that it is undecidable whether a term rewrite system
can be proved terminating by a polynomial interpretation in the natural
numbers. In this paper we show that this is also the case when restricting the
interpretations to linear polynomials, as is often done in tools, and when only
considering single-rule rewrite systems. What is more, the new undecidability
proof is simpler than the previous one. We further show that polynomial
termination over the rationals/reals is undecidable.
| arxiv topic:cs.LO |
arxiv_dataset-188592307.14905 | Transition of convex core doubles from hyperbolic to Anti-de sitter
geometry
math.GT math.DG
Let $\Sigma$ be a surface of negative Euler characteristic, homeomorphic to a
closed surface, possibly with a finite number of points removed. In this paper,
we present a construction method for a wide range of examples of geometric
transition from hyperbolic to Anti-de Sitter structures via Half-pipe geometry
on $\Sigma\times\mathbb{S}^1$, with cone singularities along a link. The main
ingredient lies in studying the deformation of a convex core structure as the
bending laminations of the upper and lower boundary components of the convex
core uniformly collapse to zero.
| arxiv topic:math.GT math.DG |
arxiv_dataset-188602307.15005 | FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on
Lossy RI
cs.MM cs.DC
Light detection and ranging (LiDAR) sensors are becoming available on modern
mobile devices and provide a 3D sensing capability. This new capability is
beneficial for perceptions in various use cases, but it is challenging for
resource-constrained mobile devices to use the perceptions in real-time because
of their high computational complexity. In this context, edge computing can be
used to enable LiDAR online perceptions, but offloading the perceptions on the
edge server requires a low-latency, lightweight, and efficient compression due
to the large volume of LiDAR point clouds data.
This paper presents FLiCR, a fast and lightweight LiDAR point cloud
compression method for enabling edge-assisted online perceptions. FLiCR is
based on range images (RI) as an intermediate representation (IR), and
dictionary coding for compressing RIs. FLiCR achieves its benefits by
leveraging lossy RIs, and we show the efficiency of bytestream compression is
largely improved with quantization and subsampling. In addition, we identify
the limitation of current quality metrics for presenting the entropy of a point
cloud, and introduce a new metric that reflects both point-wise and
entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more
suitable for edge-assisted real-time perceptions than the existing LiDAR
compressions, and we demonstrate the effectiveness of our compression and
metric with the evaluations on 3D object detection and LiDAR SLAM.
| arxiv topic:cs.MM cs.DC |
arxiv_dataset-188612307.15105 | Detecting Morphing Attacks via Continual Incremental Training
cs.CV cs.LG
Scenarios in which restrictions in data transfer and storage limit the
possibility to compose a single dataset -- also exploiting different data
sources -- to perform a batch-based training procedure, make the development of
robust models particularly challenging. We hypothesize that the recent
Continual Learning (CL) paradigm may represent an effective solution to enable
incremental training, even through multiple sites. Indeed, a basic assumption
of CL is that once a model has been trained, old data can no longer be used in
successive training iterations and in principle can be deleted. Therefore, in
this paper, we investigate the performance of different Continual Learning
methods in this scenario, simulating a learning model that is updated every
time a new chunk of data, even of variable size, is available. Experimental
results reveal that a particular CL method, namely Learning without Forgetting
(LwF), is one of the best-performing algorithms. Then, we investigate its usage
and parametrization in Morphing Attack Detection and Object Classification
tasks, specifically with respect to the amount of new training data that became
available.
| arxiv topic:cs.CV cs.LG |
arxiv_dataset-188622307.15205 | A new robust graph for graph-based methods
stat.ME math.ST stat.TH
Graph-based two-sample tests and change-point detection are powerful tools
for analyzing high-dimensional and non-Euclidean data, as they do not impose
distributional assumptions and perform effectively across a wide range of
scenarios. These methods utilize a similarity graph constructed from the
observations, with $K$-nearest neighbor graphs or $K$-minimum spanning trees
being the current state-of-the-art choices. However, in high-dimensional
settings, these graphs tend to form hubs -- nodes with disproportionately large
degrees -- and graph-based methods are sensitive to hubs. To address this
issue, we propose a robust graph that is significantly less prone to forming
hubs in high-dimensional settings. Incorporating this robust graph can
substantially improve the power of graph-based methods across various
scenarios. Furthermore, we establish a theoretical foundation for graph-based
methods using the proposed robust graph, demonstrating its consistency under
fixed alternatives in both low-dimensional and high-dimensional contexts.
| arxiv topic:stat.ME math.ST stat.TH |
arxiv_dataset-188632307.15305 | Bursty Star Formation Naturally Explains the Abundance of Bright
Galaxies at Cosmic Dawn
astro-ph.GA
Recent discoveries of a significant population of bright galaxies at cosmic
dawn $\left(z \gtrsim 10\right)$ have enabled critical tests of cosmological
galaxy formation models. In particular, the bright end of the galaxy UV
luminosity function (UVLF) appears higher than predicted by many models. Using
approximately 25,000 galaxy snapshots at $8 \leq z \leq 12$ in a suite of
FIRE-2 cosmological "zoom-in'' simulations from the Feedback in Realistic
Environments (FIRE) project, we show that the observed abundance of UV-bright
galaxies at cosmic dawn is reproduced in these simulations with a multi-channel
implementation of standard stellar feedback processes, without any fine-tuning.
Notably, we find no need to invoke previously suggested modifications such as a
non-standard cosmology, a top-heavy stellar initial mass function, or a
strongly enhanced star formation efficiency. We contrast the UVLFs predicted by
bursty star formation in these original simulations to those derived from star
formation histories (SFHs) smoothed over prescribed timescales (e.g., 100 Myr).
