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Understanding non-human primate behavior is crucial for improving animal welfare, modeling social behavior, and gaining insights into both distinctly human and shared behaviors. Despite recent advances in computer vision, automated analysis of primate behavior remains challenging due to the complexity of their social interactions and the lack of specialized algorithms. Existing methods often struggle with the nuanced behaviors and frequent occlusions characteristic of primate social dynamics. This study aims to develop an effective method for automated detection, tracking, and recognition of chimpanzee behaviors in video footage. Here we show that our proposed method, AlphaChimp, an end-to-end approach that simultaneously detects chimpanzee positions and estimates behavior categories from videos, significantly outperforms existing methods in behavior recognition. AlphaChimp achieves approximately 10% higher tracking accuracy and a 20% improvement in behavior recognition compared to state-of-the-art methods, particularly excelling in the recognition of social behaviors. This superior performance stems from AlphaChimp's innovative architecture, which integrates temporal feature fusion with a Transformer-based self-attention mechanism, enabling more effective capture and interpretation of complex social interactions among chimpanzees. Our approach bridges the gap between computer vision and primatology, enhancing technical capabilities and deepening our understanding of primate communication and sociality. We release our code and models and hope this will facilitate future research in animal social dynamics. This work contributes to ethology, cognitive science, and artificial intelligence, offering new perspectives on social intelligence., Comment: An eXpressive extension of ChimpACT [arXiv:2310.16447], proposes AlphaChimp for tracking and behavior recognition of chimpanzees. arXiv admin note: substantial text overlap with arXiv:2310.16447

We present multi-epoch optical spectropolarimetric and imaging polarimetric observations of the nearby Type II supernova (SN) 2023ixf discovered in M101 at a distance of 6.85 Mpc. The first imaging polarimetric observations were taken +2.33 days (60085.08 MJD) after the explosion, while the last imaging polarimetric data points (+73.19 and +76.19 days) were acquired after the fall from the light curve plateau. At +2.33 days there is strong evidence of circumstellar material (CSM) interaction in the spectra and the light curve. A significant level of polarization $P_r = 0.88\pm 0.06 \% $ seen during this phase indicates that this CSM is aspherical. We find that the polarization evolves with time toward the interstellar polarization level ($0.35\%$) during the photospheric phase, which suggests that the recombination photosphere is spherically symmetric. There is a jump in polarization ($P_r =0.65 \pm 0.08 \% $) at +73.19 days when the light curve falls from the plateau. This is a phase where polarimetric data is sensitive to non-spherical inner ejecta or a decrease in optical depth into the single scattering regime. We also present spectropolarimetric data that reveal line (de)polarization during most of the observed epochs. In addition, at +14.50 days we see an "inverse P Cygn" profile in the H and He line polarization, which clearly indicates the presence of asymmetrically distributed material overlying the photosphere. The overall temporal evolution of polarization is typical for Type II SNe, but the high level of polarization during the rising phase has only been observed in SN 2023ixf., Comment: 14 pages, 7 figures, submitted to ApJL, comments welcome

As cities adopt sustainable mobility solutions, electric scooters (e-scooters) offer both challenges and opportunities for public transportation systems. This study, the first in Latin America, examines the effects of e-scooters on public transport demand in Santiago, Chile, focusing on two scenarios: "generation" of trips (trips starting in study zones) and "attraction" of trips (trips ending in study zones). A negative binomial regression model was applied to data from public transport smart cards and e-scooter GPS. The methodology included urban area clustering and a differences-in-differences approach. The findings reveal significant regional differences: in the Central Region, public transport trips decreased by 21.38% in the generation scenario, while bus trips increased by 76.39%. In the Intermediate Region, metro trips increased by 70.05%, and in the Peripheral Region, bus trips increased by 84.64%. These results suggest that e-scooters reduce public transport usage in highly accessible areas but increase it in less accessible regions., Comment: 62 pages, 12 figures. Submitted to Transport Research Part D

In this paper, a definition of the gain and added noise of impedance matching networks for scanning microwave microscopy is given. This definition can be used to compare different impedance matching techniques independently of the instrument used to measure the S-parameter. As a demonstration, impedance matching devices consisting of a Beatty line, a tuner, and interferometric setups with and without amplifiers have been investigated. Measurement frequencies up to 28 GHz are used, and the maximal resulting gain found was 9504.7 per Siemens., Comment: IEEE Transactions on Instrumentation and Measurement (2024)

