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Attracting and retaining high-quality teachers is a pressing policy concern. Increasing teacher salaries and creating more attractive compensation packages are often proposed as a potential solution. Signed into law in March 2023, the LEARNS Act increased Arkansas's minimum teacher salary from $36,000 to $50,000, guaranteed all teachers a minimum raise of $2,000, and added flexibility allowing school districts to deviate from seniority-based traditional salary schedules. To study school districts' adjustments to the new legislation, we collected information about districts' teacher compensation policies one year before and the first year of implementation. We also integrated this data with teachers' administrative records to study patterns of teacher retention and mobility. Our results reveal a more equitable distribution of starting teacher salaries across districts, with minimal variation. The LEARNS Act notably increased funding for rural and high-poverty districts, mitigating the negative association between starting salaries and district poverty rates. However, the initial effects on teacher retention and mobility were modest. While some positive trends emerged, such as reduced probabilities of teachers transitioning to non-instructional roles and increased new teacher placement in geographic areas of shortage, broader impacts on retention and mobility were limited in the first year of implementation.

This paper details the purpose, difficulties, theory, implementation, and results of developing a Fast Fourier Transform (FFT) using the prime factor algorithm on an embedded system. Many applications analyze the frequency content of signals, which is referred to as spectral analysis. Some of these applications include communication systems, radar systems, control systems, seismology, speech, music, sonar, finance, image processing, and neural networks. For many real-time applications, the speed at which the spectral analysis is performed is crucial. In order to perform spectral analysis, a Fourier transform is employed. For embedded systems, where spectral analysis is done digitally, a discrete Fourier transform (DFT) is employed. The main goal for this project is to develop an FFT for a 36-point DFT on the Nuvoton Nu-LB-NUC140V2. In this case, the prime factor algorithm is utilized to compute a fast DFT., Comment: 15 pages, 8 Figures

Ensemble Kalman inversion (EKI) is a derivative-free, particle-based optimization method for solving inverse problems. It can be shown that EKI approximates a gradient flow, which allows the application of methods for accelerating gradient descent. Here, we show that Nesterov acceleration is effective in speeding up the reduction of the EKI cost function on a variety of inverse problems. We also implement Nesterov acceleration for two EKI variants, unscented Kalman inversion and ensemble transform Kalman inversion. Our specific implementation takes the form of a particle-level nudge that is demonstrably simple to couple in a black-box fashion with any existing EKI variant algorithms, comes with no additional computational expense, and with no additional tuning hyperparameters. This work shows a pathway for future research to translate advances in gradient-based optimization into advances in gradient-free Kalman optimization.

This paper explores multiple optimization methods to improve the performance of rating-based reinforcement learning (RbRL). RbRL, a method based on the idea of human ratings, has been developed to infer reward functions in reward-free environments for the subsequent policy learning via standard reinforcement learning, which requires the availability of reward functions. Specifically, RbRL minimizes the cross entropy loss that quantifies the differences between human ratings and estimated ratings derived from the inferred reward. Hence, a low loss means a high degree of consistency between human ratings and estimated ratings. Despite its simple form, RbRL has various hyperparameters and can be sensitive to various factors. Therefore, it is critical to provide comprehensive experiments to understand the impact of various hyperparameters on the performance of RbRL. This paper is a work in progress, providing users some general guidelines on how to select hyperparameters in RbRL., Comment: Accepted to the Collaborative AI and Modeling of Humans Bridge Program at AAAI 2025