The comparison demonstrates that the strongly time-variable SFHs predicted by
the FIRE simulations play a key role in correctly reproducing the observed,
bright-end UVLFs at cosmic dawn: the bursty SFHs induce order-or-magnitude
changes in the abundance of UV-bright ($M_\mathrm{UV} \lesssim -20$) galaxies
at $z \gtrsim 10$. The predicted bright-end UVLFs are consistent with both the
spectroscopically confirmed population and the photometrically selected
candidates. We also find good agreement between the predicted and
observationally inferred integrated UV luminosity densities, which evolve more
weakly with redshift in FIRE than suggested by some other models.
| arxiv topic:astro-ph.GA |
arxiv_dataset-188642307.15405 | Planar three-loop QCD helicity amplitudes for $V$+jet production at
hadron colliders
hep-ph hep-th
We compute the planar three-loop Quantum Chromodynamics (QCD) corrections to
the helicity amplitudes involving a vector boson $V=Z,W^\pm,\gamma^*$, two
quarks and a gluon. These amplitudes are relevant to vector-boson-plus-jet
production at hadron colliders and other precision QCD observables. The planar
corrections encompass the leading colour factors $N^3$, $N^2 N_f$, $N N_f^2$
and $N_f^3$. We provide the finite remainders of the independent helicity
amplitudes in terms of multiple polylogrithms, continued to all kinematic
regions and in a form which is compact and lends itself to efficient numerical
evaluation.
| arxiv topic:hep-ph hep-th |
arxiv_dataset-188652307.15505 | Exact intermittent solutions in a turbulence multi branch shell model
physics.flu-dyn
Reproducing complex phenomena with simple models marks our understanding of
the phenomena themselves and this is what Jack Herring's work demonstrated
multiple times. In that spirit, this work studies a turbulence shell model
consisting of a hierarchy of structures of different scales $\ell_n$ such that
each structure transfers its energy to two substructures of scale $\ell_{n+1} =
\ell_n /\lambda$. For this model we construct exact inertial range solutions
that display intermittency ie absence of self-similarity. Using a large
ensemble of these solutions we investigate how the probability distributions of
the velocity modes change with scale. It is demonstrated that while velocity
amplitudes are not scale invariant their ratios are. Furthermore using large
deviation theory we show how the probability distributions of the velocity
modes can be re-scaled to collapse in a scale independent form. Finally, we
discuss the implications the present results have for real turbulent flows.
| arxiv topic:physics.flu-dyn |
arxiv_dataset-188662307.15605 | Disproof of a conjecture on the minimum spectral radius and the
domination number
math.CO
Let $G_{n,\gamma}$ be the set of all connected graphs on $n$ vertices with
domination number $\gamma$. A graph is called a minimizer graph if it attains
the minimum spectral radius among $G_{n,\gamma}$. Very recently, Liu, Li and
Xie [Linear Algebra and its Applications 673 (2023) 233--258] proved that the
minimizer graph over all graphs in $\mathbb{G}_{n,\gamma}$ must be a tree.
Moreover, they determined the minimizer graph among
$G_{n,\lfloor\frac{n}{2}\rfloor}$ for even $n$, and posed the conjecture on the
minimizer graph among $G_{n,\lfloor\frac{n}{2}\rfloor}$ for odd $n$. In this
paper, we disprove the conjecture and completely determine the unique minimizer
graph among $G_{n,\lfloor\frac{n}{2}\rfloor}$ for odd $n$.
| arxiv topic:math.CO |
arxiv_dataset-188672307.15705 | Integral Field Spectroscopy of 13 Tidal Disruption Event Hosts from the
ZTF Survey
astro-ph.CO astro-ph.GA astro-ph.HE
The host galaxies of tidal disruption events (TDEs) have been shown to
possess peculiar properties, including high central light concentrations,
unusual star-formation histories, and ``green'' colors. The ubiquity of these
large-scale galaxy characteristics among TDE host populations suggests they may
serve to boost the TDE rate in such galaxies by influencing the nuclear stellar
dynamics. We present the first population study of integral field spectroscopy
for thirteen TDE host galaxies across all spectral classes and X-ray
brightnesses with the purpose of investigating their large-scale properties. We
derive the black hole masses via stellar kinematics (i.e., the $M-\sigma$
relation) and find masses in the range $5.0 \lesssim \log(M_{\rm BH}/M_\odot)
\lesssim 8.0$, with a distribution dominated by black holes with $M_{\rm BH}
\sim 10^6 M_\odot$. We find one object with $M_{\rm BH} \gtrsim 10^8 M_\odot$,
above the ``Hills mass'', which if the disrupted star was of solar type, allows
a lower limit of $a \gtrsim 0.16$ to be placed on its spin, lending further
support to the proposed connection between featureless TDEs and jetted TDEs. We
also explore the level of rotational support in the TDE hosts, quantified by
$(V/\sigma)_e$, a parameter which has been shown to correlate with stellar age
and may explain the peculiar host galaxy preferences of TDEs. We find that the
TDE hosts exhibit a broad range in $(V/\sigma)_e$ following a similar
distribution as E+A galaxies, which have been shown to be overrepresented among
TDE host populations.
