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Despite advancements in Text-to-Video (T2V) generation, producing videos with realistic motion remains challenging. Current models often yield static or minimally dynamic outputs, failing to capture complex motions described by text. This issue stems from the internal biases in text encoding, which overlooks motions, and inadequate conditioning mechanisms in T2V generation models. To address this, we propose a novel framework called DEcomposed MOtion (DEMO), which enhances motion synthesis in T2V generation by decomposing both text encoding and conditioning into content and motion components. Our method includes a content encoder for static elements and a motion encoder for temporal dynamics, alongside separate content and motion conditioning mechanisms. Crucially, we introduce text-motion and video-motion supervision to improve the model's understanding and generation of motion. Evaluations on benchmarks such as MSR-VTT, UCF-101, WebVid-10M, EvalCrafter, and VBench demonstrate DEMO's superior ability to produce videos with enhanced motion dynamics while maintaining high visual quality. Our approach significantly advances T2V generation by integrating comprehensive motion understanding directly from textual descriptions. Project page: https://PR-Ryan.github.io/DEMO-project/, Comment: Accepted at NeurIPS 2024, code available at https://github.com/PR-Ryan/DEMO

This paper presents a topology optimization approach to design 2D contact-aided compliant mechanisms (CCMs) that can trace the desired output paths with more than one kink while experiencing self and/or external contacts. Such CCMs can be used as mechanical compliant switches. Hexagonal elements are used to parameterize the design domain. Negative circular masks are employed to remove material beneath them and generate rigid contact surfaces. Each mask is assigned five design variables. The first three decide the location and radius of the mask, whereas the last two determine the presence of the contact surface and its radius. To ensure continuity in contacting surfaces' normal, we employ a boundary smoothing scheme. The augmented Lagrange multiplier method is employed to incorporate self and mutual contact. An objective is formulated using the Fourier shape descriptors with the permitted resource constraint. The hill-climber optimization technique is utilized to update the design variables. An in-house code is developed for the entire process. To demonstrate the method's efficacy, a CCM is optimized with a two-kink path. The desired and obtained paths are compared., Comment: Accepted in iNCMDAO 2024 international conference

Modelling the large deformation of hyperelastic solids under plane stress conditions for arbitrary compressible and nearly incompressible material models is challenging. This is in contrast to the case of full incompressibility where the out-of-plane deformation can be entirely characterised by the in-plane components. A rigorous general procedure for the incorporation of the plane stress condition for the compressible case (including the nearly incompressible case) is provided here, accompanied by a robust and open source finite element code. An isochoric/volumetric decomposition is adopted for nearly incompressible materials yielding a robust single-field finite element formulation. The nonlinear equation for the out-of-plane component of the deformation gradient is solved using a Newton-Raphson procedure nested at the quadrature point level. The model's performance and accuracy are made clear via a series of simulations of benchmark problems. Additional challenging numerical examples of composites reinforced with particles and fibres further demonstrate the capability of this general computational framework.

Large Language Models (LLMs) are capable of reproducing human-like inferences, including inferences about emotions and mental states, from text. Whether this capability extends beyond text to other modalities remains unclear. Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also present in GPT-4o, a multimodal LLM. Using two versions of a widely used theory of mind test, the Reading the Mind in Eyes Test and the Multiracial Reading the Mind in the Eyes Test, we found that GPT-4o outperformed humans in interpreting mental states from upright faces but underperformed humans when faces were inverted. While humans in our sample showed no difference between White and Non-white faces, GPT-4o's accuracy was higher for White than for Non-white faces. GPT-4o's errors were not random but revealed a highly consistent, yet incorrect, processing of mental-state information across trials, with an orientation-dependent error structure that qualitatively differed from that of humans for inverted faces but not for upright faces. These findings highlight how advanced mental state inference abilities and human-like face processing signatures, such as inversion effects, coexist in GPT-4o alongside substantial differences in information processing compared to humans.

Python is a low-cost and open-source substitute for the MATLAB programming language. This paper presents ``\texttt{PyTOPress}", a compact Python code for topology optimization that is primarily meant for pedagogical purposes. \texttt{PyTOPress}, based on the ``\texttt{TOPress}" MATLAB code \cite{kumar2023topress}, is built using the \texttt{NumPy} and \texttt{SciPy} libraries. The applied pressure load is modeled using the Darcy law and the drainage term. From the obtained pressure field, the constant nodal loads are found. The employed method makes it easier to compute the load sensitivity using the adjoint-variable method at a low cost. The topology optimization problems are resolved herein by minimizing the compliance of the structure with a constraint on material volume. The method of moving asymptotes is employed to update the design variables. The effectiveness and success of \texttt{PyTOPress} code are demonstrated by solving few design-dependent pressure loadbearing problems. The code is freely available at \url{https://github.com/PrabhatIn/PyTOPress}., Comment: Accepted in iNCMDAO 2024

In this work we addressed the challenge of multi-class anomaly classification in Video Capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to advanced transformer architectures. The purpose is to correctly classify diverse gastrointestinal disorders, which is critical for increasing diagnostic efficiency in clinical settings. We started with a proprietary CNN and improved performance with ResNet[7] for better feature extraction, followed by Vision Transformer (ViT)[2] to capture global dependencies. Multiscale Vision Transformer (MViT)[6] improved hierarchical feature extraction, while Dual Attention Vision Transformer (DaViT)[4] delivered cutting-edge results by combining spatial and channel attention methods. This methodology enabled us to improve model accuracy across a wide range of criteria, greatly surpassing older methods., Comment: Video Capsule Endoscopy Challenge

Stock return forecasting is a major component of numerous finance applications. Predicted stock returns can be incorporated into portfolio trading algorithms to make informed buy or sell decisions which can optimize returns. In such portfolio trading applications, the predictive performance of a time series forecasting model is crucial. In this work, we propose the use of the Evolutionary eXploration of Augmenting Memory Models (EXAMM) algorithm to progressively evolve recurrent neural networks (RNNs) for stock return predictions. RNNs are evolved independently for each stocks and portfolio trading decisions are made based on the predicted stock returns. The portfolio used for testing consists of the 30 companies in the Dow-Jones Index (DJI) with each stock have the same weight. Results show that using these evolved RNNs and a simple daily long-short strategy can generate higher returns than both the DJI index and the S&P 500 Index for both 2022 (bear market) and 2023 (bull market).