Introduction: Lung cancer screening (LCS) increases early-stage cancer detection which may reduce cancer treatment costs. Little is known about how receipt of LCS affects healthcare costs in real-world clinical settings. Methods: This retrospective study analyzed utilization and cost data from the Population-based Research to Optimize the Screening Process Lung Consortium. We included individuals who met age and smoking LCS eligibility criteria and were engaged within four healthcare systems between February 5, 2015, and December 31, 2021. Generalized linear models estimated healthcare costs from the payer perspective during 12-months prior and 12-months post baseline LCS. We compared these costs to eligible individuals who did not receive LCS. Sensitivity analyses expanded our sample to age-eligible individuals with any smoking history noted in the electronic health record. Secondary analyses examined costs among a sample diagnosed with lung cancer. We reported mean predicted costs with average values for all other explanatory variables. Results: We identified 10,049 eligible individuals who received baseline LCS and 15,233 who did not receive baseline LCS. Receipt of baseline LCS was associated with additional costs of $3,698 compared to individuals not receiving LCS. Secondary analyses showed suggestive evidence that LCS prior to cancer diagnosis decreased healthcare costs compared to cancer diagnosed without screening. Conclusion: These findings suggest LCS increases healthcare costs in the year following screening. However, LCS also improves early-stage cancer detection and may reduce treatment costs following diagnosis. These results can inform future simulation models to guide LCS recommendations, and aid health policy decision makers on resource allocation., Comment: 15 pages, 7 figures, 5 tables

One of the main obstacles for the signal extraction of the three point correlation function using photometric surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST), will be the prohibitive computation time required for dealing with a vast quantity of sources. Brute force algorithms, which naively scales as $\mathcal{O}(N^3)$ with the number of objects, can be further improved with tree methods but not enough to deal with large scale correlations of Rubin's data. However, a harmonic basis decomposition of these higher order statistics reduces the time dramatically, to scale as a two-point correlation function with the number of objects, so that the signal can be extracted in a reasonable amount of time. In this work, we aim to develop the framework to use these expansions within the Limber approximation for scalar (or spin-0) fields, such as galaxy counts, weak lensing convergence or aperture masses. We develop an estimator to extract the signal from catalogs and different phenomenological and theoretical models for its description. The latter includes halo model and standard perturbation theory, to which we add a simple effective field theory prescription based on the short range of non-locality of cosmic fields, significantly improving the agreement with simulated data. In parallel to the modeling of the signal, we develop a code that can efficiently calculate three points correlations of more than 200 million data points (a full sky simulation with Nside=4096) in $\sim$40 minutes on a single high-performance computing node, enabling a feasible analysis for the upcoming LSST data., Comment: 31 pages, 10 figures

Finding the optimal learning rate for language model pretraining is a challenging task. This is not only because there is a complicated correlation between learning rate, batch size, number of training tokens, model size, and other hyperparameters but also because it is prohibitively expensive to perform a hyperparameter search for large language models with Billions or Trillions of parameters. Recent studies propose using small proxy models and small corpus to perform hyperparameter searches and transposing the optimal parameters to large models and large corpus. While the zero-shot transferability is theoretically and empirically proven for model size related hyperparameters, like depth and width, the zero-shot transfer from small corpus to large corpus is underexplored. In this paper, we study the correlation between optimal learning rate, batch size, and number of training tokens for the recently proposed WSD scheduler. After thousands of small experiments, we found a power-law relationship between variables and demonstrated its transferability across model sizes. Based on the observation, we propose a new learning rate scheduler, Power scheduler, that is agnostic about the number of training tokens and batch size. The experiment shows that combining the Power scheduler with Maximum Update Parameterization (muP) can consistently achieve impressive performance with one set of hyperparameters regardless of the number of training tokens, batch size, model size, and even model architecture. Our 3B dense and MoE models trained with the Power scheduler achieve comparable performance as state-of-the-art small language models. We open-source these pretrained models at https://ibm.biz/BdKhLa.