| arxiv topic:astro-ph.CO astro-ph.GA astro-ph.HE |
arxiv_dataset-188682307.15805 | Equilibria and incentives for illiquid auction markets
q-fin.TR
We study a toy two-player game for periodic double auction markets to
generate liquidity. The game has imperfect information, which allows us to link
market spreads with signal strength. We characterize Nash equilibria in cases
with or without incentives from the exchange. This enables us to derive new
insights about price formation and incentives design. We show in particular
that without any incentives, the market is inefficient and does not lead to any
trade between market participants. We however prove that quadratic fees indexed
on each players half spread leads to a transaction and we propose a
quantitative value for the optimal fees that the exchange has to propose in
this model to generate liquidity.
| arxiv topic:q-fin.TR |
arxiv_dataset-188692307.15905 | Multi-view Sparse Laplacian Eigenmaps for nonlinear Spectral Feature
Selection
cs.LG
The complexity of high-dimensional datasets presents significant challenges
for machine learning models, including overfitting, computational complexity,
and difficulties in interpreting results. To address these challenges, it is
essential to identify an informative subset of features that captures the
essential structure of the data. In this study, the authors propose Multi-view
Sparse Laplacian Eigenmaps (MSLE) for feature selection, which effectively
combines multiple views of the data, enforces sparsity constraints, and employs
a scalable optimization algorithm to identify a subset of features that capture
the fundamental data structure. MSLE is a graph-based approach that leverages
multiple views of the data to construct a more robust and informative
representation of high-dimensional data. The method applies sparse
eigendecomposition to reduce the dimensionality of the data, yielding a reduced
feature set. The optimization problem is solved using an iterative algorithm
alternating between updating the sparse coefficients and the Laplacian graph
matrix. The sparse coefficients are updated using a soft-thresholding operator,
while the graph Laplacian matrix is updated using the normalized graph
Laplacian. To evaluate the performance of the MSLE technique, the authors
conducted experiments on the UCI-HAR dataset, which comprises 561 features, and
reduced the feature space by 10 to 90%. Our results demonstrate that even after
reducing the feature space by 90%, the Support Vector Machine (SVM) maintains
an error rate of 2.72%. Moreover, the authors observe that the SVM exhibits an
accuracy of 96.69% with an 80% reduction in the overall feature space.
| arxiv topic:cs.LG |
arxiv_dataset-188702307.16005 | Enhancing Object Detection in Ancient Documents with Synthetic Data
Generation and Transformer-Based Models
cs.CV
The study of ancient documents provides a glimpse into our past. However, the
low image quality and intricate details commonly found in these documents
present significant challenges for accurate object detection. The objective of
this research is to enhance object detection in ancient documents by reducing
false positives and improving precision. To achieve this, we propose a method
that involves the creation of synthetic datasets through computational
mediation, along with the integration of visual feature extraction into the
object detection process. Our approach includes associating objects with their
component parts and introducing a visual feature map to enable the model to
discern between different symbols and document elements. Through our
experiments, we demonstrate that improved object detection has a profound
impact on the field of Paleography, enabling in-depth analysis and fostering a
greater understanding of these valuable historical artifacts.
| arxiv topic:cs.CV |
arxiv_dataset-188712307.16105 | TMPNN: High-Order Polynomial Regression Based on Taylor Map
Factorization
cs.LG cs.NE
Polynomial regression is widely used and can help to express nonlinear
patterns. However, considering very high polynomial orders may lead to
overfitting and poor extrapolation ability for unseen data. The paper presents
a method for constructing a high-order polynomial regression based on the
Taylor map factorization. This method naturally implements multi-target
regression and can capture internal relationships between targets.
Additionally, we introduce an approach for model interpretation in the form of
systems of differential equations. By benchmarking on UCI open access datasets,
Feynman symbolic regression datasets, and Friedman-1 datasets, we demonstrate
that the proposed method performs comparable to the state-of-the-art regression
methods and outperforms them on specific tasks.
| arxiv topic:cs.LG cs.NE |
arxiv_dataset-188722307.16205 | Mesh Density Adaptation for Template-based Shape Reconstruction
cs.GR cs.CV
In 3D shape reconstruction based on template mesh deformation, a
regularization, such as smoothness energy, is employed to guide the
reconstruction into a desirable direction. In this paper, we highlight an often
overlooked property in the regularization: the vertex density in the mesh.
Without careful control on the density, the reconstruction may suffer from
under-sampling of vertices near shape details. We propose a novel mesh density
adaptation method to resolve the under-sampling problem. Our mesh density
adaptation energy increases the density of vertices near complex structures via
deformation to help reconstruction of shape details. We demonstrate the
usability and performance of mesh density adaptation with two tasks, inverse
rendering and non-rigid surface registration. Our method produces more accurate
reconstruction results compared to the cases without mesh density adaptation.
| arxiv topic:cs.GR cs.CV |
arxiv_dataset-188732307.16305 | Testing the first law of black hole mechanics with GW150914
gr-qc astro-ph.CO hep-ph hep-th
Whether the first law of black hole mechanics is correct is an important
question in black holes physics. Subjected to current limited gravitational
wave events, we propose its weaker version that permits a relatively large
perturbation to a black hole system and implement a simple test with the first
event GW150914. Confronting the strain data with the theory, we obtain the
constraint on the deviation parameter $\alpha=0.07\pm0.11$, which indicates
that this weaker version is valid at the 68\% confidence level. This result
implies that the first law of black hole mechanics may be correct.
| arxiv topic:gr-qc astro-ph.CO hep-ph hep-th |
arxiv_dataset-188742307.16405 | Causal-learn: Causal Discovery in Python
cs.LG stat.ME stat.ML
Causal discovery aims at revealing causal relations from observational data,
which is a fundamental task in science and engineering. We describe
$\textit{causal-learn}$, an open-source Python library for causal discovery.