A systematic investigation of superheavy nuclei in the isotopic chains of proton numbers Z=106, 114, 120, and 126 together with isotonic chains of neutron numbers N=162, 172, and 184 is presented in the theoretical framework of relativistic mean-field density functionals based on density-dependent meson-nucleon couplings. Ground-state properties, including binding energy, shape, deformation, density profile, and radius, are estimated to provide compelling evidence of magicity in these even-even nuclei, aligning with the concept of the 'island of stability'. The analysis reveals central depletion in the charge density, indicating a bubble-like structure, primarily attributed to the substantial repulsive Coulomb field and the influence of higher l-states. A thorough examination of potential decay modes, employing various semi-empirical formulas, is presented. The probable alpha-decay chains are evaluated, demonstrating excellent agreement with available experimental data, Comment: 24 Pages, 8 figures, 4 Tables, Accepted in Nuclear Physics A

The cutoff energy and the divergence of the protons generated by the target normal sheath acceleration mechanism are known to be significantly influenced by micrometer and nanometer-size structures on the target front and rear surfaces. Specifically, the cutoff energy is significantly enhanced by creating a central rectangular groove on the target front surface, as shown in a recent study [Physics of Plasmas, 30(6), 063102 (2023)]. Here we report on 2D Particle-In-Cell (PIC) simulations to thoroughly explore the effect of the depth of the central rectangular groove on the energy spectra of the accelerated protons. The proton cutoff energy is found to enhance drastically as the thickness of the rear wall of the groove is reduced from a few micrometers to a few tens of nanometers, however, it drops sharply as the thickness of the rear wall is further reduced towards creating a complete hole through the target.

Our proposal is on a new stochastic optimizer for non-convex and possibly non-smooth objective functions typically defined over large dimensional design spaces. Towards this, we have tried to bridge noise-assisted global search and faster local convergence, the latter being the characteristic feature of a Newton-like search. Our specific scheme -- acronymed FINDER (Filtering Informed Newton-like and Derivative-free Evolutionary Recursion), exploits the nonlinear stochastic filtering equations to arrive at a derivative-free update that has resemblance with the Newton search employing the inverse Hessian of the objective function. Following certain simplifications of the update to enable a linear scaling with dimension and a few other enhancements, we apply FINDER to a range of problems, starting with some IEEE benchmark objective functions to a couple of archetypal data-driven problems in deep networks to certain cases of physics-informed deep networks. The performance of the new method vis-\'a-vis the well-known Adam and a few others bears evidence to its promise and potentialities for large dimensional optimization problems of practical interest., Comment: 19 pages, 12 figures, 3 tables

Reasoning and linguistic skills form the cornerstone of human intelligence, facilitating problem-solving and decision-making. Recent advances in Large Language Models (LLMs) have led to impressive linguistic capabilities and emergent reasoning behaviors, fueling widespread adoption across application domains. However, LLMs still struggle with complex reasoning tasks, highlighting their systemic limitations. In this work, we focus on evaluating whether LLMs have the requisite representations to reason using two foundational relationships: "equivalence" and "inheritance". We introduce novel tasks and benchmarks spanning six languages and observe that current SOTA LLMs often produce conflicting answers to the same questions across languages in 17.3-57.5% of cases and violate inheritance constraints in up to 37.2% cases. To enhance consistency across languages, we propose novel "Compositional Representations" where tokens are represented as composition of equivalent tokens across languages, with resulting conflict reduction (up to -4.7%) indicating benefits of shared LLM representations.

In this paper, we explore the property of eventual exponential positivity (EEP) in complex matrices. We show that this property holds for the real part of the matrix exponential for a certain class of complex matrices. Next, we present the relation between the spectral properties of the Laplacian matrix of an unsigned digraph with complex edge-weights and the property of real EEP. Finally, we show that the Laplacian flow system of a network is stable when the negated Laplacian admits real EEP. Numerical examples are presented to demonstrate the results.

We present a simplified derivation of the optimal checkpoint interval in Young_1974 [1]. The optimal checkpoint interval derivation in [1] is based on minimizing the total lost time as an objective-function. Lost time is a function of checkpoint interval, checkpoint save time, and average failure time. This simplified derivation yields lost-time-optimal that is identical to the one derived in [1]. For large scale-out super-computer or datacenter systems, what is important is the selection of optimal checkpoint interval that maximizes availability. We show that availability-optimal checkpoint interval is different from the one derived in [1]. However, availability-optimal checkpoint interval is asymptotically same as lost-time-optimal checkpoint interval for certain conditions on checkpoint save and recovery time. We show that these optimal checkpoint intervals hold in situations where the error detection latency is significantly smaller than any selected checkpoint interval. However, in cases where the error detection latency is very large then the optimal checkpoint interval is greater than or equal to the error detection latency., Comment: 10 pages, 5 figures

