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The growth of two-dimensional (2D) transition metal dichalcogenides using chemical vapor deposition has been an area of intense study, primarily due to the scalability requirements for potential device applications. One of the major challenges of such growths is the large-scale thickness variation of the grown film. To investigate the role of different growth parameters computationally, we use a 2D asymmetric simple-exclusion process (ASEP) model with open boundaries as an approximation to the dynamics of deposition on the coarse-grained lattice. The variations in concentration of particles (growth profiles) at the lattice sites in the grown film are studied as functions of parameters like injection and ejection rate of particles from the lattice, time of observation, and the right bias (difference between the hopping probabilities towards right and towards left) imposed by the carrier gas. In addition, the deposition rates at a given coarse-grained site is assumed to depend on the occupancy of that site. The effect of the maximum deposition rate, i.e., the deposition rate at a completely unoccupied site on the substrate, has been explored. The growth profiles stretch horizontally when either the evolution time or the right bias is increased. An increased deposition rate leads to a step-like profile, with the higher density region close to the left edge. In 3D, the growth profiles become more uniform with the increase in the height of the precursor with respect to the substrate surface. These results qualitatively agree with the experimental observations., Comment: 22 pages, 9 figures

The dynamics and detection possibility of a pseudo-FIMP (pFIMP) dark matter (DM) in the presence of a thermal DM have been studied in different contexts. The pFIMP phenomenology largely depends on the WIMP-like partner DM, as pFIMP interacts with the standard model (SM) particles only via the partner DM loop. Introducing a lepton portal interaction, which connects DM directly to the SM lepton sector, improves its detection prospects. However, such possibilities are constrained strongly by the non-observation of lepton flavor-violating decays. Interestingly, this also makes it possible to probe such models in future low-energy experiments. In this article, we have tried to establish such connections and find parameter space which respects the limits from DM relic, direct, indirect, and lepton flavor violation (LFV). We also recast the constraints from di-lepton/di-tau plus missing energy signal at the LHC on our model and provide projections for HL-LHC and future lepton colliders. Although the LFV and collider limits mainly concern WIMPs, the parameter space for pFIMPs is also constrained due to its strong connection to WIMPs through DM relic density and detection prospects., Comment: 26 pages, 13 figures, 3 tables

We present a theory of expected utility with state-dependent linear utility function for monetary returns, that includes results on first order stochastic dominance, mean-preserving spread, increasing-concave linear utility profiles and risk aversion. As an application of the expected utility theory developed here, we analyze the contract that a monopolist would offer in an insurance market that allowed for partial coverage of loss. We also define a utility function for monetary returns that in a certain sense reconciles state-dependent constant average utility of money with loss aversion and the Friedman-Savage hypothesis. As an immediate consequence of such a utility function, we obtain a profile of state-dependent linear utility functions for monetary returns, where states of nature correspond to mutually disjoint intervals in which monetary gains and losses may occur., Comment: 16 pages. Along with an earlier note on page 5, a new note on page 6 suggests a way of reconciling ambiguity aversion with expected utility and loss aversion

Nonprobability surveys suffer from representativeness issues due to their unknown selection mechanism. Recent research on nonprobability surveys has primarily focused on reducing such selection bias. But bias due to measurement error is also present, as pointed out by Kennedy, Mercer and Lau (2024) using a benchmarking study in the case of commercial online nonprobability surveys in the United States. Before this study, measurement error bias in nonprobability surveys has mostly been overlooked and statistical methods have been devised for reducing only the selection bias, under the assumption of accuracy of survey responses. Motivated by this case study, our research focuses on combining two key areas in nonprobability sampling research: representativeness and measurement error, specifically aiming to mitigate bias from both sampling and measurement errors. In the context of finite population mean estimation, we propose a new composite estimator that integrates both probability and nonprobability surveys, promising improved results compared to benchmark values from large government surveys. Its performance in comparison to an existing composite estimator is analyzed in terms of mean squared error, analytically and empirically. In the context of the aforementioned case study, we further investigate when the proposed composite estimator outperforms estimator from probability surveys alone.

Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle lies in the severe lack of data - there is much less proof than code for LLMs to train upon. In this paper, we introduce SAFE, a novel framework that overcomes the lack of human-written proof to enable automated proof generation of Rust code. SAFE establishes a self-evolving cycle where data synthesis and fine-tuning collaborate to enhance the model capability, leveraging the definitive power of a symbolic verifier in telling correct proof from incorrect ones. SAFE also re-purposes the large number of synthesized incorrect proofs to train the self-debugging capability of the fine-tuned models, empowering them to fix incorrect proofs based on the verifier's feedback. SAFE demonstrates superior efficiency and precision compared to GPT-4o. Through tens of thousands of synthesized proofs and the self-debugging mechanism, we improve the capability of open-source models, initially unacquainted with formal verification, to automatically write proof for Rust code. This advancement leads to a significant improvement in performance, achieving a 70.50% accuracy rate in a benchmark crafted by human experts, a significant leap over GPT-4o's performance of 24.46%.

Higher-order spectra (or polyspectra), defined as the Fourier Transform of a stationary process' autocumulants, are useful in the analysis of nonlinear and non Gaussian processes. Polyspectral means are weighted averages over Fourier frequencies of the polyspectra, and estimators can be constructed from analogous weighted averages of the higher-order periodogram (a statistic computed from the data sample's discrete Fourier Transform). We derive the asymptotic distribution of a class of polyspectral mean estimators, obtaining an exact expression for the limit distribution that depends on both the given weighting function as well as on higher-order spectra. Secondly, we use bispectral means to define a new test of the linear process hypothesis. Simulations document the finite sample properties of the asymptotic results. Two applications illustrate our results' utility: we test the linear process hypothesis for a Sunspot time series, and for the Gross Domestic Product we conduct a clustering exercise based on bispectral means with different weight functions.

We advance the theory of parametric bootstrap in constructing highly efficient empirical best (EB) prediction intervals of small area means. The coverage error of such a prediction interval is of the order $O(m^{-3/2})$, where $m$ is the number of small areas to be pooled using a linear mixed normal model. In the context of an area level model where the random effects follow a non-normal known distribution except possibly for unknown hyperparameters, we analytically show that the order of coverage error of empirical best linear (EBL) prediction interval remains the same even if we relax the normality of the random effects by the existence of pivot for a suitably standardized random effects when hyperpameters are known. Recognizing the challenge of showing existence of a pivot, we develop a simple moment-based method to claim non-existence of pivot. We show that existing parametric bootstrap EBL prediction interval fails to achieve the desired order of the coverage error, i.e. $O(m^{-3/2})$, in absence of a pivot. We obtain a surprising result that the order $O(m^{-1})$ term is always positive under certain conditions indicating possible overcoverage of the existing parametric bootstrap EBL prediction interval. In general, we analytically show for the first time that the coverage problem can be corrected by adopting a suitably devised double parametric bootstrap. Our Monte Carlo simulations show that our proposed single bootstrap method performs reasonably well when compared to rival methods.

In this paper we derive a second-order unbiased (or nearly unbiased) mean squared prediction error (MSPE) estimator of empirical best linear unbiased predictor (EBLUP) of a small area mean for a non-normal extension to the well-known Fay-Herriot model. Specifically, we derive our MSPE estimator essentially assuming certain moment conditions on both the sampling and random effects distributions. The normality-based Prasad-Rao MSPE estimator has a surprising robustness property in that it remains second-order unbiased under the non-normality of random effects when a simple method-of-moments estimator is used for the variance component and the sampling error distribution is normal. We show that the normality-based MSPE estimator is no longer second-order unbiased when the sampling error distribution is non-normal or when the Fay-Herriot moment method is used to estimate the variance component, even when the sampling error distribution is normal. It is interesting to note that when the simple method-of moments estimator is used for the variance component, our proposed MSPE estimator does not require the estimation of kurtosis of the random effects. Results of a simulation study on the accuracy of the proposed MSPE estimator, under non-normality of both sampling and random effects distributions, are also presented.

The ability to reliably transcribe child-adult conversations in a clinical setting is valuable for diagnosis and understanding of numerous developmental disorders such as Autism Spectrum Disorder. Recent advances in deep learning architectures and availability of large scale transcribed data has led to development of speech foundation models that have shown dramatic improvements in ASR performance. However, the ability of these models to translate well to conversational child-adult interactions is under studied. In this work, we provide a comprehensive evaluation of ASR performance on a dataset containing child-adult interactions from autism diagnostic sessions, using Whisper, Wav2Vec2, HuBERT, and WavLM. We find that speech foundation models show a noticeable performance drop (15-20% absolute WER) for child speech compared to adult speech in the conversational setting. Then, we employ LoRA on the best performing zero shot model (whisper-large) to probe the effectiveness of fine-tuning in a low resource setting, resulting in ~8% absolute WER improvement for child speech and ~13% absolute WER improvement for adult speech., Comment: 5 pages, 3 figures, 4 tables

Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLM to automatically generate correctness proof for Rust code. AutoVerus is designed to match the unique features of Verus, a verification tool that can prove the correctness of Rust code using proofs and specifications also written in Rust. AutoVerus consists of a network of LLM agents that are crafted and orchestrated to mimic human experts' three phases of proof construction: preliminary proof generation, proof refinement guided by generic tips, and proof debugging guided by verification errors. To thoroughly evaluate AutoVerus and help foster future research in this direction, we have built a benchmark suite of 150 non-trivial proof tasks, based on existing code-generation benchmarks and verification benchmarks. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.

Heavy-tailed distributions, such as the Cauchy distribution, are acknowledged for providing more accurate models for financial returns, as the normal distribution is deemed insufficient for capturing the significant fluctuations observed in real-world assets. Data sets characterized by outlier sensitivity are critically important in diverse areas, including finance, economics, telecommunications, and signal processing. This article addresses a goodness-of-fit test for the Cauchy distribution. The proposed test utilizes empirical likelihood methods, including the jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL). Extensive Monte Carlo simulation studies are conducted to evaluate the finite sample performance of the proposed test. The application of the proposed test is illustrated through the analysing two real data sets., Comment: 15 pages

Two new goodness of fit tests for the Pareto type-I distribution for complete and right censored data are proposed using fixed point characterization based on Steins type identity. The asymptotic distributions of the test statistics under both the null and alternative hypotheses are obtained. The performance of the proposed tests is evaluated and compared with existing tests through a Monte Carlo simulation experiment. The newly proposed tests exhibit greater power than existing tests for the Pareto type-I distribution. Finally, the methodology is applied to real-world data sets., Comment: 33 Pages

We theoretically propose the occurrence of a quantum spin Hall (QSH) and a second order topological phase transition (TPT) driven by electron-phonon (e-p) coupling in a pseudospin-$1$ fermionic system on an $\alpha$-$T_3$ lattice. Our model is formulated in the spirit of the Kane-Mele model modified by the Holstein Hamiltonian. The Lang-Firsov approach is employed to describe polarons reasonably well in the anti-adiabatic (high frequency) limit and to obtain an effective electronic Hamiltonian. It is shown that the system possesses topologically nontrivial phases up to a critical e-p coupling, $\lambda_c$ and are characterized by the helical QSH edge states along with a non-zero $\mathbb{Z}_2$ invariant for a certain range of $\alpha$. The topological phase vanishes beyond $\lambda_c$ and is accompanied by a bulk gap closing transition at $\lambda_c$, manifesting a TPT. We observe a more intriguing phenomenon for higher values of $\alpha$, where the system exhibits TPTs supported by two distinct gap closing transitions at $\lambda_{c_1}$ and $\lambda_{c_2}$, while a slim region at slightly lower values hosts a semi-metallic signature below $\lambda_{c_1}$. Subsequently, to explore more intricate features, we introduce a time reversal symmetry breaking magnetic field to trigger the formation of a second order topological phase. The magnetic field, by construction causes a boundary dependent gapping out of the edge states, consequently giving rise to robust corner modes in a tailored open boundary conditions. We justify the formation of the higher order phase by employing an appropriate invariant, namely the projected spin Chern number. Finally, we show that the e-p coupling significantly influences the corner modes (and also the real space energy bandstructure), corroborating a higher order TPT as we tune $\lambda$ beyond a critical value for a given value of $\alpha$., Comment: 18 pages, 18 figures. Comments are welcome

Our first result is a statement of a somewhat general form of a non-substitution theorem for linear programming problems, along with a very easy proof of the same. Subsequently, we provide an easy proof of theorem 1 in a 1979 paper by Olvi L Mangasarian based on a lemma that may be of some significance by itself. We also provide a simple proof of the result that states that the set of optimal solutions of a bounded linear optimization problem is the set of all convex combinations of its basic optimal solutions and the set of basic optimal solutions are the extreme points of the set of optimal solutions. We do so by appealing to the well-known lemma of Farkas and another well-known result that states that if a linear optimization problem has an optimal solution, it has at least one basic optimal solution. Both results we appeal to have easy proofs. We do not appeal to any version of the Klein-Milman Theorem or any result in advanced polyhedral combinatorics to obtain our results., Comment: This version includes a simple proof of the result that the set of optimal solutions of "bounded" LP problems is the convex hull of the set of basic optimal solutions, the latter being the set of extreme points of the set of optimal solutions. A possibly erroneous result and the section that contained it have been omitted. The total number of pages in the pdf file is 10