This library focuses on bringing a comprehensive collection of causal discovery
methods to both practitioners and researchers. It provides easy-to-use APIs for
non-specialists, modular building blocks for developers, detailed documentation
for learners, and comprehensive methods for all. Different from previous
packages in R or Java, $\textit{causal-learn}$ is fully developed in Python,
which could be more in tune with the recent preference shift in programming
languages within related communities. The library is available at
https://github.com/py-why/causal-learn.
| arxiv topic:cs.LG stat.ME stat.ML |
arxiv_dataset-188752307.16505 | Emergence of stable meron quartets in twisted magnets
cond-mat.mes-hall cond-mat.str-el
The investigation of twist engineering in easy-axis magnetic systems has
revealed the remarkable potential for generating topological spin textures,
such as magnetic skyrmions. Here, by implementing twist engineering in
easy-plane magnets, we introduce a novel approach to achieve fractional
topological spin textures such as merons. Through atomistic spin simulations on
twisted bilayer magnets, we demonstrate the formation of a stable double meron
pair in two magnetic layers, which we refer to as the "Meron Quartet" (MQ).
Unlike merons in a single pair, which is unstable against pair annihilation,
the merons within the MQ exhibit exceptional stability against pair
annihilation due to the protective localization mechanism induced by the twist
that prevents the collision of the meron cores. Furthermore, we showcase that
the stability of the MQ can be enhanced by adjusting the twist angle, resulting
in increased resistance to external perturbations such as external magnetic
fields. Our findings highlight the twisted magnet as a promising platform for
investigating the intriguing properties of merons, enabling their realization
as stable magnetic quasiparticles in van der Waals magnets.
| arxiv topic:cond-mat.mes-hall cond-mat.str-el |
arxiv_dataset-188762307.16605 | VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive
Representation
cs.CV
Conditional 3D generation is undergoing a significant advancement, enabling
the free creation of 3D content from inputs such as text or 2D images. However,
previous approaches have suffered from low inference efficiency, limited
generation categories, and restricted downstream applications. In this work, we
revisit the impact of different 3D representations on generation quality and
efficiency. We propose a progressive generation method through Voxel-Point
Progressive Representation (VPP). VPP leverages structured voxel representation
in the proposed Voxel Semantic Generator and the sparsity of unstructured point
representation in the Point Upsampler, enabling efficient generation of
multi-category objects. VPP can generate high-quality 8K point clouds within
0.2 seconds. Additionally, the masked generation Transformer allows for various
3D downstream tasks, such as generation, editing, completion, and pre-training.
Extensive experiments demonstrate that VPP efficiently generates high-fidelity
and diverse 3D shapes across different categories, while also exhibiting
excellent representation transfer performance. Codes will be released at
\url{https://github.com/qizekun/VPP}.
| arxiv topic:cs.CV |
arxiv_dataset-188772307.16705 | Preserving Topology of Network Systems: Metric, Analysis, and Optimal
Design
eess.SY cs.MA cs.SY
Preserving the topology from being inferred by external adversaries has
become a paramount security issue for network systems (NSs), and adding random
noises to the nodal states provides a promising way. Nevertheless, recent works
have revealed that the topology cannot be preserved under i.i.d. noises in the
asymptotic sense. How to effectively characterize the non-asymptotic
preservation performance still remains an open issue. Inspired by the deviation
quantification of concentration inequalities, this paper proposes a novel
metric named trace-based variance-expectation ratio. This metric effectively
captures the decaying rate of the topology inference error, where a slower rate
indicates better non-asymptotic preservation performance. We prove that the
inference error will always decay to zero asymptotically, as long as the added
noises are non-increasing and independent (milder than the i.i.d. condition).
Then, the optimal noise design that produces the slowest decaying rate for the
error is obtained. More importantly, we amend the noise design by introducing
one-lag time dependence, achieving the zero state deviation and the non-zero
topology inference error in the asymptotic sense simultaneously. Extensions to
a general class of noises with multi-lag time dependence are provided.
Comprehensive simulations verify the theoretical findings.
| arxiv topic:eess.SY cs.MA cs.SY |
arxiv_dataset-188782307.16805 | Poincar\'{e} symmetries and representations in pseudo-Hermitian quantum
field theory
hep-th math-ph math.MP quant-ph
This paper explores quantum field theories with pseudo-Hermitian
Hamiltonians, where PT-symmetric Hamiltonians serve as a special case. In
specific regimes, these pseudo-Hermitian Hamiltonians have real eigenspectra,
orthogonal eigenstates, and unitary time evolution. So far, most
pseudo-Hermitian quantum field theories have been constructed using analytic
continuation or by adding non-Hermitian terms to otherwise Hermitian
Hamiltonians. However, in this paper, we take a different approach. We
construct pseudo-Hermitian scalar and fermionic quantum field theories from
first principles by extending the Poincar\'e algebra to include non-Hermitian
generators. This allows us to develop consistent pseudo-Hermitian quantum field
theories, with Lagrangian densities that transform appropriately under the
proper Poincar\'e group. By doing so, we establish a more solid theoretical
foundation for the emerging field of non-Hermitian quantum field theory.
