Miracle-dz/UniFAR-FTUs huggingface dataset:
Holographic entanglement entropy and mutual information in deformed field theories at finite temperature

In this paper we have studied the effect of deformation and temperature on holographic entanglement entropy and mutual information between two subsystems in a deformed field theory at finite temperature. The $T{\overline{T}}$ deformation operator in field theory introduces a cut-off in the dual gravity background. We define different regimes of parameters and calculate the entanglement entropy and mutual information analytically. We observe that temperature and deformation parameter have similar effects on entanglement entropy and mutual information. They both lead to decrease in mutual information or somehow the correlation between the two subsystems. Interestingly we observe the emergence of correction terms in entanglement entropy and mutual information that are universal and do not depend on the scale of the non-local operator.
The 't Hooft equation as a quantum spectral curve

In an attempt to establish a link between different quantization methods, I examine the massless ’t Hooft equation, which governs meson bound state wavefunctions in 2d SU(N) gauge theory in the large-N limit. The integral equation can also be obtained by lightcone quantizing a folded string in flat space. The folded string is a limiting case of a more general setup: a four-segmented string moving in AdS3. I compute its classical spectral curve by using celestial variables and planar bipartite graphs (on-shell diagrams/brane tilings). The adjugate of the Kasteleyn matrix vanishes at two special points, which ensures that the string segments form a closed loop in target space. The Hamiltonian takes on a Ruijsenaars-Schneider form and the phase space is a coadjoint SL(2) orbit whose middle region has been removed. In AdS, the ’t Hooft equation acquires an extra term, which has previously been proposed as an effective confining potential in QCD. After an integral transform, the equation can be inverted in terms of a finite difference equation. I show that this difference equation can be interpreted as the quantized (non-analytic) spectral curve of the string. I calculate the spectrum numerically, which interpolates between ∆2 = n(n + 1) in the tensionless limit and ’t Hooft’s nearly linear Regge trajectory at infinite AdS radius. ar X iv :2 30 1. 07 15 4v 1 [ he pth ] 1 7 Ja n 20 23
The Complex X-Ray Obscuration Environment in the Radio-loud Type 2 Quasar 3C 223

3C 223 is a radio-loud, Type 2 quasar at z = 0.1365 with an intriguing X-ray Multi-mirror Mission (XMM)-Newton spectrum that implicated it as a rare, Compton-thick (N H ≳ 1.25 × 1024 cm−2) active galactic nucleus (AGN). We obtained contemporaneous XMM-Newton and Nuclear Spectroscopic Telescope Array (NuSTAR) spectra to fit the broadband X-ray spectrum with the physically motivated MYTorus and borus02 models. We confirm earlier results that the obscuring gas is patchy with both high (though not Compton-thick) levels of obscuration (N H > 1023 cm−2) and gas clouds with column densities up to an order of magnitude lower. The spectral fitting results indicate additional physical processes beyond those modeled in the spectral grids of MYTorus and borus02 impact the emergent spectrum: the Compton-scattering region may be extended beyond the putative torus; a ring of heavy Compton-thick material blocks most X-ray emission along the line of sight; or the radio jet is beamed, boosting the production of Fe Kα line photons in the global medium compared with what is observed along the line of sight. We revisit a recent claim that no radio-loud Compton-thick AGN have yet been conclusively shown to exist, finding three reported cases of radio-loud AGN with global average (but not line-of-sight) column densities that are Compton thick. Now that it is possible to separately determine line-of-sight and global column densities, inhomogeneity in the obscuring medium has consequences for how we interpet the spectrum and classify an AGN as “Compton thick. ”
Model-independent Evidence for an Increase in the Mean Mass of Cosmic Rays above 3 EeV

Measurements of the Elongation Rate of the depth of shower maximum above 1 EeV are reviewed. There is evidence, from four independent estimates of this rate, made in the two hemispheres using three different techniques, for a decrease in the Elongation Rate above ~3 EeV, as first discovered by the Pierre Auger Collaboration over 15 year ago. Unless there is a dramatic change in the hadronic physics above this energy, the mean mass of the primary cosmic rays must increase as a function of energy, well into the decade beyond 10 EeV. To estimate the mass, the use of hadronic models is required, the accuracy of which remains uncertain. However, the possibility of a dramatic change in the hadronic physics appears unlikely, and would be inconsistent with data from the Auger Collaboration on the mass composition in the range 3 to 10 EeV, and on the anisotropy of arrival directions above 8 EeV. Both of these conclusions are insensitive to uncertainties in the shower models. Some remarks are made about the belief, still firmly held by some, that the highest-energy cosmic rays are dominantly protons.
Bounded Model Checking for Asynchronous Hyperproperties