In this work, we examine the criteria for vacuum stability in two models with extended scalar sectors namely, the N2HDM and the 2HDMS and make a detailed comparison between the two. For the purpose of demonstration, we choose a scenario which can accommodate the recently observed 95 GeV excess in both models. We further explore the impact of the measurement of the Yukawa couplings, the gauge boson couplings and most importantly the trilinear self-couplings of the scalars, in distinguishing the vacuum structure in both models. We further investigate the constraints from vacuum stability on the 2HDMS scenario that accommodates a viable dark matter candidate and compare it with the N2HDM case., Comment: 38 pages, 9 figures

We provide an exact algorithm to solve the log-linear continuous (fractional) knapsack problem. The algorithm is based on two lemmas that follow from the application of weak duality theorem and complementary slackness theorem to the linear optimization problem with linear objective function that is associated with any solution of a linear optimization problem with (differentiable) concave objective function.

Road accidents are quite common in almost every part of the world, and, in majority, fatal accidents are attributed to over speeding of vehicles. The tendency to over speeding is usually tried to be controlled using check points at various parts of the road but not all traffic police have the device to check speed with existing speed estimating devices such as LIDAR based, or Radar based guns. The current project tries to address the issue of vehicle speed estimation with handheld devices such as mobile phones or wearable cameras with network connection to estimate the speed using deep learning frameworks., Comment: This is the pre-print of the paper which was accepted for oral presentation and publication in the proceedings of IEEE CONIT 2024, organized at Pune from June 21 to 23, 2024. The paper is 6 pages long and it contains 11 figures and 1 table. This is not the final version of the paper

In a typical e-commerce setting, Content Ranking Optimization (CRO) mechanisms are employed to surface content on the search page to fulfill customers' shopping missions. CRO commonly utilizes models such as contextual deep bandits model to independently rank content at different positions, e.g., one optimizer dedicated to organic search results and another to sponsored results. However, this regional optimization approach does not necessarily translate to whole page optimization, e.g., maximizing revenue at the top of the page may inadvertently diminish the revenue of lower positions. In this paper, we propose a reinforcement learning based method for whole page ranking to jointly optimize across all positions by: 1) shifting from position level optimization to whole page level optimization to achieve an overall optimized ranking; 2) applying reinforcement learning to optimize for the cumulative rewards instead of the instant reward. We formulate page level CRO as a cooperative Multi-agent Markov Decision Process , and address it with the novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) model. MADDPG supports a flexible and scalable joint optimization framework by adopting a "centralized training and decentralized execution" approach. Extensive experiments demonstrate that MADDPG scales to a 2.5 billion action space in the public Mujoco environment, and outperforms the deep bandits modeling by 25.7% on the offline CRO data set from a leading e-commerce company. We foresee that this novel multi-agent optimization is applicable to similar joint optimization problems in the field of information retrieval., Comment: 14 pages

A relativistic action for scalar condensate-fermion mixture is considered where both the scalar boson and the fermion fields are coupled to a $U(1)$ gauge field. The dynamics of the gauge field is governed by a linear combination of the Maxwell term and the Lorentz invariant $\mathbf{E\cdot B}$ term with a constant coefficient $\theta$. We obtain an effective action describing an emergent fermion-fermion interaction and fermion-vortex tube interaction by using the particle-string duality, and find that the $\theta$ term can significantly affect the interaction of fermions and vortices. We also perform a dimensional reduction to show a $\theta$ dependent flux attachment to the itinerant fermions.