| arxiv topic:hep-th math-ph math.MP quant-ph |
arxiv_dataset-188792308.00008 | Interpolation-Split: a data-centric deep learning approach with big
interpolated data to boost airway segmentation performance
eess.IV cs.LG
The morphology and distribution of airway tree abnormalities enables
diagnosis and disease characterisation across a variety of chronic respiratory
conditions. In this regard, airway segmentation plays a critical role in the
production of the outline of the entire airway tree to enable estimation of
disease extent and severity. In this study, we propose a data-centric deep
learning technique to segment the airway tree. The proposed technique utilises
interpolation and image split to improve data usefulness and quality. Then, an
ensemble learning strategy is implemented to aggregate the segmented airway
trees at different scales. In terms of segmentation performance (dice
similarity coefficient), our method outperforms the baseline model by 2.5% on
average when a combined loss is used. Further, our proposed technique has a low
GPU usage and high flexibility enabling it to be deployed on any 2D deep
learning model.
| arxiv topic:eess.IV cs.LG |
arxiv_dataset-188802308.00108 | DPBERT: Efficient Inference for BERT based on Dynamic Planning
cs.CL cs.AI
Large-scale pre-trained language models such as BERT have contributed
significantly to the development of NLP. However, those models require large
computational resources, making it difficult to be applied to mobile devices
where computing power is limited. In this paper we aim to address the weakness
of existing input-adaptive inference methods which fail to take full advantage
of the structure of BERT. We propose Dynamic Planning in BERT, a novel
fine-tuning strategy that can accelerate the inference process of BERT through
selecting a subsequence of transformer layers list of backbone as a
computational path for an input sample. To do this, our approach adds a
planning module to the original BERT model to determine whether a layer is
included or bypassed during inference. Experimental results on the GLUE
benchmark exhibit that our method reduces latency to 75\% while maintaining
98\% accuracy, yielding a better accuracy-speed trade-off compared to
state-of-the-art input-adaptive methods.
| arxiv topic:cs.CL cs.AI |
arxiv_dataset-188812308.00208 | Correlations between charge radii differences of mirror nuclei and
stellar observables
nucl-th astro-ph.SR nucl-ex
The correlation between the charge radii differences in mirror nuclei pairs
and the neutron skin thickness has been studied with the so-called finite range
simple effective interaction over a wide mass region. The so far precisely
measured charge radii difference data within their experimental uncertainty
ranges in the 34Ar-34S, 36Ca-36S, 38Ca-38Ar, and 54Ni-54Fe mirror pairs are
used to ascertain an upper limit for the slope parameter of the nuclear
symmetry energy L $\approx$ 100 MeV. This limiting value of L is found to be
consistent with the upper bound of the NICER PSR J0740+6620 constraint at
1$\sigma$ level for the radius R$_{1.4}$ of 1.4 M$_\odot$ neutron stars. The
lower bound of the NICER R$_{1.4}$ data constrains the lower limit of L to
$\approx$ 70 MeV. Within the range for L = 70-100 MeV the tidal deformability
$\Lambda^{1.4}$ constraint, which is extracted from the GW170817 event at
2$\sigma$ level, and the recent PREX-2 and CREX data on the neutron skin
thickness are discussed.
| arxiv topic:nucl-th astro-ph.SR nucl-ex |
arxiv_dataset-188822308.00308 | Thermodynamics and evaporation of perfect fluid dark matter black hole
in phantom background
gr-qc hep-th
We present a novel interpretation of the thermodynamics of perfect fluid dark
matter (PFDM) black hole based on Misner-Sharp energy, and then investigate its
evaporation behavior. We find that the ratio between dark sector initial
density and black hole horizon radius significantly influences black hole
evaporation behaviors. We demonstrate that the presence of the dark sector can
significantly extend the lifetime of a black hole which is similar to the
Reissner-Nordstrom case. Our work reformulates the thermodynamics of PFDM black
holes and points out the existence of long-lived black holes in the presence of
the dark sector.
| arxiv topic:gr-qc hep-th |
arxiv_dataset-188832308.00408 | Space Debris: Are Deep Learning-based Image Enhancements part of the
Solution?
eess.IV cs.CV physics.space-ph
The volume of space debris currently orbiting the Earth is reaching an
unsustainable level at an accelerated pace. The detection, tracking,
identification, and differentiation between orbit-defined, registered
spacecraft, and rogue/inactive space ``objects'', is critical to asset
protection. The primary objective of this work is to investigate the validity
of Deep Neural Network (DNN) solutions to overcome the limitations and image
artefacts most prevalent when captured with monocular cameras in the visible
light spectrum. In this work, a hybrid UNet-ResNet34 Deep Learning (DL)
architecture pre-trained on the ImageNet dataset, is developed. Image
degradations addressed include blurring, exposure issues, poor contrast, and
noise. The shortage of space-generated data suitable for supervised DL is also
addressed. A visual comparison between the URes34P model developed in this work
and the existing state of the art in deep learning image enhancement methods,
relevant to images captured in space, is presented. Based upon visual
inspection, it is determined that our UNet model is capable of correcting for
space-related image degradations and merits further investigation to reduce its
computational complexity.
| arxiv topic:eess.IV cs.CV physics.space-ph |
arxiv_dataset-188842308.00508 | Relational Contrastive Learning for Scene Text Recognition
cs.CV
Context-aware methods achieved great success in supervised scene text
recognition via incorporating semantic priors from words. We argue that such
prior contextual information can be interpreted as the relations of textual
primitives due to the heterogeneous text and background, which can provide
effective self-supervised labels for representation learning. However, textual
relations are restricted to the finite size of dataset due to lexical
dependencies, which causes the problem of over-fitting and compromises
representation robustness. To this end, we propose to enrich the textual
relations via rearrangement, hierarchy and interaction, and design a unified
framework called RCLSTR: Relational Contrastive Learning for Scene Text
Recognition. Based on causality, we theoretically explain that three modules
suppress the bias caused by the contextual prior and thus guarantee
representation robustness. Experiments on representation quality show that our
method outperforms state-of-the-art self-supervised STR methods. Code is
available at https://github.com/ThunderVVV/RCLSTR.