SN 2023ixf was discovered in M101 within a day of explosion and rapidly classified as a Type II supernova with flash features. Here we present ultraviolet (UV) spectra obtained with the Hubble Space Telescope 14, 19, 24, and 66 days after explosion. Interaction between the supernova ejecta and circumstellar material (CSM) is seen in the UV throughout our observations in the flux of the first three epochs and asymmetric Mg II emission on day 66. We compare our observations to CMFGEN supernova models that include CSM interaction ($\dot{M}<10^{-3}$ M$_{\odot}$ yr$^{-1}$) and find that the power from CSM interaction is decreasing with time, from $L_{\rm sh}\approx5\times10^{42}$ erg s$^{-1}$ to $L_{\rm sh}\approx1\times10^{40}$ erg s$^{-1}$ between days 14 and 66. We examine the contribution of individual atomic species to the spectra on days 14 and 19, showing that the majority of the features are dominated by iron, nickel, magnesium, and chromium absorption in the ejecta. The UV spectral energy distribution of SN 2023ixf sits between that of supernovae which show no definitive signs of CSM interaction and those with persistent signatures assuming the same progenitor radius and metallicity. Finally, we show that the evolution and asymmetric shape of the Mg II $\lambda\lambda$ 2796, 2802 emission are not unique to SN 2023ixf. These observations add to the early measurements of dense, confined CSM interaction, tracing the mass-loss history of SN 2023ixf to $\sim33$ yr prior to the explosion and the density profile to a radius of $\sim5.7\times10^{15}$ cm. They show the relatively short evolution from a quiescent red supergiant wind to high mass loss., Comment: Accepted ApJL

This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight continual pretraining by gradually increasing its RoPE base frequency with repository-level file packing and length-upsampled long-context data. Additionally, we also release instruction-tuned models with long-context support which are derived by further finetuning the long context base models on a mix of permissively licensed short and long-context instruction-response pairs. While comparing to the original short-context Granite code models, our long-context models achieve significant improvements on long-context tasks without any noticeable performance degradation on regular code completion benchmarks (e.g., HumanEval). We release all our long-context Granite code models under an Apache 2.0 license for both research and commercial use.

High entropy alloys (HEAs) represent a novel frontier in metallurgical advancements, offering exceptional mechanical properties owing to their unique multicomponent nature. This study explores a novel strategy utilising commodity powders - Ni625, Invar36, and CoCrF75 - to tailor HEAs via metal injection moulding (MIM). The objective is to achieve cost-effective manufacturing while maintaining desired properties. The research involves mixing these powders in a specific proportion, integrating them with a sustainable polyethylene glycol-cellulose acetate butyrate binder system, and characterising the resulting feedstocks for MIM processing. Subsequent debinding and sintering steps were executed to densify and form a single face-centered cubic (FCC) phase HEA, followed by comprehensive analyses to evaluate the suitability of the developed HEA compositions. In addition, all MIM stages were thoroughly characterised to control the porosity of the final parts and to ensure a single FCC solid solution with promising mechanical properties in the developed non-equiatomic CoCrFeNiMox-type HEAs.

Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challenging benchmarks like SWE-Bench and Agent-Bench. However, to realize the true potential of LLMs as autonomous agents, they must learn to identify, call, and interact with external tools and application program interfaces (APIs) to complete complex tasks. These tasks together are termed function calling. Endowing LLMs with function calling abilities leads to a myriad of advantages, such as access to current and domain-specific information in databases and knowledge sources, and the ability to outsource tasks that can be reliably performed by tools, e.g., a Python interpreter or calculator. While there has been significant progress in function calling with LLMs, there is still a dearth of open models that perform on par with proprietary LLMs like GPT, Claude, and Gemini. Therefore, in this work, we introduce the GRANITE-20B-FUNCTIONCALLING model under an Apache 2.0 license. The model is trained using a multi-task training approach on seven fundamental tasks encompassed in function calling, those being Nested Function Calling, Function Chaining, Parallel Functions, Function Name Detection, Parameter-Value Pair Detection, Next-Best Function, and Response Generation. We present a comprehensive evaluation on multiple out-of-domain datasets comparing GRANITE-20B-FUNCTIONCALLING to more than 15 other best proprietary and open models. GRANITE-20B-FUNCTIONCALLING provides the best performance among all open models on the Berkeley Function Calling Leaderboard and fourth overall. As a result of the diverse tasks and datasets used for training our model, we show that GRANITE-20B-FUNCTIONCALLING has better generalizability on multiple tasks in seven different evaluation datasets.