Group Theory has become an invaluable tool in the physics community. Despite numerous introductory books, the subject remains challenging for beginners. Mathematica has emerged as a popular tool for research and education, offering various packages and built-in tools for Group Theory. However, these resources are often too scattered for effective educational use. This work aims to provide a comprehensive source to help beginning students grasp Group Theory concepts and their applications from a physicist's perspective, while also building familiarity with symbolic language. We present several example notebooks that succinctly cover well-known theories and demonstrate specific concepts, which can be easily adapted for educational purposes. We provide basic examples on finite, compact and non-compact groups, and motivate the use of these concepts in solving physics problems such as addition of angular momenta, modelling a system of qubits and the description of spacetime transformations., Comment: 110 pages

The coexistence of Ferromagnetism and superconductivity in so called ferromagnetic superconductors is an intriguing phenomenon which may lead to novel physical effects as well as applications. Here in this work we have explored the interplay of topological excitations, namely vortices and skyrmions, in ferromagnetic superconductors using a field theoretic description of such systems. In particular, numerical solutions for the continuous spin field compatible to a given vortex profile are determined in absence and presence of a Dzyaloshinskii-Moriya interaction (DMI) term. The solutions show that the spin configuration is like a skyrmion but intertwined with the vortex structure -- the radius of the the skyrmion-like solution depends on the penetration depth and also the polarity of the skyrmion depends on the sign of the winding number. Thus our solution describes a novel topological structure -- namely a skyrmion-vortex composite. We have also determined the spin wave solutions in such systems in presence and absence of a vortex. In absence of vortex frequency and wave vector satisfy a cubic equation which leads to various interesting features. In particular, we have shown that in the low frequency regime the minimum in dispersion relation shifts from $k=0$ to a non zero $k$ value depending on the parameters. We also discuss the nature of spin wave dispersion in the $\omega \sim \Tilde{m}$ regime which shows a similar pattern in the dispersion curve. The group velocity of the spin wave would change it's sign across such a minimum which is unique to FMSC. Also, the spin wave modes around the local minimum looks like roton mode in superfluid and hence called a magnetic roton. In presence of a vortex, the spin wave amplitude is shown to vary spatially such that the profile looks like that of a N\'eel Skyrmion. Possible experimental signature of both solutions are also discussed.

We systematically investigate the topological and localization properties of a quadrupolar insulator represented by the celebrated Benalcazar-Bernevig-Hughes model in presence of a quasiperiodic disorder instilled in its hopping amplitude. While disorder can be detrimental to the existence of the topological order in a system, we observe the emergence of a disorder driven topological phase where the original (clean) system demonstrates trivial behavior. This phenomenon is confirmed by the re-emergence of zero energy states in the bandstructure together with a non-zero bulk quadrupole moment, which in turn establishes the bulk boundary correspondence (BBC). Furthermore, the distribution of the excess electronic charge shows a pattern that is reminiscent of the bulk quadrupole topology. To delve into the localization properties of the mid-band states, we compute the inverse participation and normalized participation ratios. It is observed that the in-gap states become critical (multifractal) at the point that discerns a transition from a topological localized to a trivial localized phase. Finally, we carry out a similar investigation to ascertain the effect of the quasiperiodic disorder on the quadrupolar insulator when the model exhibits topological properties in the absence of disorder. Again, we note a multifractal behavior of the eigenstates in the vicinity of the transition., Comment: 9 pages, 14 figures

An st-shortest path, or st-path for short, in a graph G is a shortest (induced) path from s to t in G. Two st-paths are said to be adjacent if they differ on exactly one vertex. A reconfiguration sequence between two st-paths P and Q is a sequence of adjacent st-paths starting from P and ending at Q. Deciding whether there exists a reconfiguration sequence between two given $st$-paths is known to be PSPACE-complete, even on restricted classes of graphs such as graphs of bounded bandwidth (hence pathwidth). On the positive side, and rather surprisingly, the problem is polynomial-time solvable on planar graphs. In this paper, we study the parameterized complexity of the Shortest Path Reconfiguration (SPR) problem. We show that SPR is W[1]-hard parameterized by k + \ell, even when restricted to graphs of bounded (constant) degeneracy; here k denotes the number of edges on an st-path, and \ell denotes the length of a reconfiguration sequence from P to Q. We complement our hardness result by establishing the fixed-parameter tractability of SPR parameterized by \ell and restricted to nowhere-dense classes of graphs. Additionally, we establish fixed-parameter tractability of SPR when parameterized by the treedepth, by the cluster-deletion number, or by the modular-width of the input graph.