Many types of attacks on confidentiality stem from the nondeterministic nature of the environment that computer programs operate in (e.g., schedulers and asynchronous communication channels). In this paper, we focus on verification of confidentiality in nondeterministic environments by reasoning about asynchronous hyperproperties. First, we generalize the temporal logic A-HLTL to allow nested trajectory quantification, where a trajectory determines how different execution traces may advance and stutter. We propose a bounded model checking algorithm for A-HLTL based on QBF-solving for a fragment of the generalized A-HLTL and evaluate it by various case studies on concurrent programs, scheduling attacks, compiler optimization, speculative execution, and cache timing attacks. We also rigorously analyze the complexity of model checking for different fragments of A-HLTL.
Finite Parts of Certain Divergent Integrals and Their Dependence on Regularization Data

Let $X$ be a reduced complex space of pure dimension. We consider divergent integrals of certain forms on $X$ that are singular along a subvariety defined by the zero set of a holomorphic section of some holomorphic vector bundle $E \rightarrow X$. We consider two different regularizations of such integrals, both depending on a choice of smooth Hermitian metric on $E$. Given such a choice, for each of the two regularizations there is a natural way to define a finite part of the divergent integral, and we show that they coincide. Our main result is an explicit formula for the dependence on the choice of metric of the finite part.
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism

In this paper, Gated-ViGAT, an efficient approach for video event recognition, utilizing bottom-up (object) information, a new frame sampling policy and a gating mechanism is proposed. Specifically, the frame sampling policy uses weighted in-degrees (WiDs), derived from the adjacency matrices of graph attention networks (GATs), and a dissimilarity measure to select the most salient and at the same time diverse frames representing the event in the video. Additionally, the proposed gating mechanism fetches the selected frames sequentially, and commits early-exiting when an adequately confident decision is achieved. In this way, only a few frames are processed by the computationally expensive branch of our network that is responsible for the bottom-up information extraction. The experimental evaluation on two large, publicly available video datasets (MiniKinetics, ActivityNet) demonstrates that Gated-ViGAT provides a large computational complexity reduction in comparison to our previous approach (ViGAT), while maintaining the excellent event recognition and explainability performance1.
Almost optimal measurement scheduling of molecular Hamiltonian via finite projective plane

We propose an efficient and almost optimal scheme for measuring molecular Hamiltonians in quantum chemistry on quantum computers, which requires $2N^2$ distinct measurements in the leading order with $N$ being the number of molecular orbitals. It achieves the state-of-the-art by improving a previous proposal by Bonet-Monroig et al. [Phys. Rev. X 10, 031064 (2020)] which exhibits $\frac{10}{3}N^2$ scaling in the leading order. We develop a novel method based on a finite projective plane to construct sets of simultaneously-measurable operators contained in molecular Hamiltonians. Each measurement only requires a depth-$O(N)$ circuit consisting of $O(N^2)$ one- and two-qubit gates under the Jordan-Wigner and parity mapping, assuming the linear connectivity of qubits on quantum hardwares. Because evaluating expectation values of molecular Hamiltonians is one of the major bottlenecks in the applications of quantum devices to quantum chemistry, our finding is expected to accelerate such applications.
fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review

Functional magnetic resonance imaging (fMRI) has been widely utilized to study the motor deficits and rehabilitation following stroke. In particular, functional connectivity(FC) analyses with fMRI at rest can be employed to reveal the neural connectivity rationale behind this post-stroke motor function impairment and recovery. However, the methods and findings have not been summarized in a review focusing on post-stroke functional connectivity analysis. In this context, we broadly review the static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) for post-stroke motor dysfunction patients, aiming to provide method guides and the latest findings regarding post-stroke motor function recovery. Specifically, a brief overview of the SFC and DFC methods for fMRI analysis is provided, along with the preprocessing and denoising procedures that go into these methods. Following that, the current status of research in functional connectivity networks for post-stoke patients under these two views was synthesized individually. Results show that SFC is the most frequent post-stroke functional connectivity analysis method. The SFC findings demonstrate that the stroke lesion reduces FC between motor areas, and the FC increase positively correlates with functional recovery. Meanwhile, the current DFC analysis in post-stroke has just been uncovered as the tip of the iceberg of its prospect, and its exceptionally rapidly progressing development can be expected.
Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness

This paper reconsiders the On-Demand Multimodal Transit Systems (ODMTS) Design with Adoptions problem (ODMTS-DA) to capture the latent demand in on-demand multimodal transit systems. The ODMTS-DA is a bilevel optimization problem, for which Basciftci and Van Hentenryck proposed an exact combinatorial Benders decomposition. Unfortunately, their proposed algorithm only finds high-quality solutions for medium-sized cities and is not practical for large metropolitan areas. The main contribution of this paper is to propose a new path-based optimization model, called P-Path, to address these computational difficulties. The key idea underlying P-Path is to enumerate two specific sets of paths which capture the essence of the choice model associated with the adoption behavior of riders. With the help of these path sets, the ODMTS-DA can be formulated as a single-level mixed-integer programming model. In addition, the paper presents preprocessing techniques that can reduce the size of the model significantly. P-Path is evaluated on two comprehensive case studies: the midsize transit system of the Ann Arbor – Ypsilanti region in Michigan (which was studied by Basciftci and Van Hentenryck) and the large-scale transit system for the city of Atlanta. The experimental results show that P-Path solves the Michigan ODMTS-DA instances in a few minutes, bringing more than two orders of magnitude improvements compared with the existing approach. For Atlanta, the results show that P-Path can solve large-scale ODMTS-DA instances (about 17 millions variables and 37 millions constraints) optimally in a few hours or in a few days. These results show the tremendous computational benefits of P-Path which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand. History: Accepted by Andrea Lodi, Design & Analysis of Algorithms—Discrete. Funding: This work was partially supported by National Science Foundation Leap-HI [Grant 1854684] and the Tier 1 University Transportation Center (UTC): Transit - Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) from the U.S. Department of Transportation [69A3552047141]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0014 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0014 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github. io/ .
Horvitz-Thompson Estimators for Double-Platform Line Transect Surveys

SUMMARY Horvitz-Thompson estimators based on estimated inclusion probabilities are developed for line transect surveys in which some of the central assumptions of conventional line transect theory fail. The estimators are designed to accommodate uncertain detection of animals in the observers' path simultaneously with dependence of detection probabilities on explanatory variables other than perpendicular distance. They require that data be collected by two observer teams simultaneously searching the survey region, recording for each detection both positional data and details of any other variables that may affect detectability. One of the estimators is designed to correct for responsive animal movement. This method requires that the primary observer team searches independently of the secondary team, whereas the second estimator requires both observer teams to search independently of each other. The methods can be viewed as specific forms of mark-recapture experiments, in which individual capture probabilities are modelled as functions of observable explanatory variables and in which animals detected by both observer teams correspond to recaptures. They differ from existing mark-recapture Horvitz-Thompson estimators primarily in that they use an assumption about the distribution of perpendicular distances that is implicit in conventional
Pooling Under Misspecification: Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques

Two different methods for pooling time series of cross section data are used by economists. The first method, described by Kmenta, is based on the idea that pooled time series of cross sections are plagued with both heteroskedasticity and serial correlation.The second method, made popular by Balestra and Nerlove, is based on the error components procedure where the disturbance term is decomposed into a cross-section effect, a time-period effect, and a remainder.Although these two techniques can be easily implemented, they differ in the assumptions imposed on the disturbances and lead to different estimators of the regression coefficients. Not knowing what the true data generating process is, this article compares the performance of these two pooling techniques under two simple setting. The first is when the true disturbances have an error components structure and the second is where they are heteroskedastic and time-wise autocorrelated. First, the strengths and weaknesses of the two techniques are discussed. Next, the loss from applying the wrong estimator is evaluated by means of Monte Carlo experiments. Finally, a Bartletfs test for homoskedasticity and the generalized Durbin-Watson test for serial correlation are recommended for distinguishing between the two error structures underlying the two pooling techniques.
Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal

In observational study, the propensity score has the central role to estimate causal eﬀects. Since the propensity score is usually unknown, estimating by appropriate procedures is an indispensable step. A point to note that a causal eﬀect estimator might have some bias if a propensity score model was misspeciﬁed; valid model construction is important. To overcome the problem, a variety of interesting methods has been proposed. In this paper, we review four methods: using ordinary logistic regression approach; CBPS proposed by Imai and Ratkovic; boosted CART proposed by McCaﬀrey and colleagues; a semiparametric strategy proposed by Liu and colleagues. Also, we propose the novel robust two step strategy: estimating each candidate model in the ﬁrst step and integrating them in the second step. We conﬁrm the performance of these methods through simulation examples by estimating the ATE and ATO proposed by Li and colleagues. From the results of the simulation examples, the boosted CART and CBPS with higher-order balancing condition have good properties; both the estimate of the ATE and ATO has the small variance and the absolute value of bias. The boosted CART and CBPS are useful for a variety of estimands and estimating procedures.
A Distribution Free Truncated Kernel Ridge Regression Estimator and Related Spectral Analyses

It is well known that kernel ridge regression (KRR) is a popular nonparametric regression estimator. Nonetheless, in the presence of a large data set with size $n\gg 1,$ the KRR estimator has the drawback to require an intensive computational load. Recently, scalable KRR approaches have been proposed with the aims to reduce the computational complexity of the KRR, while maintaining its superb convergence rate. In this work, we study a new scalable KRR based approach for nonparametric regression. Our truncated kernel ridge regression (TKRR) approach is simple. It is based on substituting the full $n\times n$ random kernel or Gram matrix $B_n,$ associated with a Mercer's kernel $\mathbb K,$ by its main $n\times N$ sub-matrix $A_N,$ where usually $N \ll n. $ Also, we show that the TKRR works with $d-$dimensional random sampling data following an unknown probability law. To do so, we give a spectral analysis for the compact kernel integral operator, associated with a probability measure, different from its usual probability measure. This decay estimate is then extended to the decay of the tail of the trace of the associated random Gram matrix. A special interest is devoted to develop rules for the optimal choices of the involved truncation order $N$ and the value for regularization parameter $\lambda>0. $ The proposed rules are based on the behavior and the decay rate of the spectrum of the positive integral operator, associated with the kernel $\mathbb K. $ These optimal values of the parameters ensure that in terms of the empirical risk error, the TKRR and the full KRR estimators have the same optimal convergence rate. Finally, we provide the reader with some numerical simulations that illustrate the performance of our proposed TKRR estimator.
From creep to flow: Granular materials under cyclic shear

Granular materials such as sand, powders, and grains are omnipresent in daily life, industrial applications, and earth-science [1]. When unperturbed, they form stable structures that resemble the ones of other amorphous solids like metallic and colloidal glasses [2]. It is commonly conjectured that all these amorphous materials show a universal mechanical response when sheared slowly, i.e., to have an elastic regime, followed by yielding [3]. Here we use X-ray tomography to determine the microscopic dynamics of a cyclically sheared granular system in three dimensions. Independent of the shear amplitude $\Gamma$, the sample shows a cross-over from creep to diffusive dynamics, indicating that granular materials have no elastic response and always yield, in stark contrast to other glasses. The overlap function [4] reveals that at large $\Gamma$ yielding is a simple cross-over phenomenon, while for small $\Gamma$ it shows features of a first order transition with a critical point at $\Gamma\approx 0.1$ at which one finds a pronounced slowing down and dynamical heterogeneity. Our findings are directly related to the surface roughness of granular particles which induces a micro-corrugation to the potential energy landscape, thus creating relaxation channels that are absent in simple glasses. These processes must be understood for reaching an understanding of the complex relaxation dynamics of granular systems.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.
Everett’s Interpretation and Convivial Solipsism

I show how the quantum paradoxes occurring when we adopt a standard realist framework (or a framework in which the collapse implies a physical change of the state of the system) vanish if we abandon the idea that a measurement is related (directly or indirectly) to a physical change of state. In Convivial Solipsism, similarly to Everett’s interpretation, there is no collapse of the wave function. However, contrary to Everett’s interpretation, there is only one world. This also allows us to get rid of any non-locality and to provide a solution to the Wigner’s friend problem and its more recent versions.