| arxiv topic:cs.CV |
arxiv_dataset-188852308.00608 | Explainable Cost-Sensitive Deep Neural Networks for Brain Tumor
Detection from Brain MRI Images considering Data Imbalance
cs.CV
This paper presents a research study on the use of Convolutional Neural
Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to
efficiently detect brain tumors in order to reduce the time required for manual
review of the report and create an automated system for classifying brain
tumors. An automated pipeline is proposed, which encompasses five models: CNN,
ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the
proposed architecture is evaluated on a balanced dataset and found to yield an
accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable
AI approaches are incorporated to visualize the model's latent behavior in
order to understand its black box behavior. To further optimize the training
process, a cost-sensitive neural network approach has been proposed in order to
work with imbalanced datasets which has achieved almost 4% more accuracy than
the conventional models used in our experiments. The cost-sensitive InceptionV3
(CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a
recall value of 1.00 respectively on an imbalanced dataset. The proposed models
have shown great potential in improving tumor detection accuracy and must be
further developed for application in practical solutions. We have provided the
datasets and made our implementations publicly available at -
https://github.com/shahariar-shibli/Explainable-Cost-Sensitive-Deep-Neural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images
| arxiv topic:cs.CV |
arxiv_dataset-188862308.00708 | VeriGen: A Large Language Model for Verilog Code Generation
cs.PL cs.LG cs.SE
In this study, we explore the capability of Large Language Models (LLMs) to
automate hardware design by generating high-quality Verilog code, a common
language for designing and modeling digital systems. We fine-tune pre-existing
LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We
evaluate the functional correctness of the generated Verilog code using a
specially designed test suite, featuring a custom problem set and testing
benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the
commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase.
Upon testing with a more diverse and complex problem set, we find that the
fine-tuned model shows competitive performance against state-of-the-art
gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41%
improvement in generating syntactically correct Verilog code across various
problem categories compared to its pre-trained counterpart, highlighting the
potential of smaller, in-house LLMs in hardware design automation.
| arxiv topic:cs.PL cs.LG cs.SE |
arxiv_dataset-188872308.00808 | Towards Climate Neutrality: A Comprehensive Overview of Sustainable
Operations Management, Optimization, and Wastewater Treatment Strategies
econ.GN q-fin.EC
Various studies have been conducted in the fields of sustainable operations
management, optimization, and wastewater treatment, yielding unsubstantiated
recovery. In the context of Europes climate neutrality vision, this paper
reviews effective decarbonization strategies and proposes sustainable
approaches to mitigate carbonization in various sectors such as building,
energy, industry, and transportation. The study also explores the role of
digitalization in decarbonization and reviews decarbonization policies that can
direct governments action towards a climate-neutral society. The paper also
presents a review of optimization approaches applied in the fields of science
and technology, incorporating modern optimization techniques based on various
peer-reviewed published research papers. It emphasizes non-conventional energy
and distributed power generating systems along with the deregulated and
regulated environment. Additionally, this paper critically reviews the
performance and capability of micellar enhanced ultrafiltration (MEUF) process
in the treatment of dye wastewater. The review presents evidence of
simultaneous removal of co-existing pollutants and explores the feasibility and
efficiency of biosurfactant in-stead of chemical surfactant. Lastly, the paper
proposes a novel firm-regulator-consumer interaction framework to study
operations decisions and interactive cooperation considering the interactions
among three agents through a comprehensive literature review on sustainable
operations management. The framework provides support for exploring future
research opportunities.
| arxiv topic:econ.GN q-fin.EC |
arxiv_dataset-188882308.00908 | Simulating Gaussian boson sampling quantum computers
quant-ph
A growing cohort of experimental linear photonic networks implementing
Gaussian boson sampling (GBS) have now claimed quantum advantage. However, many
open questions remain on how to effectively verify these experimental results,
as scalable methods are needed that fully capture the rich array of quantum
correlations generated by these photonic quantum computers. In this paper, we
briefly review recent theoretical methods to simulate experimental GBS
networks. We focus mostly on methods that use phase-space representations of
quantum mechanics, as these methods are highly scalable and can be used to
validate experimental outputs and claims of quantum advantage for a variety of
input states, ranging from the ideal pure squeezed vacuum state to more
realistic thermalized squeezed states. A brief overview of the theory of GBS,
recent experiments and other types of methods are also presented. Although this
is not an exhaustive review, we aim to provide a brief introduction to
phase-space methods applied to linear photonic networks to encourage further
theoretical investigations.
| arxiv topic:quant-ph |
arxiv_dataset-188892308.01008 | Operations on Milnor-Witt K-theory
math.AT math.AG math.KT
For all positive integers $n$ and all homotopy modules $M_*$, we define
certain operations $\underline{\operatorname{K}}^{\operatorname{MW}}_n
\rightarrow M_*$ and show that these generate the $M_*(k)$-module of all (in
general non-additive) operations
$\underline{\operatorname{K}}^{\operatorname{MW}}_n \rightarrow M_*$ in a
suitable sense, if $M_*$ is $\mathbb{N}$-graded and has a ring structure. This
also allows us to explicitly compute the abelian group
$\operatorname{Op}(\underline{\operatorname{K}}^{\operatorname{MW}}_n,\underline{\operatorname{K}}^{\operatorname{MW}}_m)$
and all operations between related theories such as Milnor, Witt and
Milnor-Witt K-theory.