Verification-aware programming languages such as Dafny and F* provide means to formally specify and prove properties of a program. Although the problem of checking an implementation against a specification can be defined mechanically, there is no algorithmic way of ensuring the correctness of the {\it user-intent formalization for programs}, expressed as a formal specification. This is because intent or requirement is expressed {\it informally} in natural language and the specification is a formal artefact. Despite, the advent of large language models (LLMs) has made tremendous strides bridging the gap between informal intent and formal program implementations recently, driven in large parts by benchmarks and automated metrics for evaluation. Recent work has proposed a framework for evaluating the {\it user-intent formalization} problem for mainstream programming languages~\cite{endres-fse24}. However, such an approach does not readily extend to verification-aware languages that support rich specifications (using quantifiers and ghost variables) that cannot be evaluated through dynamic execution. Previous work also required generating program mutants using LLMs to create the benchmark. We advocate an alternate, perhaps simpler approach of {\it symbolically testing specifications} to provide an intuitive metric for evaluating the quality of specifications for verification-aware languages. We demonstrate that our automated metric agrees closely on a human-labeled dataset of Dafny specifications for the popular MBPP code-generation benchmark, yet demonstrates cases where the human labeling is not perfect. We also outline formal verification challenges that need to be addressed to apply the technique more widely. We believe our work provides a stepping stone to enable the establishment of a benchmark and research agenda for the problem of user-intent formalization for programs., Comment: Proceedings of the 24th Conference on Formal Methods in Computer Aided Design (FMCAD 2024)

We present a quantum interference phenomenon in which four-photon quantum states generated by two independent sources are used to create a two-photon interference pattern without detecting two of the photons. Contrary to the common perception, the interference pattern can be made fully independent of phases acquired by the photons detected to construct it. However, it still contains information about spatially dependent phases acquired by the two undetected photons. This phenomenon can also be observed with fermionic particles. We show that the phenomenon can be applied to develop an interferometric, quantum phase imaging technique that is immune to uncontrollable phase fluctuations in the interferometer and allows image acquisition without detecting the photons illuminating the object., Comment: 4 figures, 9 pages

Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.

The interaction of fermion spin with spacetime can be non-universal, leading to a new interaction beyond the Standard Model, independent of gravitation. Fermions generate spacetime torsion, which can be integrated out in favor of a four-fermion interaction in a torsion-free background. This is a current-current interaction which involves all fermions and generically has different coupling constants for different chiralities and species of fermions. It does not vanish when curvature goes to zero, so accelerator experiments should be able to see its effect. We calculate the contribution of this geometrical interaction to parity nonconservation in $e^-e^-$ and $e^-D$ scattering and compare with known observations. This provides an estimate of an upper bound on the coupling constants, suggesting that the strength of the ``new physics'' can be as large as only one order of magnitude smaller than that of weak interactions.

Individual human recognition is important for species that live in close proximity to humans. Numerous studies on domesticated species and urban-adapted birds have highlighted this ability. One such species which is heavily reliant on humans is the free-ranging dog. Very little knowledge exists on the amount of time taken by free-ranging dogs to learn and remember individual humans. Due to their territorial nature, they have a high probability of encountering the same people multiple times on the streets. Being able to distinguish individual humans might be helpful in making decisions regarding people from whom to beg for food or social reward. We investigated if free-ranging dogs are capable of identifying the person rewarding them and the amount of time required for them to learn it. We conducted field trials on randomly selected adult free-ranging dogs in West Bengal, India. On Day 1, a choice test was conducted. The experimenter chosen did not provide reward while the other experimenter provided a piece of boiled chicken followed by petting. The person giving reward on Day 1 served as the correct choice on four subsequent days of training. Day 6 was the test day when none of the experimenters had a reward. We analyzed the choice made by the dogs, the time taken to approach during the choice tests, and the socialization index, which was calculated based on the intensity of affiliative behaviour shown towards the experimenters. The dogs made correct choices at a significantly higher rate on the fifth and sixth days, as compared to Day 2, suggesting learning. This is the first study aiming to understand the time taken for individual human recognition in free-ranging dogs and can serve as the scaffold for future studies to understand the dog-human relationship in open environments, like urban ecosystems.