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery., Comment: IEEE VLSI Test Symposium 2024 (VTS)

We present high-cadence photometric and spectroscopic observations of supernova (SN) 2024ggi, a Type II SN with flash spectroscopy features which exploded in the nearby galaxy NGC 3621 at $\sim$7 Mpc. The light-curve evolution over the first 30 hours can be fit by two power law indices with a break after 22 hours, rising from $M_V \approx -12.95$ mag at +0.66 days to $M_V \approx -17.91$ mag after 7 days. In addition, the densely sampled color curve shows a strong blueward evolution over the first few days and then behaves as a normal SN II with a redward evolution as the ejecta cool. Such deviations could be due to interaction with circumstellar material (CSM). Early high- and low-resolution spectra clearly show high-ionization flash features from the first spectrum to +3.42 days after the explosion. From the high-resolution spectra, we calculate the CSM velocity to be 37 $\pm~4~\mathrm{km\,s^{-1}} $. We also see the line strength evolve rapidly from 1.22 to 1.49 days in the earliest high-resolution spectra. Comparison of the low-resolution spectra with CMFGEN models suggests that the pre-explosion mass-loss rate of SN 2024ggi falls in a range of $10^{-3}$ to $10^{-2}$ M$_{\odot}$ yr$^{-1}$, which is similar to that derived for SN 2023ixf. However, the rapid temporal evolution of the narrow lines in the spectra of SN 2024ggi ($R_\mathrm{CSM} \sim 2.7 \times 10^{14} \mathrm{cm}$) could indicate a smaller spatial extent of the CSM than in SN 2023ixf ($R_\mathrm{CSM} \sim 5.4 \times 10^{14} \mathrm{cm}$) which in turn implies lower total CSM mass for SN 2024ggi., Comment: 23 pages, 15 figures, 4 tables, accepted for publication in ApJL

In this work, we compute the universe temperature for the cosmological horizon for the FLRW metric. For this purpose, we consider a scalar field on the cosmological horizon. This scalar field satisfies the Klein-Gordon equation in a curved space-time. Recently, some authors like Barrow, Bekenstein and others have proposed that the fundamental constants might vary with time. Along these lines of thought, we derive the electromagnetic momentum and electromagnetic field for the FLRW universe. This enables us to obtain a temporal dependence of the Hubble parameter which, in turn, induces a time dependence of the fundamental constants. In order to validate this model, the theoretical predictions are then compared with observational data as a function of the redshift, $z$, for the universe expansion. As a consequence, this time dependence on the fundamental constants makes it possible to predict a change in time of the informational content for the entropy of the universe surface area, this is expressed as the bit number by using the holographic principle., Comment: 27 pages, 2 figures. arXiv admin note: text overlap with arXiv:1007.4044 by other authors

We present photometric and spectroscopic observations of SN 2023fyq, a type Ibn supernova in the nearby galaxy NGC 4388 (D$\simeq$18~Mpc). In addition, we trace long-standing precursor emission at the position of SN 2023fyq using data from DLT40, ATLAS, ZTF, ASAS-SN, Swift, and amateur astronomer Koichi Itagaki. Precursor activity is observed up to nearly three years before the supernova explosion, with a relatively rapid rise in the final 100 days. The double-peaked post-explosion light curve reaches a luminosity of $\sim10^{43}~\rm erg\,s^{-1}$. The strong intermediate-width He lines observed in the nebular spectrum of SN 2023fyq imply the interaction is still active at late phases. We found that the precursor activity in SN 2023fyq is best explained by the mass transfer in a binary system involving a low-mass He star and a compact companion. An equatorial disk is likely formed in this process ($\sim$0.6$\rm M_{\odot}$), and the interaction of SN ejecta with this disk powers the main peak of the supernova. The early SN light curve reveals the presence of dense extended material ($\sim$0.3$\rm M_{\odot}$) at $\sim$3000$\rm R_{\odot}$ ejected weeks before the SN explosion, likely due to final-stage core silicon burning or runaway mass transfer resulting from binary orbital shrinking, leading to rapid rising precursor emission within $\sim$30 days prior to explosion. The final explosion could be triggered either by the core-collapse of the He star or by the merger of the He star with a compact object. SN 2023fyq, along with SN 2018gjx and SN 2015G, forms a unique class of Type Ibn SNe which originate in binary systems and are likely to exhibit detectable long-lasting pre-explosion outbursts with magnitudes ranging from $-$10 to $-$13., Comment: submitted to ApJ

Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use., Comment: Corresponding Authors: Rameswar Panda, Ruchir Puri; Equal Contributors: Mayank Mishra, Matt Stallone, Gaoyuan Zhang

The ionosphere affects radio signals by altering their speed, direction, and trajectory, causing a temporary delay known as ionospheric delay, which is directly related to the total electron content (TEC). Although research in other equatorial locations has explored TEC implications, qualitative research is required to predict its behavior in the ionosphere. This study aims to depict TEC intensity evolution through color maps using data from 14 GPS receivers placed across Ecuador. For this purpose, pseudorange observables collected from the stations were used to present a calculation method for the TEC and its evolution during January 2022. The results revealed an oscillatory behavior in the evolution of the TEC, with intensity peaks that, on some occasions, approach and even exceed 100 T ECU (TEC units), while its local minimums never reach zero values.

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.

Nuclear facilities must routinely survey their infrastructure for radiation contamination. Generally, this is done by trained professionals, wearing personal protective equipment (PPE) that swipe potentially contaminated surfaces and test the wipes under detectors. This approach leaves personnel vulnerable to radiation exposure and is not comprehensive. Robots address these inadequacies, offering a cost-effective solution with negligible downtime. We present a Robot Radiation Survey System (RRSS): a heterogeneous robot team to perform comprehensive alpha/beta/gamma radiation surveys. The RRSS system members, core capabilities, and comprehensive survey plan are addresses in this paper., Comment: 6 pages, 16 figures

We introduce API Pack, a massive multi-programming language dataset containing more than 1 million instruction-API call pairs to improve the API call generation capabilities of large language models. By fine-tuning CodeLlama-13B on 20,000 Python instances from API Pack, we enable it to outperform GPT-3.5 and GPT-4 in generating unseen API calls. Fine-tuning on API Pack also facilitates cross-programming language generalization by leveraging a large amount of data in one language and small amounts of data from other languages. Scaling the training data to 1 million instances further improves the model's ability to generalize to new APIs not used in training. To facilitate further research, we open-source the API Pack dataset, trained model, and associated source code at https://github.com/zguo0525/API-Pack.

We present comprehensive optical observations of SN~2021gmj, a Type II supernova (SN~II) discovered within a day of explosion by the Distance Less Than 40~Mpc (DLT40) survey. Follow-up observations show that SN~2021gmj is a low-luminosity SN~II (LL~SN~II), with a peak magnitude $M_V = -15.45$ and Fe~II velocity of $\sim 1800 \ \mathrm{km} \ \mathrm{s}^{-1}$ at 50 days past explosion. Using the expanding photosphere method, we derive a distance of $17.8^{+0.6}_{-0.4}$~Mpc. From the tail of the light curve we obtain a radioactive nickel mass of $0.014 \pm 0.001$ M$_{\odot}$. The presence of circumstellar material (CSM) is suggested by the early-time light curve, early spectra, and high-velocity H$\alpha$ in absorption. Analytical shock-cooling models of the light curve cannot reproduce the fast rise, supporting the idea that the early-time emission is partially powered by the interaction of the SN ejecta and CSM. The inferred low CSM mass of 0.025 M$_{\odot}$ in our hydrodynamic-modeling light curve analysis is also consistent with our spectroscopy. We observe a broad feature near 4600 \AA, which may be high-ionization lines of C, N, or/and He~II. This feature is reproduced by radiation-hydrodynamic simulations of red supergiants with extended atmospheres. Several LL~SNe~II show similar spectral features, implying that high-density material around the progenitor may be common among them., Comment: Accepted version at ApJ