Despite the recent progress, existing frame interpolation methods still struggle with processing extremely high resolution input and handling challenging cases such as repetitive textures, thin objects, and large motion. To address these issues, we introduce a patch-based cascaded pixel diffusion model for frame interpolation, HiFI, that excels in these scenarios while achieving competitive performance on standard benchmarks. Cascades, which generate a series of images from low- to high-resolution, can help significantly with large or complex motion that require both global context for a coarse solution and detailed context for high resolution output. However, contrary to prior work on cascaded diffusion models which perform diffusion on increasingly large resolutions, we use a single model that always performs diffusion at the same resolution and upsamples by processing patches of the inputs and the prior solution. We show that this technique drastically reduces memory usage at inference time and also allows us to use a single model at test time, solving both frame interpolation and spatial up-sampling, saving training cost. We show that HiFI helps significantly with high resolution and complex repeated textures that require global context. HiFI demonstrates comparable or beyond state-of-the-art performance on multiple benchmarks (Vimeo, Xiph, X-Test, SEPE-8K). On our newly introduced dataset that focuses on particularly challenging cases, HiFI also significantly outperforms other baselines on these cases. Please visit our project page for video results: https://hifi-diffusion.github.io, Comment: Project page: https://hifi-diffusion.github.io/

Using a perturbative approach, an evolution equation for the space charge density, correct up to the third order, is deduced for arbitrary initial density profiles of the electron and ion fluids in a cold nonrelativistic plasma. The evolution equation is solved to reproduce known results pertaining to the phase mixing time in the immobile ion limit as well as in the case of mobile ions, for a homogeneous plasma as well as for a plasma with a periodic inhomogeneity. The case of non-periodic plasma inhomogeneity, as in a finite-size plasma, is also discussed and some insights are given which are well supported by fluid simulation observations., Comment: 18 pages, 7 figures

Many scientific fields collect longitudinal count compositional data. Each observation is a multivariate count vector, where the total counts are arbitrary, and the information lies in the relative frequency of the counts. Multiple authors have proposed Bayesian Multinomial Logistic-Normal Dynamic Linear Models (MLN-DLMs) as a flexible approach to modeling these data. However, adoption of these methods has been limited by computational challenges. This article develops an efficient and accurate approach to posterior state estimation, called $\textit{Fenrir}$. Our approach relies on a novel algorithm for MAP estimation and an accurate approximation to a key posterior marginal of the model. As there are no equivalent methods against which we can compare, we also develop an optimized Stan implementation of MLN-DLMs. Our experiments suggest that Fenrir can be three orders of magnitude more efficient than Stan and can even be incorporated into larger sampling schemes for joint inference of model hyperparameters. Our methods are made available to the community as a user-friendly software library written in C++ with an R interface.

Understanding community structure has played an essential role in explaining network evolution, as nodes join communities which connect further to form large-scale complex networks. In real-world networks, nodes are often organized into communities based on ethnicity, gender, race, or wealth, leading to structural biases and inequalities. Community detection (CD) methods use network structure and nodes' attributes to identify communities, and can produce biased outcomes if they fail to account for structural inequalities, especially affecting minority groups. In this work, we propose group fairness metrics ($\Phi^{F*}_{p}$) to evaluate CD methods from a fairness perspective. We also conduct a comparative analysis of existing CD methods, focusing on the performance-fairness trade-off, to determine whether certain methods favor specific types of communities based on their size, density, or conductance. Our findings reveal that the trade-off varies significantly across methods, with no specific type of method consistently outperforming others. The proposed metrics and insights will help develop and evaluate fair and high performing CD methods., Comment: Complex Networks 2024

The dispersion involves the coordination of $k \leq n$ agents on a graph of size $n$ to reach a configuration where at each node at most one agent can be present. It is a well-studied problem. Also, this problem is studied on dynamic graphs with $n$ nodes where at each discrete time step the graph is a connected sub-graph of the complete graph $K_n$. An optimal algorithm is provided assuming global communication and 1-hop visibility of the agents. How this problem pans out on Time-Varying Graphs (TVG) is an open question in the literature. In this work we study this problem on TVG where at each discrete time step the graph is a connected sub-graph of an underlying graph $G$ (known as a footprint) consisting of $n$ nodes. We have the following results even if only one edge from $G$ is missing in the connected sub-graph at any time step and all agents start from a rooted initial configuration. Even with unlimited memory at each agent and 1-hop visibility, it is impossible to solve dispersion for $n$ co-located agents on a TVG in the local communication model. Furthermore, even with unlimited memory at each agent but without 1-hop visibility, it is impossible to achieve dispersion for $n$ co-located agents in the global communication model. From the positive side, the existing algorithm for dispersion on dynamic graphs with the assumptions of global communication and 1-hop visibility works on TVGs as well. This fact and the impossibility results push us to come up with a modified definition of the dispersion problem on TVGs, as one needs to start with more than $n$ agents if the objective is to drop the strong assumptions of global communication and 1-hop visibility. Then, we provide an algorithm to solve the modified dispersion problem on TVG starting with $n+1$ agents with $O(\log n)$ memory per agent while dropping both the assumptions of global communication and 1-hop visibility.

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs.

Modular addition is, on its face, a simple operation: given $N$ elements in $\mathbb{Z}_q$, compute their sum modulo $q$. Yet, scalable machine learning solutions to this problem remain elusive: prior work trains ML models that sum $N \le 6$ elements mod $q \le 1000$. Promising applications of ML models for cryptanalysis-which often involve modular arithmetic with large $N$ and $q$-motivate reconsideration of this problem. This work proposes three changes to the modular addition model training pipeline: more diverse training data, an angular embedding, and a custom loss function. With these changes, we demonstrate success with our approach for $N = 256, q = 3329$, a case which is interesting for cryptographic applications, and a significant increase in $N$ and $q$ over prior work. These techniques also generalize to other modular arithmetic problems, motivating future work.

