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As the frequency and severity of climate-related events such as droughts, floods, and water scarcity continue to escalate, accurate rainfall monitoring becomes increasingly critical. This paper covers various industry methods of measuring rainfall as well as our own ground pluviometer system. Our system consists of an inexpensive static rain gauge that can operate for approximately six to twelve months without maintenance. It utilizes resistive sensing technology accompanied by a microcontroller to measure the water level depth from the device vessel, recording rainfall at an hourly rate. This study also provides a side-by-side comparison of our pluviometer system with an industry rain gauge, the MeteoRain 200 Compact, from Barani Systems, with the differences in data being statistically insignificant. By prioritizing cost, sustainability, simplicity, ease of maintenance, and assembly, this research contributes to essential rainfall monitoring solutions, specifically for developing countries., Comment: 13 pages, 6 figures

Early JWST photometric studies discovered a population of UV faint ($\rm 700$\r{A}) exclude the most metal-poor efficient ionizers and favor 1) more chemically enriched systems with comparable extreme radiation fields and 2) older starbursting systems. In contrast, metallicity degeneracies are reduced in H$\alpha$ space, enabling the identification of these metal-poor efficient ionizers by their specific star-formation rate., Comment: 16+4 pages, 7 figures

To reduce the latency associated with autoretrogressive LLM inference, speculative decoding has emerged as a novel decoding paradigm, where future tokens are drafted and verified in parallel. However, the practical deployment of speculative decoding is hindered by its requirements for additional computational resources and fine-tuning, which limits its out-of-the-box usability. To address these challenges, we present PLD+, a suite of novel algorithms developed to accelerate the inference process of LLMs, particularly for input-guided tasks. These tasks, which include code editing, text editing, summarization, etc., often feature outputs with substantial overlap with their inputs-an attribute PLD+ is designed to exploit. PLD+ also leverages the artifacts (attention and hidden states) generated during inference to accelerate inference speed. We test our approach on five input-guided tasks and through extensive experiments we find that PLD+ outperforms all tuning-free approaches. In the greedy setting, it even outperforms the state-of-the-art tuning-dependent approach EAGLE on four of the tasks. (by a margin of upto 2.31 in terms of avg. speedup). Our approach is tuning free, does not require any additional compute and can easily be used for accelerating inference of any LLM.

The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-500 keV) compared to their estimation of 35% for the current uncertainty. Integral irradiation experiments suggest that the evaluated cross section of the JEFF-3.1 library overestimates the 242Pu(n,{\gamma}) cross section by 14% in the range between 1 keV and 1 MeV. In addition, the last measurement at LANSCE reported a systematic reduction of 20-30% in the 1-40 keV range relative to the evaluated libraries and previous data sets. In the present work this cross section has been determined up to 600 keV in order to solve the mentioned discrepancies. A 242Pu target of 95(4) mg enriched to 99.959% was irradiated at the n TOF-EAR1 facility at CERN. The capture cross section of 242Pu has been obtained between 1 and 600 keV with a systematic uncertainty (dominated by background subtraction) between 8 and 12%, reducing the current uncertainties of 35% and achieving the accuracy requested by the NEA in a large energy range. The shape of the cross section has been analyzed in terms of average resonance parameters using the FITACS code as implemented in SAMMY, yielding results compatible with our recent analysis of the resolved resonance region.The results are in good agreement with the data of Wisshak and K\"appeler and on average 10-14% below JEFF-3.2 from 1 to 250 keV, which helps to achieve consistency between integral experiments and cross section data. At higher energies our results show a reasonable agreement within uncertainties with both ENDF/B-VII.1 and JEFF-3.2. Our results indicate that the last experiment from DANCE underestimates the capture cross section of 242Pu by as much as 40% above a few keV., Comment: 20 pages, 19 figures, submitted to Phys. Rev. C