Electronic Discovery (eDiscovery) involves identifying relevant documents from a vast collection based on legal production requests. The integration of artificial intelligence (AI) and natural language processing (NLP) has transformed this process, helping document review and enhance efficiency and cost-effectiveness. Although traditional approaches like BM25 or fine-tuned pre-trained models are common in eDiscovery, they face performance, computational, and interpretability challenges. In contrast, Large Language Model (LLM)-based methods prioritize interpretability but sacrifice performance and throughput. This paper introduces DISCOvery Graph (DISCOG), a hybrid approach that combines the strengths of two worlds: a heterogeneous graph-based method for accurate document relevance prediction and subsequent LLM-driven approach for reasoning. Graph representational learning generates embeddings and predicts links, ranking the corpus for a given request, and the LLMs provide reasoning for document relevance. Our approach handles datasets with balanced and imbalanced distributions, outperforming baselines in F1-score, precision, and recall by an average of 12%, 3%, and 16%, respectively. In an enterprise context, our approach drastically reduces document review costs by 99.9% compared to manual processes and by 95% compared to LLM-based classification methods, Comment: 8 pages, 2 tables, 6 figures

In this work, we simulate the transition to turbulence in the pipe flow based on the modified NS theory incorporating the viscous fluid strength in the constitutive equations. The latter concept enriches theory by allowing for material instabilities in addition to the kinematic ones. We present results of comparative numerical simulations based on the classical NS model and the NS model enhanced with the finite viscous strength. As expected, simulations based on the classical NS model exhibit stable laminar flow in contrast to experimental observations. Conversely, simulations based on the modified NS model with viscous strength exhibit instabilities and transition to turbulence per experimental observations. The transition to turbulence is triggered by the growing material instabilities.

Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiability Modulo Theories (SMT) solvers to automate proofs in languages such as F*. Seeking to spur research on using AI to automate the construction of proof-oriented programs, we curate a dataset of 600K lines of open-source F* programs and proofs, including software used in production systems ranging from Windows and Linux to Python and Firefox. Our dataset includes around 32K top-level F* definitions, each representing a type-directed program and proof synthesis problem producing a definition given a formal specification expressed as an F* type. We provide a program fragment checker that queries F* to check the correctness of candidate solutions. We also report on an extended version of our dataset containing a total of 940K lines of programs and proofs, with a total of 54k top-level F* definitions. We believe this is the largest corpus of SMT-assisted program proofs coupled with a reproducible program-fragment checker. Grounded in this dataset, we investigate the use of AI to synthesize programs and their proofs in F*, with promising results. Our main finding in that the performance of fine-tuned smaller language models (such as Phi-2 or StarCoder) compare favorably with large language models (such as GPT-4), at a much lower computational cost. We also identify various type-based retrieval augmentation techniques and find that they boost performance significantly. With detailed error analysis and case studies, we identify potential strengths and weaknesses of models and techniques and suggest directions for future improvements., Comment: 47th International Conference on Software Engineering

We study the coscattering mechanism in a simple Higgs portal which add two real singlet scalars to the Standard Model. In this scenario, the lighter scalar is stabilized by a single $\mathcal{Z}_2$ symmetry and acts as the dark matter relic, whose freeze-out is driven by conversion processes. The heavier scalar becomes an unstable state which participate actively in the coscattering. We find viable parameter regions fulfilling the measured relic abundance, while evading direct detection and big-bang nucleosynthesis bounds. In addition, we discuss collider prospects for the heavier scalar as a long-lived particle at present and future detectors.

Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant features with respect to a protected variable (e.g., race when classifying sex from face images) or enforce CI relative to the protected attribute only on the model output (e.g., the sex label). Neither of these methods are effective in enforcing CI in high-dimensional feature spaces. In this paper, we focus on a nascent approach characterizing the CI constraint in terms of two Jensen-Shannon divergence terms, and we extend it to high-dimensional feature spaces using a novel dynamic sampling strategy. In doing so, we introduce a new training paradigm that can be applied to any encoder architecture. We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces. Our experimental results demonstrate that our approach can achieve high accuracy on downstream tasks while upholding equality of odds., Comment: To appear at the 2024 IEEE CVPR Workshop on Fair, Data-Efficient, and Trusted Computer Vision

Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs into efficient parsing logic in low-level, memory unsafe languages. Several researchers have proposed formal specification languages for data formats from which efficient code can be extracted. However, distilling informal requirements into formal specifications is challenging and, despite their benefits, new, formal languages are hard for people to learn and use. In this work, we present 3DGen, a framework that makes use of AI agents to transform mixed informal input, including natural language documents (i.e., RFCs) and example inputs into format specifications in a language called 3D. To support humans in understanding and trusting the generated specifications, 3DGen uses symbolic methods to also synthesize test inputs that can be validated against an external oracle. Symbolic test generation also helps in distinguishing multiple plausible solutions. Through a process of repeated refinement, 3DGen produces a 3D specification that conforms to a test suite, and which yields safe, efficient, provably correct, parsing code in C. We have evaluated 3DGen on 20 Internet standard formats, demonstrating the potential for AI-agents to produce formally verified C code at a non-trivial scale. A key enabler is the use of a domain-specific language to limit AI outputs to a class for which automated, symbolic analysis is tractable.

Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, given NL is informal, it does not lend easily to checking that the generated code correctly satisfies the user intent. In this paper, we propose a novel interactive workflow TiCoder for guided intent clarification (i.e., partial formalization) through tests to support the generation of more accurate code suggestions. Through a mixed methods user study with 15 programmers, we present an empirical evaluation of the effectiveness of the workflow to improve code generation accuracy. We find that participants using the proposed workflow are significantly more likely to correctly evaluate AI generated code, and report significantly less task-induced cognitive load. Furthermore, we test the potential of the workflow at scale with four different state-of-the-art LLMs on two python datasets, using an idealized proxy for a user feedback. We observe an average absolute improvement of 45.97% in the pass@1 code generation accuracy for both datasets and across all LLMs within 5 user interactions, in addition to the automatic generation of accompanying unit tests., Comment: IEEE Transactions on Software Engineering, vol. 50, no. 09, pp. 2254-2268, 2024

Managing the limited energy on mobile platforms executing long-running, resource intensive streaming applications requires adapting an application's operators in response to their power consumption. For example, the frame refresh rate may be reduced if the rendering operation is consuming too much power. Currently, predicting an application's power consumption requires (1) building a device-specific power model for each hardware component, and (2) analyzing the application's code. This approach can be complicated and error-prone given the complexity of an application's logic and the hardware platforms with heterogeneous components that it may execute on. We propose eScope, an alternative method to directly estimate power consumption by each operator in an application. Specifically, eScope correlates an application's execution traces with its device-level energy draw. We implement eScope as a tool for Android platforms and evaluate it using workloads on several synthetic applications as well as two video stream analytics applications. Our evaluation suggests that eScope predicts an application's power use with 97% or better accuracy while incurring a compute time overhead of less than 3%.

The Bernevig-Hughes-Zhang (BHZ) model, which serves as a cornerstone in the study of the quantum spin Hall insulators, showcases robust spin-filtered helical edge states in a nanoribbon geometry. In the presence of an in-plane magnetic field, these (first-order) helical states gap out to be replaced by second-order corner states under suitable open-boundary conditions. Here, we show that the inclusion of a spin-dependent non-Hermitian balanced gain/loss potential induces a competition between these first and second-order topological phases. Surprisingly, the previously dormant first-order helical edge states in the nanoribbon resurface as the non-Hermitian effect intensifies, effectively neutralizing the role played by the magnetic field. By employing the projected spin spectra and the spin Chern number, we conclusively explain the resurgence of the first-order topological properties in the time-reversal symmetry-broken BHZ model in presence of non-Hermiticity. Finally, the biorthogonal spin-resolved Berry phase, exhibiting a non-trivial winding, definitively establishes the topological nature of these revived edge states, emphasizing the dominance of non-Hermiticity over the magnetic field., Comment: Published in Physical Review B

Nested error regression models are commonly used to incorporate observational unit specific auxiliary variables to improve small area estimates. When the mean structure of this model is misspecified, there is generally an increase in the mean square prediction error (MSPE) of Empirical Best Linear Unbiased Predictors (EBLUP). Observed Best Prediction (OBP) method has been proposed with the intent to improve on the MSPE over EBLUP. We conduct a Monte Carlo simulation experiment to understand the effect of mispsecification of mean structures on different small area estimators. Our simulation results lead to an unexpected result that OBP may perform very poorly when observational unit level auxiliary variables are used and that OBP can be improved significantly when population means of those auxiliary variables (area level auxiliary variables) are used in the nested error regression model or when a corresponding area level model is used. Our simulation also indicates that the MSPE of OBP in an increasing function of the difference between the sample and population means of the auxiliary variables.

This paper describes Toronto Metropolitan University's participation in the TREC Clinical Trials Track for 2023. As part of the tasks, we utilize advanced natural language processing techniques and neural language models in our experiments to retrieve the most relevant clinical trials. We illustrate the overall methodology, experimental settings, and results of our implementation for the run submission as part of Team - V-TorontoMU.