The performance of relativistic particle pushers has long been a topic of interest in the field of computational plasma physics, particularly from the point of view of the particle-in-cell approach. Previous works undertaken to compare such integrators have predominantly targeted the ultra-relativistic regime. In this paper, we utilize a custom-built code to study the core run-times of the Boris, the Vay, and the Higuera-Cary particle pushers for low-, high-, and ultra-relativistic particles. This is followed by a comparison of the three integrators in terms of accuracy and error. A fitness parameter is then proposed that can serve as a one-stop value to determine which method is more suitable for a particular simulation setup. It is hoped that through knowledge of such intricacies, the choice for the integrator will be easier to make depending on the problem at hand.

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security.

JADES-GS-z14-0 is the most distant spectroscopically confirmed galaxy so far, at $z>14$. With a UV magnitude of -20.81, it is one of the most luminous galaxies at cosmic dawn and its half-light radius of 260 pc means that stars dominate the observed UV emission. We report the ALMA detection of [OIII]88$\mu$m line emission with a significance of 6.67$\sigma$ and at a frequency of 223.524 GHz, corresponding to a redshift of $14.1796\pm0.0007$, which is consistent with the candidate CIII] line detected in the NIRSpec spectrum. At this spectroscopic redshift, the Lyman break identified with NIRSpec requires a damped Lyman-$\alpha$ absorber with a column density of $\log(N_{\rm HI}/\mathrm{cm}^{-2})=22.23$. The total [OIII]88$\mu$m luminosity (log($(L_{\rm [OIII]}/L_\odot) = 8.3\pm0.1$) is fully consistent with the local $L_{\rm [OIII]}-SFR$ relation. Based on the ${L_{\rm [OIII]}/SFR}$, we infer a gas-phase metallicity $>0.1~{\rm Z_{\rm \odot}}$, which is somewhat unexpected given the weakness of the UV emission lines. Using prospector SED modeling and combining the ALMA data with JWST observations, we find $Z=0.17~{Z_{\rm \odot}}$ and an escape fraction of ionizing photons of 20%, which is necessary to explain the UV spectrum. We measure an [O III]5007\r{A}/[O III]88$\mu$m line flux ratio between 1 and 10, resulting in an upper limit to the electron density of roughly 300 cm$^{-3}$, which is lower than those measured in other high-$z$ luminous galaxies. The [OIII]88$\mu$m emission line is spectrally resolved, with a FWHM of 100 km/s, resulting in a dynamical mass of $\log$(M$_{\rm dyn}/M_\odot$) = 9.0$\pm0.2$. This value is comparable to the stellar mass derived from the SED fitting, which implies a very low gas fraction. Past radiation-driven outflows may have cleared the galaxy from the gas, reducing the gas fraction and thus increasing the escape fraction of ionizing photons., Comment: 12 pages, 7 figure

In a mean-field approximation within Kinetic Field Theory, it is possible to derive an accurate analytic expression for the power spectrum of present-day non-linear cosmic density fluctuations. It depends on the theory of gravity and the cosmological model via the expansion function of the background space-time, the growth factor derived from it, and the gravitational coupling strength, which may deviate from Newton's constant in a manner depending on time and spatial scale. In earlier work [1], we introduced a functional Taylor expansion around general relativity and the cosmological standard model to derive the effects of a wide class of modified-gravity theories on the non-linear power spectrum, assuming that such effects need to be small given the general success of the standard model. Here, we extend this class towards theories with small-scale screening, modeling screening effects by a suitably flexible interpolating function. We compare the Taylor expansion with full mean-field solutions and find good agreement where expected. We find typical relative enhancements of the non-linear power spectrum between a few and a few ten per cent in a broad range of wave numbers between $k\gtrsim0.1-10\,h\,\mathrm{Mpc}$, in good qualitative agreement with results of numerical simulations. This extends the application of our analytic approach to non-linear cosmic structure formation to essentially all classes of modified-gravity theories., Comment: Prepared for submission to JCAP

Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems are designed to map answer text back to the source document, yet the granularity of this mapping has not been addressed. Furthermore, a critical question arises: What exactly should be attributed? This involves identifying the specific information units within an answer that require grounding. In this paper, we propose and investigate a novel approach to the factual decomposition of generated answers for attribution, employing template-based in-context learning. To accomplish this, we utilize the question and integrate negative sampling during few-shot in-context learning for decomposition. This approach enhances the semantic understanding of both abstractive and extractive answers. We examine the impact of answer decomposition by providing a thorough examination of various attribution approaches, ranging from retrieval-based techniques to LLM-based attributors.

Rowhammer is a hardware security vulnerability at the heart of every system with modern DRAM-based memory. Despite its discovery a decade ago, comprehensive defenses remain elusive, while the probability of successful attacks grows with DRAM density. Hardware-based defenses have been ineffective, due to considerable cost, delays in commercial adoption, and attackers' repeated ability to circumvent them. Meanwhile, more flexible software-based solutions either incur substantial performance and memory capacity overheads, or offer limited forms of protection. Citadel is a new memory allocator design that prevents Rowhammer-initiated security exploits by addressing the vulnerability's root cause: physical adjacency of DRAM rows. Citadel enables creation of flexible security domains and isolates different domains in physically disjoint memory regions, guaranteeing security by design. On a server system, Citadel supports thousands of security domains at a modest 7.4% average memory overhead and no performance loss. In contrast, recent domain isolation schemes fail to support many workload scenarios due to excessive overheads, and incur 4--6x higher overheads for supported scenarios. As a software solution, Citadel offers readily deployable Rowhammer-aware isolation on legacy, current, and future systems.