The concept of a monitoring edge-geodetic set (MEG-set) in a graph $G$, denoted $MEG(G)$, refers to a subset of vertices $MEG(G)\subseteq V(G)$ such that every edge $e$ in $G$ is monitored by some pair of vertices $ u, v \in MEG(G)$, where $e$ lies on all shortest paths between $u$ and $v$. The minimum number of vertices required to form such a set is called the monitoring edge-geodetic number, denoted $meg(G)$. The primary motivation for studying $MEG$-sets in previous works arises from scenarios in which certain edges are removed from $G$. In these cases, the vertices of the $MEG$-set are responsible for detecting these deletions. Such detection is crucial for identifying which edges have been removed from $G$ and need to be repaired. In real life, repairing these edges may be costly, or sometimes it is impossible to repair edges. In this case, the original $MEG$-set may no longer be effective in monitoring the modified graph. This highlights the importance of reassessing and adapting the $MEG$-set after edge deletions. This work investigates the monitoring edge-geodetic properties of graphs, focusing on how the removal of $k$ edges affects the structure of a graph and influences its monitoring capabilities. Specifically, we explore how the monitoring edge-geodetic number $meg(G)$ changes when $k$ edges are removed. The study aims to compare the monitoring properties of the original graph with those of the modified graph and to understand the impact of edge deletions., Comment: 24 pages

The rapid development of Large Multimodal Models (LMMs) has significantly advanced multimodal understanding by harnessing the language abilities of Large Language Models (LLMs) and integrating modality-specific encoders. However, LMMs are plagued by hallucinations that limit their reliability and adoption. While traditional methods to detect and mitigate these hallucinations often involve costly training or rely heavily on external models, recent approaches utilizing internal model features present a promising alternative. In this paper, we critically assess the limitations of the state-of-the-art training-free technique, the logit lens, in handling generalized visual hallucinations. We introduce a refined method that leverages contextual token embeddings from middle layers of LMMs. This approach significantly improves hallucination detection and grounding across diverse categories, including actions and OCR, while also excelling in tasks requiring contextual understanding, such as spatial relations and attribute comparison. Our novel grounding technique yields highly precise bounding boxes, facilitating a transition from Zero-Shot Object Segmentation to Grounded Visual Question Answering. Our contributions pave the way for more reliable and interpretable multimodal models.

We explore the use of local algorithms in the design of streaming algorithms for the Maximum Directed Cut problem. Specifically, building on the local algorithm of Buchbinder et al. (FOCS'12) and Censor-Hillel et al. (ALGOSENSORS'17), we develop streaming algorithms for both adversarially and randomly ordered streams that approximate the value of maximum directed cut in bounded-degree graphs. In $n$-vertex graphs, for adversarially ordered streams, our algorithm uses $O(n^{1-\Omega(1)})$ (sub-linear) space and for randomly ordered streams, our algorithm uses logarithmic space. Moreover, both algorithms require only one pass over the input stream. With a constant number of passes, we give a logarithmic-space algorithm which works even on graphs with unbounded degree on adversarially ordered streams. Our algorithms achieve any fixed constant approximation factor less than $\frac12$. In the single-pass setting, this is tight: known lower bounds show that obtaining any constant approximation factor greater than $\frac12$ is impossible without using linear space in adversarially ordered streams (Kapralov and Krachun, STOC'19) and $\Omega(\sqrt{n})$ space in randomly ordered streams, even on bounded degree graphs (Kapralov, Khanna, and Sudan, SODA'15). In terms of techniques, our algorithms partition the vertices into a small number of different types based on the structure of their local neighborhood, ensuring that each type carries enough information about the structure to approximately simulate the local algorithm on a vertex with that type. We then develop tools to accurately estimate the frequency of each type. This allows us to simulate an execution of the local algorithm on all vertices, and thereby approximate the value of the maximum directed cut., Comment: 45 pages, to appear in SODA 2025

The influence of nuclear deformation on proton-decay half-lives has been systematically studied in microscopic theoretical frameworks for a wide range of nuclei with Z<82. Correlation between 1p-decay half-lives and the deformed nuclear shapes of both the parent and daughter nuclei has been investigated. Since the deformations of proton emitters and their residual nuclei impact the potential barrier and disintegration energy which are crucial for the accurate determination of half-lives, we incorporate the nuclear deformations of both the emitters and residues in a phenomenological manner and propose a new semi-empirical formula to estimate the 1p-decay half-lives. The robustness of this formula is demonstrated by the accurate predictions of the measured values while making it reliable for forecasting the properties of other potential proton emitters. The phenomenon of shape coexistence as observed in several proton emitters and their respective daughter nuclei, is particularly signicant in this context due to secondary minima in the potential energy surfaces of both the nuclei. Accounting for these factors signicantly affects the estimation of half-lives and branching ratios by introducing additional decay pathways and altering transition probabilities between different nuclear shapes., Comment: 16 pages, 3 figures, 5 tables, Published in Scientific Reports (Nature Portfolio)