Reinforcement learning (RL), a common tool in decision making, learns policies from various experiences based on the associated cumulative return/rewards without treating them differently. On the contrary, humans often learn to distinguish from different levels of performance and extract the underlying trends towards improving their decision making for best performance. Motivated by this, this paper proposes a novel RL method that mimics humans' decision making process by differentiating among collected experiences for effective policy learning. The main idea is to extract important directional information from experiences with different performance levels, named ratings, so that policies can be updated towards desired deviation from these experiences with different ratings. Specifically, we propose a new policy loss function that penalizes distribution similarities between the current policy and failed experiences with different ratings, and assign different weights to the penalty terms based on the rating classes. Meanwhile, reward learning from these rated samples can be integrated with the new policy loss towards an integrated reward and policy learning from rated samples. Optimizing the integrated reward and policy loss function will lead to the discovery of directions for policy improvement towards maximizing cumulative rewards and penalizing most from the lowest performance level while least from the highest performance level. To evaluate the effectiveness of the proposed method, we present results for experiments on a few typical environments that show improved convergence and overall performance over the existing rating-based reinforcement learning method with only reward learning., Comment: Accepted to the Collaborative AI and Modeling of Humans Bridge Program at AAAI 2025

Optical trapping, also known as optical tweezing or optical levitation, is a technique that uses highly focused laser beams to manipulate micro- and nanoscopic particles. In optical traps driven by high-energy pulses, material non-linearity can result in unusual opto-mechanical effects, such as displaced equilibrium points. However, existing theoretical models of non-linear optical force on small particles consider smooth material dependence on the incident field strength alone, and not the feedback between the particle permittivity and internal field strength, which is, in turn, a function of the permittivity. The hysteresis effects of optical bistability in pulsed optical traps therefore elude existing optical force models. Here, we investigate a bistable optical trap, set up by counter-propagating ultrashort pulses, in which the optical force exerted on a particle depends not only on the field at the particle's current location, but on the particle's historic trajectory in the trap. The developed formalism will be important for designing optical traps and nanoparticle manipulation in pulsed field for various applications, including potentially time crystal demonstrations.

We model the total mass and galactic substructure in the strong lensing galaxy cluster MACS J0138.0-2155 using a combination of Chandra X-ray data, Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy, and Hubble Space Telescope imaging. MACS J0138.0-2155 lenses a source galaxy at z=1.95 which hosts two strongly lensed supernovae, Requiem and Encore. We find MACS J0138.0-2155 to have an X-ray temperature of 6.7 +/- 0.4 keV and a velocity dispersion of cluster member galaxies of 718^{+132}_{-182} km/s, which indicate a cluster mass of ~5 x 10^{14} solar masses. The round morphology of the X-ray emission indicates that this cluster is relaxed with an ellipticity within the lensing region of e=0.12 +/- 0.03. Using 18 of the brightest, non-blended, quiescent galaxies, we fit the cluster specific Faber-Jackson relation, including a set of 81 variations in the analysis choices to estimate the systematic uncertainties in our results. We find a slope of alpha = 0.26 +/- 0.06 (stat.) +/- 0.03 (sys.) with an intrinsic scatter of 31^{+8}_{-6} (stat.) +/- 4 (sys.) km/s at a reference velocity dispersion of ~220 km/s. We also report on significant galaxies along the line-of-sight potentially impacting the lens modeling, including a massive galaxy with stellar velocity dispersion of 291 +/- 3 km/s$ which lies close in projection to the central cluster galaxy. This galaxy is part of a small group at a slightly higher redshift than the cluster., Comment: submitted to Open Journal of Astrophysics

Hidden sector SUSY breaking where charged hidden sector fields obtain SUSY breaking vevs once seemed common in dynamical SUSY breaking (DSB). In such a case, scalars can obtain large masses but gauginos and A-terms gain loop-suppressed anomaly-mediated contributions which may be smaller by factors of 1/16\pi^2 ~1/160. This situation leads to models such as PeV or mini-split supersymmetry with m(scalars)~ 160 m(gauginos). In order to generate a light Higgs mass m_h~ 125 GeV, the scalar mass terms are required in the 10-100 TeV range, leading to large, unnatural contributions to the weak scale. Alternatively, in gravity mediation with singlet hidden sector fields, then m(scalars)~ m(gauginos)~ A-terms and the large A-terms lift m_h ->125 GeV even for natural values of m(stop1)~ 1-3 TeV. Requiring naturalness, which is probabilistically preferred by the string landscape, then the measured Higgs mass seems to favor singlets in the hidden sector, which can be common in metastable and retrofitted DSB models., Comment: 18 pages with 8 .png figures