While advertising has become commonplace in today's online interactions, there is a notable dearth of research investigating the extent to which browser fingerprinting is harnessed for user tracking and targeted advertising. Prior studies only measured whether fingerprinting-related scripts are being run on the websites but that in itself does not necessarily mean that fingerprinting is being used for the privacy-invasive purpose of online tracking because fingerprinting might be deployed for the defensive purposes of bot/fraud detection and user authentication. It is imperative to address the mounting concerns regarding the utilization of browser fingerprinting in the realm of online advertising. To understand the privacy-invasive use of fingerprinting for user tracking, this paper introduces a new framework ``FPTrace'' (fingerprinting-based tracking assessment and comprehensive evaluation framework) designed to identify alterations in advertisements resulting from adjustments in browser fingerprinting settings. Our approach involves emulating genuine user interactions, capturing advertiser bid data, and closely monitoring HTTP information. Using FPTrace we conduct a large-scale measurement study to identify whether browser fingerprinting is being used for the purpose of user tracking and ad targeting. The results we have obtained provide robust evidence supporting the utilization of browser fingerprinting for the purposes of advertisement tracking and targeting. This is substantiated by significant disparities in bid values and a reduction in HTTP records subsequent to changes in fingerprinting. In conclusion, our research unveils the widespread employment of browser fingerprinting in online advertising, prompting critical considerations regarding user privacy and data security within the digital advertising landscape.

Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift over time. Most existing studies extract invariant patterns to counter data drift but ignore pattern diversity, exhibiting poor generalization to unseen entities. To address this issue, we design an Informative Graph Neural Network (INF-GNN) to distill diversified invariant patterns and improve prediction accuracy under data drift. Firstly, we build an informative subgraph with a uniquely designed metric, Relation Importance (RI), that can effectively select stable entities and distinct spatial relationships. This subgraph further generalizes new entities' data via neighbors merging. Secondly, we propose an informative temporal memory buffer to help the model emphasize valuable timestamps extracted using influence functions within time intervals. This memory buffer allows INF-GNN to discern influential temporal patterns. Finally, RI loss optimization is designed for pattern consolidation. Extensive experiments on real-world dataset under substantial data drift demonstrate that INF-GNN significantly outperforms existing alternatives.

In the present work, we have predicted the half-lives for the $\beta^{-}$ decay for higher forbidden unique transitions in the mass range of nuclei from A = 40-138. For these transitions, the experimental data for half-lives are not available except for a few cases. The calculations for half-lives are performed within the framework of the nuclear shell model (SM). We have used the effective interactions sdpf-mu, gxpf1a, gwbxg, G-matrix, snet, sn100pn, and jj56pnb to perform the SM calculations in different mass regions. A comprehensive discussion has been made between the SM-predicted half-lives and the scaled half-lives from proton-neutron quasiparticle random-phase approximation (pnQRPA). The results of the present study will be useful to plan new experiments to measure half-lives for these higher forbidden unique $\beta^{-}$ transitions., Comment: 8 pages, 2 figures

A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints and provide interpretability of the sequence of commands over the horizon. However, despite being able to plan, MPC struggles to scale with task complexity, often failing to achieve robust behavior on rapidly changing surfaces. On the other hand, model-free Reinforcement Learning (RL) methods have outperformed MPC on multiple terrains, showing emergent motions but inherently lack any ability to handle constraints or perform planning. To address these limitations, we propose a framework that integrates proprioceptive planning with RL, allowing for agile and safe locomotion behaviors through the horizon. Inspired by MPC, we incorporate an internal model that includes a velocity estimator and a Dreamer module. During training, the framework learns an expert policy and an internal model that are co-dependent, facilitating exploration for improved locomotion behaviors. During deployment, the Dreamer module solves an infinite-horizon MPC problem, adapting actions and velocity commands to respect the constraints. We validate the robustness of our training framework through ablation studies on internal model components and demonstrate improved robustness to training noise. Finally, we evaluate our approach across multi-terrain scenarios in both simulation and hardware., Comment: Preprint under review

Error mitigation techniques, while instrumental in extending the capabilities of near-term quantum computers, often suffer from exponential resource scaling with noise levels. To address this limitation, we introduce a novel approach, constant runtime Error Mitigation by Restricted Evolution (EMRE). Through numerical simulations, we demonstrate that EMRE surpasses the performance of Probabilistic Error Cancellation (PEC) while maintaining constant sample complexity. Moreover, we uncover a continuous family of error mitigation protocols, Hybrid EMREs (HEMREs), encompassing PEC and EMRE as special cases. HEMREs offer a tunable bias parameter, allowing for a trade-off between sample complexity and error reduction. Thus, our error mitigation protocols provide flexibility in balancing error mitigation with computational overhead, catering to practical application requirements of near-term and early-fault tolerant quantum devices., Comment: 22 pages, 9 figures. Comments are welcome

Ly$\alpha$ is the transition to the ground state from the first excited state of hydrogen (the most common element). Resonant scattering of this line by neutral hydrogen greatly impedes its emergence from galaxies, so the fraction of galaxies which show Ly$\alpha$ is a tracer of the neutral fraction of the intergalactic medium (IGM), and thus the history of reionization. In previous works, we used early JWST/NIRSpec data from the JWST Advanced Deep Extragalactic Survey (JADES) to classify and characterise Ly$\alpha$ emitting galaxies (LAEs). This survey is now approaching completion, and the current sample is nearly an order of magnitude larger. From a sample of 784 galaxies in JADES at $4.0

Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences, including the development of cancer. Earlier identification of mutagenic compounds in the drug development process is therefore crucial for preventing the progression of unsafe candidates and reducing development costs. While computational techniques, especially machine learning models have become increasingly prevalent for this endpoint, they rely on a single modality. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model which incorporate multiple modalities such as simplified molecular input line entry system (SMILES) and molecular graph. These modalities capture diverse information about molecules such as substructural, physicochemical, geometrical and topological. To derive substructural, geometrical and physicochemical information, we use SMILES, while topological information is extracted through a graph attention network (GAT) via molecular graph. Our model uses a stacked ensemble of machine learning classifiers to make predictions using these multiple features. We employ the explainable artificial intelligence (XAI) technique SHAP (Shapley Additive Explanations) to determine the significance of each classifier and the most relevant features in the prediction. We demonstrate that our method surpasses SOTA methods on two standard datasets across various metrics. Notably, we achieve an area under the curve of 95.21\% on the Hansen benchmark dataset, affirming the efficacy of our method in predicting mutagenicity. We believe that this research will captivate the interest of both clinicians and computational biologists engaged in translational research., Comment: Submitted to a journal