There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video. The lack of a robust solution to this problem limits the performance of SfM camera-calibration pipelines. We propose a novel approach to video-based motion segmentation to identify the components of a scene that are moving w.r.t. a fixed world frame. Our simple but effective iterative method, RoMo, combines optical flow and epipolar cues with a pre-trained video segmentation model. It outperforms unsupervised baselines for motion segmentation as well as supervised baselines trained from synthetic data. More importantly, the combination of an off-the-shelf SfM pipeline with our segmentation masks establishes a new state-of-the-art on camera calibration for scenes with dynamic content, outperforming existing methods by a substantial margin.

We present the discovery of a large extended radio jet associated with the extremely radio-loud quasar J1601+3102 at $z\sim5$ from sub-arcsecond resolution imaging at 144 MHz with the LOFAR International Telescope. These large radio lobes have been argued to remain elusive at $z>4$ due to energy losses in the synchrotron emitting plasma as a result of scattering of the strong CMB at these high redshifts. Nonetheless, the 0.3" resolution radio image of J1601+3102 reveals a Northern and Southern radio lobe located at 9 and 57 kpc from the optical quasar, respectively. The measured jet size of 66 kpc makes J1601+3102 the largest extended radio jet at $z>4$ to date. However, it is expected to have an even larger physical size in reality due to projection effects brought about by the viewing angle. Furthermore, we observe the rest-frame UV spectrum of J1601+3102 with Gemini/GNIRS to examine its black hole properties, which results in a mass of 4.5$\times$10$^{8}$ M$_{\odot}$ with an Eddington luminosity ratio of 0.45. The BH mass is relatively low compared to the known high-$z$ quasar population, which suggests that a high BH mass is not strictly necessary to generate a powerful jet. This discovery of the first $\sim100$ kpc radio jet at $z>4$ shows that these objects exist despite energy losses from Inverse Compton scattering and can put invaluable constraints on the formation of the first radio-loud sources in the early Universe., Comment: Accepted for publication in ApJL. 15 pages, 5 figures

The UV continuum slope of galaxies, $\beta$, is a powerful diagnostic. Understanding the redshift evolution of $\beta$ and its dependence on key galaxy properties can shed light on the evolution of galaxy physical properties over cosmic time. In this study, we present $\beta$ measurements for 295 spectroscopically confirmed galaxies at $5.5 9.5$ to begin to redden, deviating from the trend observed at $z < 9.5$. By producing stacked spectra in bins of redshift and $\beta$, we derive trends between $\beta$ and dust attenuation, metallicity, ionization parameter, and stellar age indicators directly from spectra, finding a lack of dust attenuation to be the dominant driver of bluer $\beta$ values. We further report six galaxies with $\beta<-3.0$, which show a range of spectroscopic properties and signs of significant LyC photon leakage. Finally, we show that the redder $\beta$ values at $z > 9.5$ may require rapid build-up of dust reservoirs in the very early Universe or a significant contribution from the nebular continuum emission to the observed UV spectra, with the nebular continuum fraction depending on the gas temperatures and densities. Our modeling shows that in the absence of dust, nebular emission at $T > 15,000$ K can reproduce the range of $\beta$ that we see in our sample. Higher gas temperatures driven by hot, massive stars can boost the fraction of nebular continuum emission, potentially explaining the observed $\beta$ values as well as bright UV magnitudes seen across galaxies at $z > 10$., Comment: submitted to MNRAS, 22 pages including 1 appendix, 12 Figures (including 1 in appendix), comments welcome!