Recent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (RL), on commodity GPUs. However, such successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynamics. Simulation techniques for soft bodies are comparatively several orders of magnitude slower, thereby limiting the use of RL due to sample complexity requirements. To address this challenge, this paper presents both a novel RL algorithm and a simulation platform to enable scaling RL on tasks involving rigid bodies and deformables. We introduce Soft Analytic Policy Optimization (SAPO), a maximum entropy first-order model-based actor-critic RL algorithm, which uses first-order analytic gradients from differentiable simulation to train a stochastic actor to maximize expected return and entropy. Alongside our approach, we develop Rewarped, a parallel differentiable multiphysics simulation platform that supports simulating various materials beyond rigid bodies. We re-implement challenging manipulation and locomotion tasks in Rewarped, and show that SAPO outperforms baselines over a range of tasks that involve interaction between rigid bodies, articulations, and deformables.

Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate actionable policies tailored to specific robotic embodiments. To address this, Visual-Language-Action (VLA) models have emerged, yet they face challenges in long-horizon spatial reasoning and grounded task planning. In this work, we propose the Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning, Emma-X. Emma-X leverages our constructed hierarchical embodiment dataset based on BridgeV2, containing 60,000 robot manipulation trajectories auto-annotated with grounded task reasoning and spatial guidance. Additionally, we introduce a trajectory segmentation strategy based on gripper states and motion trajectories, which can help mitigate hallucination in grounding subtask reasoning generation. Experimental results demonstrate that Emma-X achieves superior performance over competitive baselines, particularly in real-world robotic tasks requiring spatial reasoning., Comment: https://github.com/declare-lab/Emma-X, https://huggingface.co/declare-lab/Emma-X

Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool.

The string landscape statistical draw to large scalar soft masses leads to a mixed quasi-degeneracy/decoupling solution to the SUSY flavor and CP problems where first/second generation matter scalars lie in the 20-40 TeV range. With increasing first/second generation scalars, SUSY models actually become more natural due to two-loop RG effects which suppress the corresponding third generation soft masses. This can also lead to substantial parameter space regions which are forbidden by the presence of charge and/or color breaking (CCB) minima of the scalar potential. We outline the allowed SUSY parameter space for the gravity-mediated three extra-parameter-non-universal Higgs model NUHM3. The natural regions with m_h~ 125 GeV, \Delta_{EW}<~ 30 and decoupled first/second generation scalar are characterized by rather heavy gluinos and EW gauginos, but with rather small \mu and top-squarks not far beyond LHC Run 2 limits. This scenario also explains why SUSY has so far eluded discovery at LHC in that the parameter space with small scalar and gaugino masses is all excluded by the presence of CCB minima., Comment: 18 pages with 11 .png figures

Singular optics aims to understand and manipulate light's topological defects, pioneered by the discovery that phase vortex lines, strands of destructive interference, naturally occur in scalar wave fields. Monochromatic electromagnetic fields, however, are described by complex three-dimensional vectors that make individual scalar phase vortices in their vector components, which depend on the choice of co-ordinate basis, less meaningful. Instead, polarisation singularities can capture the vector texture of complicated, even non-paraxial light, with separate spatial descriptions for the electric $\mathbf{E}$ and magnetic $\mathbf{H}$ fields. But polarisation textures, too, are basis-dependent, because the laws of electromagnetism can be expressed not only by separate $\mathbf{E}$ and $\mathbf{H}$ fields, but by linear combinations of the two. We instead propose fundamental, basis-independent topological features generic in monochromatic electromagnetic fields: one- and two-dimensional structures that relate to time-averaged symmetries, including parity, duality and time-reversal, held locally by the combined electric and magnetic field polarisation geometry.