Traditional medical basic sciences educational approaches follow a one-size-fits-all model, neglecting the diverse learning styles of individual students. I propose an intelligent AI companion which will fill this gap by providing on-the-fly solutions to students' questions in the context of not only USMLE Step 1 but also other similar examinations in other countries, inter alia, PLAB Part 1 in United Kingdom, and NEET (PG) and FMGE in India. I have harnessed Generative AI for dynamic, accurate, human-like responses and for knowledge retention and application. Users were encouraged to employ prompt engineering, in particular, in-context learning, for response optimization and enhancing the model's precision in understanding the intent of the user through the way the query is framed. The implementation of RAG has enhanced the chatbot's ability to combine pre-existing medical knowledge with generative capabilities for efficient and contextually relevant support. Mistral was employed using Python to perform the needed functions. The digital conversational agent was implemented and achieved a score of 0.5985 on a reference-based metric similar to BLEU and ROUGE scores. My approach addresses a critical gap in traditional medical basic sciences education by introducing an intelligent AI companion which specializes in helping medical aspirants with planning and information retention for USMLE Step 1 and other similar exams. Considering the stress that medical aspirants face in studying for the exam and in obtaining spontaneous answers to medical basic sciences queries, especially whose answers are challenging to obtain by searching online, and obviating a student's need to search bulky medical texts or lengthy indices or appendices, I have been able to create a quality assistant capable of producing ad-libitum responses best suited to the user's needs., Comment: 42 pages, 15 figures, 1 table

We generalize conventional multiferroicity \ -- simultaneous existence of ferroelectric and ferromagnetic orders \ -- to multipolar degrees of freedom, examining its emergence in a $d^2$ Mott insulator with strong spin-orbit and Hund's couplings. Specifically, we investigate the origin of magnetic multipolar interactions in $d^2$ Mott insulators. In addition, we show that an admixture of quadrupolar and octupolar magnetic order simultaneously induces electrical quadrupolar and ferroelectric polarization. Our theoretical formalism extends the multiferroic framework to the higher-order sector, exploring the possibility of coexisting multipolar orders of the same and different ranks. We finally comment on some of the experimental signatures., Comment: 5+12 pages, 7 figures, comments are welcome

$\require{mediawiki-texvc}$Cosmic Reionisation commenced when ultraviolet (UV) radiation produced in the first galaxies began illuminating the cold, neutral gas that filled the primordial Universe. Recent James Webb Space Telescope (JWST) observations have shown that surprisingly UV-bright galaxies were in place beyond redshift $z = 14$, when the Universe was less than 300 Myr old. Smooth turnovers of their UV continua have been interpreted as damping-wing absorption of Lyman-$\alpha$ (Ly$\alpha$), the principal hydrogen transition. However, spectral signatures encoding crucial properties of these sources, such as their emergent radiation field, largely remain elusive. Here we report spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES) of a galaxy at redshift $z = 13.0$ that reveal a singular, bright emission line unambiguously identified as Ly$\alpha$, in addition to a smooth turnover. We observe an equivalent width of $\text{EW}_\mathrm{Ly\alpha} > 40 \, \AA$ (rest frame), previously only seen at $z < 9$ where the intervening intergalactic medium (IGM) becomes increasingly ionised. Together with a very blue UV continuum, the Ly$\alpha$ line indicates the galaxy is a prolific producer of ionising photons, a significant fraction of which may escape. This suggests it resides in an early reionised region preventing complete extinction of Ly$\alpha$, thus shedding new light on the nature of the earliest galaxies and the onset of Reionisation only 330 Myr after the Big Bang., Comment: 19 pages, 9 figures, 3 tables. Comments welcome

The process of ship design is intricate, heavily influenced by the hull form which accounts for approximately 70% of the total cost. Traditional methods rely on human-driven iterative processes based on naval architecture principles and engineering analysis. In contrast, generative AI presents a novel approach, utilizing computational algorithms rooted in machine learning and artificial intelligence to optimize ship hull design. This report outlines the systematic creation of a generative AI for this purpose, involving steps such as dataset collection, model architecture selection, training, and validation. Utilizing the "SHIP-D" dataset, consisting of 30,000 hull forms, the report adopts the Gaussian Mixture Model (GMM) as the generative model architecture. GMMs offer a statistical framework to analyze data distribution, crucial for generating innovative ship designs efficiently. Overall, this approach holds promise in revolutionizing ship design by exploring a broader design space and integrating multidisciplinary optimization objectives effectively.

Materials with antiferromagnetic order have recently emerged as promising candidates in spintronics based on their beneficial characteristics such as vanishing stray fields and ultra-fast dynamics. At the same time more complex localized non-coplanar magnetic states as for instance skyrmions are in the focus of applications due to their intriguing properties such as the topological Hall effect. Recently a conceptual shift has occurred to envision the use of such magnetic defects not only in one-dimensional race track devices but to exploit their unique properties in two-dimensional networks. Here we use local strain in a collinear antiferromagnet to induce non-coplanar domain wall junctions, which connect in a very specific way to form extended networks. We combine spin-polarized scanning tunneling microscopy with density functional theory to characterize the different building blocks of the network, and unravel the origin of the handedness of triple-junctions and the size of the arising topological orbital moments., Comment: Marie Sk{\l}odowska-Curie Actions, H2020-MSCA-ITN-2020; Project acronym SPEAR; Grant Agreement No. 955671

Robust solid-state protein junctions (RPJs) with permanent contacts are essential for a wide range of studies aimed at elucidating the mechanisms for electron transport across such junctions and their possible applications. Here we report on first time, RPJs formed with vacuum-evaporated carbon (eC) followed by Au as top electrode set on a single bilayer of bacteriorhodopsin (bR), self-assembled from so-lution on an Au substrate. The light-induced photocycle of bR, as well as the amide I & II vibrational frequencies of bR, were unchanged upon eC deposition; the yield of the resulting junctions was reproducible ~ 90 % (non-shorted), and they were stable between ~ 10 and 300K. The current-voltage characteristics of these junc-tions were temperature-independent and the junctions were stable for several months. Hence, such robust all-solid-state protein junctions provide valuable tools for investigating the potential use of proteins in future bio-molecular electronic de-vices.