We discuss what topological data must be provided to define topologically twisted partition functions of four-dimensional $\mathcal{N}=2$ supersymmetric field theories. The original example of Donaldson-Witten theory depends only on the diffeomorphism type of the spacetime and 't Hooft fluxes (characteristic classes of background gerbe connections, a.k.a. "one-form symmetry connections.") The example of $\mathcal{N}=2^*$ theories shows that, in general, the twisted partition functions depend on further topological data. We describe topological twisting for general four-dimensional $\mathcal{N}=2$ theories and argue that the topological partition functions depend on (a): the diffeomorphism type of the spacetime, (b): the characteristic classes of background gerbe connections and (c): a "generalized spin-c structure," a concept we introduce and define. The main ideas are illustrated with both Lagrangian theories and class $\mathcal{S}$ theories. In the case of class $\mathcal{S}$ theories of $A_1$ type, we note that the different $S$-duality orbits of a theory associated with a fixed UV curve $C_{g,n}$ can have different topological data., Comment: 46 pages + appendices = 97 pages, 4 figures

Rust aims to offer full memory safety for programs, a guarantee that untamed C programs do not enjoy. How difficult is it to translate existing C code to Rust? To get a complementary view from that of automatic C to Rust translators, we report on a user study asking humans to translate real-world C programs to Rust. Our participants are able to produce safe Rust translations, whereas state-of-the-art automatic tools are not able to do so. Our analysis highlights that the high-level strategy taken by users departs significantly from those of automatic tools we study. We also find that users often choose zero-cost (static) abstractions for temporal safety, which addresses a predominant component of runtime costs in other full memory safety defenses. User-provided translations showcase a rich landscape of specialized strategies to translate the same C program in different ways to safe Rust, which future automatic translators can consider., Comment: Accepted by NDSS Symposium 2025. Please cite the conference version of this paper, e.g., "Ruishi Li, Bo Wang, Tianyu Li, Prateek Saxena, Ashish Kundu. Translating C To Rust: Lessons from a User Study. In 32nd Annual Network and Distributed System Security Symposium (NDSS 2025)."

As AI-generated images become widespread, reliable watermarking is essential for content verification, copyright enforcement, and combating disinformation. Existing techniques rely on heuristic approaches and lack formal guarantees of undetectability, making them vulnerable to steganographic attacks that can expose or erase the watermark. Additionally, these techniques often degrade output quality by introducing perceptible changes, which is not only undesirable but an important barrier to adoption in practice. In this work, we introduce CLUE-Mark, the first provably undetectable watermarking scheme for diffusion models. CLUE-Mark requires no changes to the model being watermarked, is computationally efficient, and because it is provably undetectable is guaranteed to have no impact on model output quality. Our approach leverages the Continuous Learning With Errors (CLWE) problem -- a cryptographically hard lattice problem -- to embed watermarks in the latent noise vectors used by diffusion models. By proving undetectability via reduction from a cryptographically hard problem we ensure not only that the watermark is imperceptible to human observers or adhoc heuristics, but to \emph{any} efficient detector that does not have the secret key. CLUE-Mark allows multiple keys to be embedded, enabling traceability of images to specific users without altering model parameters. Empirical evaluations on state-of-the-art diffusion models confirm that CLUE-Mark achieves high recoverability, preserves image quality, and is robust to minor perturbations such JPEG compression and brightness adjustments. Uniquely, CLUE-Mark cannot be detected nor removed by recent steganographic attacks.

The management of type 1 diabetes has been revolutionized by the artificial pancreas system (APS), which automates insulin delivery based on continuous glucose monitor (CGM). While conventional closed-loop systems rely on CGM data, which leads to higher energy consumption at the sensors and increased data redundancy in the underlying communication network. In contrast, this paper proposes a self-triggered control mechanism that can potentially achieve lower latency and energy efficiency. The model for the APS consists of a state and input-constrained dynamical system affected by exogenous meal disturbances. Our self-triggered mechanism relies on restricting the state evolution within the robust control invariant of such a system at all times. To that end, using tools from reachability, we associate a safe time interval with such invariant sets, which denotes the maximum time for which the invariant set remains invariant, even without transmission of CGM data at all times.