Macroscopic studies of cortical tissue reveal a prevalence of oscillatory activity, that reflect a fine tuning of neural interactions. This research extends neural field theories by incorporating generalized oscillatory dynamics into previous work on conservative or semi-conservative neural field dynamics. Prior studies have largely assumed isotropic connections among neural units; however, this study demonstrates that a broad range of anisotropic and fluctuating connections can still sustain oscillations. Using Lagrangian field methods, we examine different types of connectivity, their dynamics, and potential interactions with neural fields. From this theoretical foundation, we derive a framework that incorporates Hebbian and non-Hebbian learning, i.e., plasticity, into the study of neural fields via the concept of a connectivity field., Comment: 31 pages, 4 figures

Large language models (LLMs) have shown increasing competence in solving mathematical reasoning problems. However, many open-source LLMs still struggle with errors in calculation and semantic understanding during intermediate reasoning steps. In this work, we introduce Prove, a simple yet effective framework that leverages translated programs derived from natural language solutions as a verification mechanism to filter out potentially incorrect reasoning paths before aggregating final answers. Unlike vanilla majority voting, our approach filters out solutions whose corresponding program output is inconsistent with the generated solution, aggregating only those that pass verification. We conducted extensive experiments using 13 open-source LLMs from various model families and sizes, ranging from 0.5B to 13B parameters, across eight mathematical benchmarks. Our results show that Prove consistently outperforms vanilla majority voting as a heuristic for solving mathematical reasoning tasks across all model sizes and datasets, achieving improvements of up to 18% on GSM8K and 8% on MATH-500. Our codes are available at https://github.com/declare-lab/prove.

The study of transmission and progression of human papillomavirus (HPV) is crucial for understanding the incidence of cervical cancers, and has been identified as a priority worldwide. The complexity of the disease necessitates a detailed model of HPV transmission and its progression to cancer; to infer properties of the above we require a careful process that can match to imperfect or incomplete observational data. In this paper, we describe the HPVsim simulator to satisfy the former requirement; to satisfy the latter we couple this stochastic simulator to a process of emulation and history matching using the R package hmer. With these tools, we are able to obtain a comprehensive collection of parameter combinations that could give rise to observed cancer data, and explore the implications of the variability of these parameter sets as it relates to future health interventions., Comment: 21 pages, 15 figures; submitted to Epidemics

The ionization potential (IP) of radium monofluoride (RaF) was measured to be 4.969(2)[10] eV, revealing a relativistic enhancement in the series of alkaline earth monofluorides. The results are in agreement with a relativistic coupled-cluster prediction of 4.969[7] eV, incorporating up to quantum electrodynamics corrections. Using the same computational methodology, an improved calculation for the dissociation energy ($D_{0}$) of 5.54[5] eV is presented. This confirms that radium monofluoride joins the small group of diatomic molecules for which $D_{0}>\mathrm{IP}$, paving the way for precision control and interrogation of its Rydberg states.

In today's era, where large language models (LLMs) are integrated into numerous real-world applications, ensuring their safety and robustness is crucial for responsible AI usage. Automated red-teaming methods play a key role in this process by generating adversarial attacks to identify and mitigate potential vulnerabilities in these models. However, existing methods often struggle with slow performance, limited categorical diversity, and high resource demands. While Rainbow Teaming, a recent approach, addresses the diversity challenge by framing adversarial prompt generation as a quality-diversity search, it remains slow and requires a large fine-tuned mutator for optimal performance. To overcome these limitations, we propose Ferret, a novel approach that builds upon Rainbow Teaming by generating multiple adversarial prompt mutations per iteration and using a scoring function to rank and select the most effective adversarial prompt. We explore various scoring functions, including reward models, Llama Guard, and LLM-as-a-judge, to rank adversarial mutations based on their potential harm to improve the efficiency of the search for harmful mutations. Our results demonstrate that Ferret, utilizing a reward model as a scoring function, improves the overall attack success rate (ASR) to 95%, which is 46% higher than Rainbow Teaming. Additionally, Ferret reduces the time needed to achieve a 90% ASR by 15.2% compared to the baseline and generates adversarial prompts that are transferable i.e. effective on other LLMs of larger size. Our codes are available at https://github.com/declare-lab/ferret.