In continual learning (CL), model growth enhances adaptability over new data, improving knowledge retention for more tasks. However, improper model growth can lead to severe degradation of previously learned knowledge, an issue we name as growth-induced forgetting (GIFt), especially in task-agnostic CL using entire grown model for inference. Existing works, despite adopting model growth and random initialization for better adaptability, often fail to recognize the presence of GIFt caused by improper model growth. This oversight limits comprehensive control of forgetting and hinders full utilization of model growth. We are the first in CL to identify this issue and conduct an in-depth study on root cause of GIFt, where layer expansion stands out among model growth strategies, widening layers without affecting model functionality. Yet, direct adoption of layer expansion presents challenges. It lacks data-driven control and initialization of expanded parameters to balance adaptability and knowledge retention. This paper presents a novel SparseGrow approach to overcome the issue of GIFt while enhancing adaptability over new data. SparseGrow employs data-driven sparse layer expansion to control efficient parameter usage during growth, reducing GIFt from excessive growth and functionality changes. It also combines sparse growth with on-data initialization at training late-stage to create partially 0-valued expansions that fit learned distribution, enhancing retention and adaptability. To further minimize forgetting, freezing is applied by calculating the sparse mask, allowing data-driven preservation of important parameters. Through experiments across datasets with various settings, cases, and task numbers, we demonstrate the necessity of layer expansion and showcase the effectiveness of SparseGrow in overcoming GIFt, highlighting its adaptability and knowledge retention for incremental tasks.

Molecular Property Prediction (MPP) plays a pivotal role across diverse domains, spanning drug discovery, material science, and environmental chemistry. Fueled by the exponential growth of chemical data and the evolution of artificial intelligence, recent years have witnessed remarkable strides in MPP. However, the multifaceted nature of molecular data, such as molecular structures, SMILES notation, and molecular images, continues to pose a fundamental challenge in its effective representation. To address this, representation learning techniques are instrumental as they acquire informative and interpretable representations of molecular data. This article explores recent AI/-based approaches in MPP, focusing on both single and multiple modality representation techniques. It provides an overview of various molecule representations and encoding schemes, categorizes MPP methods by their use of modalities, and outlines datasets and tools available for feature generation. The article also analyzes the performance of recent methods and suggests future research directions to advance the field of MPP., Comment: Submitted to the journal

Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely on supervised learning, necessitates a large amount of annotated data for model training. However, acquiring such extensive labeled data can be laborious and time\-intensive. This research examines the three learning environments: supervised learning (SL), semi\-supervised learning (Semi\-SL), and self\-supervised learning (Self\-SL): to predict kidney, lung, and breast cancer. Three pre\-trained deep learning models (Residual Network\-50, Visual Geometry Group\-16, and EfficientNetB0) are evaluated based on these learning settings using seven carefully curated training sets. To create the first training set (TS1), SL is applied to all annotated image samples. Five training sets (TS2\-TS6) with different ratios of labeled and unlabeled cancer images are used to evaluateSemi\-SL. Unlabeled cancer images from the final training set (TS7) are utilized for Self\-SL assessment. Among different learning environments, outcomes from the Semi\-SL setting show a strong degree of agreement with the outcomes achieved in the SL setting. The uniform pattern of observations from the pre\-trained models across all three datasets validates the methodology and techniques of the research. Based on modest number of labeled samples and minimal computing cost, our study suggests that the Semi\-SL option can be a highly viable replacement for the SL option under label annotation constraint scenarios., Comment: Submitted to the journal

Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summarization, but they often struggle with processing long input contexts. Furthermore, while television transcripts have received attention in recent studies, movie screenplay summarization remains underexplored. To stimulate research in this area, we present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. We manually formatted the movie screenplays to represent their structural elements. Compared to existing datasets, MovieSum possesses several distinctive features: (1) It includes movie screenplays, which are longer than scripts of TV episodes. (2) It is twice the size of previous movie screenplay datasets. (3) It provides metadata with IMDb IDs to facilitate access to additional external knowledge. We also show the results of recently released large language models applied to summarization on our dataset to provide a detailed baseline., Comment: ACL 2024 Findings

Large language models (LLMs) represent a groundbreaking advancement in the domain of natural language processing due to their impressive reasoning abilities. Recently, there has been considerable interest in increasing the context lengths for these models to enhance their applicability to complex tasks. However, at long context lengths and large batch sizes, the key-value (KV) cache, which stores the attention keys and values, emerges as the new bottleneck in memory usage during inference. To address this, we propose Eigen Attention, which performs the attention operation in a low-rank space, thereby reducing the KV cache memory overhead. Our proposed approach is orthogonal to existing KV cache compression techniques and can be used synergistically with them. Through extensive experiments over OPT, MPT, and Llama model families, we demonstrate that Eigen Attention results in up to 40% reduction in KV cache sizes and up to 60% reduction in attention operation latency with minimal drop in performance., Comment: 12 page, 6 figures, 6 tables