| arxiv topic:math.AT math.AG math.KT |
arxiv_dataset-188902308.01108 | Hamiltonian formulation of gravity as a spontaneously-broken gauge
theory of the Lorentz group
gr-qc
A number of approaches to gravitation have much in common with the gauge
theories of the standard model of particle physics. In this paper, we develop
the Hamiltonian formulation of a class of gravitational theories that may be
regarded as spontaneously-broken gauge theories of the complexified Lorentz
group $SO(1,3)_C$ with the gravitational field described entirely by a gauge
field valued in the Lie algebra of $SO(1,3)_C$ and a `Higgs field' valued in
the group's fundamental representation. The theories have one free parameter
$\beta$ which appears in a similar role to the inverse of the Barbero-Immirzi
parameter of Einstein-Cartan theory. However, contrary to that parameter, it is
shown that the number of degrees of freedom crucially depends on the value of
$\beta$. For non-zero values of $\beta$, it is shown that three complex degrees
of freedom propagate on general backgrounds, and for the specific values
$\beta=\pm i$ an extension to General Relativity is recovered in a
symmetry-broken regime. For the value $\beta=0$, the theory propagates no local
degrees of freedom. A non-zero value of $\beta$ corresponds to the self-dual
and anti-self-dual gauge fields appearing asymmetrically in the action,
therefore in these models, the existence of gravitational degrees of freedom is
tied to chiral asymmetry in the gravitational sector.
| arxiv topic:gr-qc |
arxiv_dataset-188912308.01208 | Adaptive Collaborative Filtering with Personalized Time Decay Functions
for Financial Product Recommendation
cs.IR cs.LG q-fin.CP stat.ML
Classical recommender systems often assume that historical data are
stationary and fail to account for the dynamic nature of user preferences,
limiting their ability to provide reliable recommendations in time-sensitive
settings. This assumption is particularly problematic in finance, where
financial products exhibit continuous changes in valuations, leading to
frequent shifts in client interests. These evolving interests, summarized in
the past client-product interactions, see their utility fade over time with a
degree that might differ from one client to another. To address this challenge,
we propose a time-dependent collaborative filtering algorithm that can
adaptively discount distant client-product interactions using personalized
decay functions. Our approach is designed to handle the non-stationarity of
financial data and produce reliable recommendations by modeling the dynamic
collaborative signals between clients and products. We evaluate our method
using a proprietary dataset from BNP Paribas and demonstrate significant
improvements over state-of-the-art benchmarks from relevant literature. Our
findings emphasize the importance of incorporating time explicitly in the model
to enhance the accuracy of financial product recommendation.
| arxiv topic:cs.IR cs.LG q-fin.CP stat.ML |
arxiv_dataset-188922308.01308 | Masked and Swapped Sequence Modeling for Next Novel Basket
Recommendation in Grocery Shopping
cs.IR cs.LG
Next basket recommendation (NBR) is the task of predicting the next set of
items based on a sequence of already purchased baskets. It is a recommendation
task that has been widely studied, especially in the context of grocery
shopping. In next basket recommendation (NBR), it is useful to distinguish
between repeat items, i.e., items that a user has consumed before, and explore
items, i.e., items that a user has not consumed before. Most NBR work either
ignores this distinction or focuses on repeat items. We formulate the next
novel basket recommendation (NNBR) task, i.e., the task of recommending a
basket that only consists of novel items, which is valuable for both real-world
application and NBR evaluation. We evaluate how existing NBR methods perform on
the NNBR task and find that, so far, limited progress has been made w.r.t. the
NNBR task. To address the NNBR task, we propose a simple bi-directional
transformer basket recommendation model (BTBR), which is focused on directly
modeling item-to-item correlations within and across baskets instead of
learning complex basket representations. To properly train BTBR, we propose and
investigate several masking strategies and training objectives: (i) item-level
random masking, (ii) item-level select masking, (iii) basket-level all masking,
(iv) basket-level explore masking, and (v) joint masking. In addition, an
item-basket swapping strategy is proposed to enrich the item interactions
within the same baskets. We conduct extensive experiments on three open
datasets with various characteristics. The results demonstrate the
effectiveness of BTBR and our masking and swapping strategies for the NNBR
task. BTBR with a properly selected masking and swapping strategy can
substantially improve NNBR performance.
| arxiv topic:cs.IR cs.LG |
arxiv_dataset-188932308.01408 | UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text
using Transformer Ensembles
cs.CL
This paper describes the solutions submitted by the UPB team to the
AuTexTification shared task, featured as part of IberLEF-2023. Our team
participated in the first subtask, identifying text documents produced by large
language models instead of humans. The organizers provided a bilingual dataset
for this subtask, comprising English and Spanish texts covering multiple
domains, such as legal texts, social media posts, and how-to articles. We
experimented mostly with deep learning models based on Transformers, as well as
training techniques such as multi-task learning and virtual adversarial
training to obtain better results. We submitted three runs, two of which
consisted of ensemble models. Our best-performing model achieved macro
F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset.