Laplacian flows model the rate of change of each node's state as being proportional to the difference between its value and that of its neighbors. Typically, these flows capture diffusion or synchronization dynamics and are well-studied. Expanding on these classical flows, we introduce a pseudoinverse Laplacian flow system, substituting the Laplacian with its pseudoinverse within complex-valued networks. Interestingly, for undirected graphs and unsigned weight-balanced digraphs, Laplacian and the pseudoinverse Laplacian flows exhibit an interdependence in terms of consensus. To show this relation, we first present the conditions for achieving consensus in the pseudoinverse Laplacian flow system using the property of real eventually exponentially positivity. Thereafter, we show that the pseudoinverse Laplacian flow system converges to consensus if and only if the Laplacian flow system achieves consensus in the above-mentioned networks. However, these are only the sufficient conditions for digraphs. Further, we illustrate the efficacy of the proposed approach through examples, focusing primarily on power networks.

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields., Comment: Accepted for publication in Astronomy & Astrophysics. 24 pages, 21 figures

Vehicle-to-Everything (V2X) communication, which includes Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Pedestrian (V2P) networks, is gaining significant attention due to the rise of connected and autonomous vehicles. V2X systems require diverse Quality of Service (QoS) provisions, with V2V communication demanding stricter latency and reliability compared to V2I. The 5G New Radio-V2X (NR-V2X) standard addresses these needs using multi-numerology Orthogonal Frequency Division Multiple Access (OFDMA), which allows for flexible allocation of radio resources. However, V2I and V2V users sharing the same radio resources leads to interference, necessitating efficient power and resource allocation. In this work, we propose a novel resource allocation and sharing algorithm for 5G-based V2X systems. Our approach first groups Resource Blocks (RBs) into Resource Chunks (RCs) and allocates them to V2I users using the Gale-Shapley stable matching algorithm. Power is then allocated to RCs to facilitate efficient resource sharing between V2I and V2V users through a bisection search method. Finally, the Gale-Shapley algorithm is used to pair V2I and V2V users, maintaining low computational complexity while ensuring high performance. Simulation results demonstrate that our proposed Gale-Shapley Resource Allocation with Gale-Shapley Sharing (GSRAGS) achieves competitive performance with lower complexity compared to existing works while effectively meeting the QoS demands of V2X communication systems., Comment: 8 pages, 10 figures

Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer., Comment: 34 pages, 7 tables, 15 figs

We investigate a weighted variant of the interval stabbing problem, where the goal is to design an efficient data structure for a given set $\mathcal{I}$ of weighted intervals such that, for a query point $q$ and an integer $k>0$, we can report the $k$ intervals with largest weights among those stabbed by $q$. In this paper, we present a linear space solution with $O(\log n + k)$ query time. Moreover, we also present another trade-off for the problem.

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 for Capsule Vision Challenge 2024, we addressed the challenge of multiclass 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 baseline CNN model and improved performance with ResNet[2] for better feature extraction, followed by Vision Transformer (ViT)[3] to capture global dependencies. We further improve the results by using Multiscale Vision Transformer (MViT)[4] for improved hierarchical feature extraction, while Dual Attention Vision Transformer (DaViT) [5] delivered best results by combining spatial and channel attention methods. Our best balanced accuracy on validation set [6] was 0.8592 and Mean AUC was 0.9932. This methodology enabled us to improve model accuracy across a wide range of criteria, greatly surpassing all other methods.Additionally, our team capsule commandos achieved 7th place ranking with a test set[7] performance of Mean AUC: 0.7314 and balanced accuracy: 0.3235, Comment: Submission for 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

Besides being studied over static graphs heavily, the dispersion problem is also 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 kept as an open question in the literature. In this work, we study this problem on TVG considering $k\geq 1$ agents where at each discrete time step the adversary can remove at most one edge keeping the underlying graph connected. We have the following main results considering all agents start from a rooted initial configuration. Global communication and 1-hop visibility are must to solve dispersion for $pn$ ($p\geq 1$) co-located agents on a TVG even if agents have unlimited memory and knowledge of $n$. We provide an algorithm that disperses $n+1$ agents on TVG by dropping both the assumptions of global communication and 1-hop visibility using $O(\log n)$ memory per agent. We extend this algorithm to solve dispersion with $pn+1$ agents with the same model assumptions.

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., Comment: EMNLP 2024

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.