We report the experimental observation of fully developed Kolmogorov turbulence originating from self-excited vortex flows in a three-dimensional (3D) dust cloud. The characteristic -5/3 scaling of three-dimensional Kolmogorov turbulence is universally observed in both the spatial and temporal power spectra. Additionally, the 2/3 scaling in the second-order structure function further confirms the presence of Kolmogorov turbulence. We also identified a slight deviation in the tails of the probability distribution functions for velocity gradients. The dust cloud formed in the diffused region away from the electrode and above the glass device surface in the glow discharge experiments. The dust rotation was observed in multiple experimental campaigns under different discharge conditions at different spatial locations and background plasma environments., Comment: 13 pages, 14 figures

We study, from both a theoretical and observational perspective, the physical origin and spectroscopic impact of extreme nebular emission in high-redshift galaxies. The nebular continuum, which can appear during extreme starbursts, is of particular importance as it tends to redden UV slopes and has a significant contribution to the UV luminosities of galaxies. Furthermore, its shape can be used to infer the gas density and temperature of the ISM. First, we provide a theoretical background, showing how different stellar populations (SPS models, IMFs, and stellar temperatures) and nebular conditions impact observed galaxy spectra. We demonstrate that, for systems with strong nebular continuum emission, 1) UV fluxes can increase by up to 0.7~magnitudes (or more in the case of hot/massive stars) above the stellar continuum, which may help reconcile the surprising abundance of bright high-redshift galaxies and the elevated UV luminosity density at $z>10$, 2) at high gas densities, UV slopes can redden from $\beta\lesssim-2.5$ to $\beta\sim-1$, 3) observational measurements of $\xi_{ion}$ are grossly underestimated, and 4) UV downturns from two-photon emission can masquerade as DLAs. Second, we present a dataset of 58 galaxies observed with NIRSpec on JWST at $2.510$ galaxies, finding that UV slopes and UV downturns are in some cases redder or steeper than expected from SPS models, which may hint at more exotic (e.g. hotter/more massive stars or AGN) ionizing sources., Comment: 26 pages, 18 figures, submitted to The Open Journal of Astrophysics

Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these models are susceptible to attacks that manipulate input data or exploit noise in quantum circuits. Following this, various studies have explored the robustness of these models. These works focus on the robustness certification of manipulations of the quantum states. We extend this line of research by investigating the robustness against perturbations in the classical data for a general class of data encoding schemes. We show that for such schemes, the addition of suitable noise channels is equivalent to evaluating the mean value of the noiseless classifier at the smoothed data, akin to Randomized Smoothing from classical machine learning. Using our general framework, we show that suitable additions of phase-damping noise channels improve empirical and provable robustness for the considered class of encoding schemes.

Vertical Take-Off and Landing (VTOL) vehicles have gained immense popularity in the delivery drone market and are now being developed for passenger transportation in urban areas to efficiently enable Urban Air Mobility (UAM). UAM aims to utilize the urban airspace \hidetxt{vertical dimension} to address the problem of heavy road congestion in dense urban cities. VTOL vehicles require vertiport terminals for landing, take-off, passengers boarding or deboarding, refuelling (or charging), and maintenance. An efficient scheduling algorithm is essential to maximize the throughput of the vertiport terminal (vertiminal)\hidetxt{ as well as efficient use of airspace} while maintaining safety protocols to handle the UAM traffic. While traditional departure and taxiing operations can be applied in the context of vertiminal, specific algorithms are required for take-off and landing schedules. Unlike fixed-wing aircraft that require a runway to take-off and climb in a single direction, VTOL vehicles can approach and climb in several directions. We propose a Mixed Integer Linear Program (MILP) formulation to schedule flights for taxiing, climbing (or approaching) using multiple directions after take-off (before landing) and turnaround on gates. We also derived equations to thoroughly analyze the throughput capacity of a vertiminal considering all its core elements. We have shown that our MILP can achieve the maximum throughput obtained through the equations. Given the input parameters, our analysis can be used to analyze the capacity of a vertiminal without running any simulation, while our MILP can be used to get the most efficient schedule., Comment: Extension of conference work "Integrated Taxiing and TLOF pad Scheduling Using Different Surface Directions with Fairness Analysis" published in ITSC 2023. DOI: https://doi.org/10.1109/ITSC57777.2023.10422484

Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machine learning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.

Lattice cryptography schemes based on the learning with errors (LWE) hardness assumption have been standardized by NIST for use as post-quantum cryptosystems, and by HomomorphicEncryption.org for encrypted compute on sensitive data. Thus, understanding their concrete security is critical. Most work on LWE security focuses on theoretical estimates of attack performance, which is important but may overlook attack nuances arising in real-world implementations. The sole existing concrete benchmarking effort, the Darmstadt Lattice Challenge, does not include benchmarks relevant to the standardized LWE parameter choices - such as small secret and small error distributions, and Ring-LWE (RLWE) and Module-LWE (MLWE) variants. To improve our understanding of concrete LWE security, we provide the first benchmarks for LWE secret recovery on standardized parameters, for small and low-weight (sparse) secrets. We evaluate four LWE attacks in these settings to serve as a baseline: the Search-LWE attacks uSVP, SALSA, and Cool & Cruel, and the Decision-LWE attack: Dual Hybrid Meet-in-the-Middle (MitM). We extend the SALSA and Cool & Cruel attacks in significant ways, and implement and scale up MitM attacks for the first time. For example, we recover hamming weight $9-11$ binomial secrets for KYBER ($\kappa=2$) parameters in $28-36$ hours with SALSA and Cool\&Cruel, while we find that MitM can solve Decision-LWE instances for hamming weights up to $4$ in under an hour for Kyber parameters, while uSVP attacks do not recover any secrets after running for more than $1100$ hours. We also compare concrete performance against theoretical estimates. Finally, we open source the code to enable future research., Comment: Accepted at Oakland S&P 2025