| arxiv topic:cs.CL |
arxiv_dataset-188942308.01508 | Circumventing Concept Erasure Methods For Text-to-Image Generative
Models
cs.LG cs.CR cs.CV
Text-to-image generative models can produce photo-realistic images for an
extremely broad range of concepts, and their usage has proliferated widely
among the general public. On the flip side, these models have numerous
drawbacks, including their potential to generate images featuring sexually
explicit content, mirror artistic styles without permission, or even
hallucinate (or deepfake) the likenesses of celebrities. Consequently, various
methods have been proposed in order to "erase" sensitive concepts from
text-to-image models. In this work, we examine five recently proposed concept
erasure methods, and show that targeted concepts are not fully excised from any
of these methods. Specifically, we leverage the existence of special learned
word embeddings that can retrieve "erased" concepts from the sanitized models
with no alterations to their weights. Our results highlight the brittleness of
post hoc concept erasure methods, and call into question their use in the
algorithmic toolkit for AI safety.
| arxiv topic:cs.LG cs.CR cs.CV |
arxiv_dataset-188952308.01608 | Manifestation of topological phase in neutron spin rotation without
adiabatic regime
quant-ph
The Bitter-Dubbers (BD) experiment is an important experiment that originally
aimed to measure topological phase using polarized-neutron spin rotation in a
helical magnetic field under adiabatic conditions. Contrary to expectations,
upon reevaluation of the BD experiment, it has been found that adiabatic
conditions are not necessary for measuring topological phase. In scenarios
where the magnetic field is neither homogeneous nor strong enough, and the
neutron has a fast velocity, the topological phase can still be manifested. To
demonstrate this, we analytically solve the time-dependent Schrodinger equation
for the neutron spin rotation in general rotating systems. These exact
solutions are then utilized to investigate the nonadiabatic topological phase
under the conditions mentioned above. The numerical simulations of the
nonadiabatic topological phase have shown a strong concurrence with the BD
experimental data. This novel result extends our understanding of the
topological phase observed in neutron spin rotation, even in more complex and
dynamic scenarios beyond the originally required adiabatic conditions.
| arxiv topic:quant-ph |
arxiv_dataset-188962308.01708 | Local entanglement of electrons in 1D hydrogen molecule
quant-ph
The quantum entanglement entropy of the electrons in one-dimensional hydrogen
molecule is quantified locally using an appropriate partitioning of the
two-dimensional configuration space. Both the global and the local entanglement
entropy exhibit a monotonic increase when increasing the inter-nuclear
distance, while the local entropy remains peaked at the middle between the
nuclei with its width decreasing. Our findings show that at the inter-nuclear
distance where stable hydrogen molecule is formed, the quantum entropy shows no
peculiarity thus indicating that the entropy and the energy measures display
different sensitivity with respect to the interaction between the two identical
electrons involved. One possible explanation is that the calculation of the
quantum entropy does not account explicitly for the distance between the
nuclei, which contrasts to the total energy calculation where the energy
minimum depends decisively on that distance. The numerically exact and the
time-dependent quantum Monte Carlo calculations show close results.
| arxiv topic:quant-ph |
arxiv_dataset-188972308.01808 | Turbulence measurements in the neutral ISM from Hi-21 cm
emission-absorption spectra
astro-ph.GA
We study the correlation between the non-thermal velocity dispersion
($\sigma_{\rm nth}$) and the length-scale (L) in the neutral interstellar
medium (ISM) using a large number of Hi gas components taken from various
published Hi surveys and previous Hi studies. We notice that above the
length-scale ($L$) of 0.40 pc, there is a power-law relationship between
$\sigma_{\rm nth}$ and $L$. However, below 0.40 pc, there is a break in the
power-law, where $\sigma_{\rm nth}$ is not significantly correlated with $L$.
It has been observed from the Markov chain Monte Carlo (MCMC) method that for
the dataset of $L > 0.40$ pc, the most probable values of intensity ($A$) and
power-law index ($p$) are 1.14 and 0.55 respectively. Result of $p$ suggests
that the power-law is steeper than the standard Kolmogorov law of turbulence.
This is due to the dominance of clouds in the cold neutral medium. This is even
more clear when we separate the clouds into two categories: one for $L$ is >
0.40 pc and the kinetic temperature ($T_k$ ) is < 250 K, which are in the cold
neutral medium (CNM) and for other one where L is > 0.40 pc and T k is between
250 K and 5000 K, which are in the thermally unstable phase (UNM). Most
probable values of $A$ and $p$ are 1.14 and 0.67 respectively in the CNM phase
and 1.01 and 0.52 respectively in the UNM phase. A greater number of data
points is effective for the UNM phase in constructing a more accurate estimate
of $A$ and $p$, since most of the clouds in the UNM phase lie below 500 K.
However, from the value of $p$ in the CNM phase, it appears that there is a
significant difference from the Kolmogorov scaling, which can be attributed to
a shock-dominated medium.
| arxiv topic:astro-ph.GA |
arxiv_dataset-188982308.01908 | From Heegaard diagrams to surgery
math.GT
A procedure of going from Heegaard diagrams to framed link diagrams is
explained in this note.
| arxiv topic:math.GT |
arxiv_dataset-188992308.02008 | An equilibrated estimator for mixed finite element discretizations of
the curl-curl problem
math.NA cs.NA
We propose a new a posteriori error estimator for mixed finite element
discretizations of the curl-curl problem. This estimator relies on a
Prager--Synge inequality, and therefore leads to fully guaranteed constant-free
upper bounds on the error. The estimator is also locally efficient and
polynomial-degree-robust. The construction is based on patch-wise
divergence-constrained minimization problems, leading to a cheap embarrassingly
parallel algorithm. Crucially, the estimator operates without any assumption on
the topology of the domain, and unconventional arguments are required to
establish the reliability estimate. Numerical examples illustrate the key
theoretical results, and suggest that the estimator is suited for mesh
adaptivity purposes.
| arxiv topic:math.NA cs.NA |
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