While the minimal anomaly-mediated SUSY breaking model (mAMSB) seems ruled out by constraints on Higgs mass, naturalness and wino dark matter, a slightly generalized version dubbed natural AMSB (nAMSB) remains both viable and compelling. Like mAMSB, nAMSB features winos as the lightest gauginos, but unlike mAMSB, nAMSB allows a small mu parameter so that higgsinos are the lightest of electroweakinos (EWinos). nAMSB spectra depend on the input value of gravitino mass m_{3/2}, where the lower range of m_{3/2} is excluded by LHC gluino pair searches while a higher m_{3/2} band is excluded by LHC limits on wino pair production followed by boosted hadronic wino decays. A remaining intermediate gap in m_{3/2} values remains allowed by present LHC searches, but appears to be completely explorable by high luminosity ugrades of LHC (HL-LHC). We explore a variety of compelling discovery channels that may allow one to close the intermediate gap in m_{3/2} values: 1. same-sign diboson +MET (SSdB) production arising from wino pair production, leading to same-sign dileptons plus MET, 2. trilepton production arising from wino pair production and 3. soft dilepton plus jet events from higgsino pair production, 4. top-squark pair production. From our signal-to-background analysis along a nAMSB model line, we expect HL-LHC to either discover or rule out the nAMSB model with 3000 fb^{-1} of integrated luminosity., Comment: 25 pages with 10 figures

Natural supersymmetry with light higgsinos is most likely to emerge from the string landscape since the volume of scan parameter space shrinks to tiny volumes for electroweak unnatural models. Rather general arguments favor a landscape selection of soft SUSY breaking terms tilted to large values, but tempered by the atomic principle: that the derived value of the weak scale in each pocket universe lie not too far from its measured value in our universe. But that leaves (at least) three different paradigms for gaugino masses in natural SUSY models: unified (as in nonuniversal Higgs models), anomaly-mediation form (as in natural AMSB) and mirage mediation form (with comparable moduli- and anomaly-mediated contributions). We perform landscape scans for each of these, and show they populate different, but overlapping, positions in m(\ell\bar{\ell}) and m(wino) space. The first of these may be directly measurable at high-lumi LHC via the soft opposite-sign dilepton plus jets plus MET signature arising from higgsino pair production while the second of these could be extracted from direct wino pair production leading to same-sign diboson production., Comment: 17 pages with 3 .png figures

In machine learning algorithm design, there exists a trade-off between the interpretability and performance of the algorithm. In general, algorithms which are simpler and easier for humans to comprehend tend to show worse performance than more complex, less transparent algorithms. For example, a random forest classifier is likely to be more accurate than a simple decision tree, but at the expense of interpretability. In this paper, we present an ensemble classifier design which classifies easier inputs using a highly-interpretable classifier (i.e., white box model), and more difficult inputs using a more powerful, but less interpretable classifier (i.e., black box model)., Comment: This paper was presented at ICICT2024 and will be published in conference proceedings by Springer LNNS. ISSN: 2367-3370, Series

The use of Virtual Reality (VR) technologies has been extensively researched in surgical and anatomical education. VR provides a lifelike and interactive environment where healthcare providers can practice and refresh their skills in a safe environment. VR has been shown to be as effective as traditional medical education teaching methods, with the potential to provide more cost-effective and convenient means of curriculum delivery, especially in rural and remote areas or in environments with limited access to hands-on training. In this sense, VR offers the potential to be used to support resuscitation training for healthcare providers such as the Neonatal Resuscitation Program (NRP). The NRP program is an evidence-based and standardized approach for training healthcare providers on the resuscitation of the newborn. In this article, we describe a VR simulation environment that was designed and developed to refresh the skills of NRP providers. To validate this platform, we compared the VR-NRP simulation with exposure to 360-degree immersive video. We found that both VR technologies were positively viewed by healthcare professionals and performed very similarly to each other. However, the VR simulation provided a significantly increased feeling of presence. Furthermore, participants found the VR simulation more useful, leading to improved experiential learning outcomes. Also, participants using VR simulation reported higher confidence in certain NRP skills, such as proper mask placement and newborn response evaluation. This research represents a step forward in understanding how VR and related extended reality (XR) technologies can be applied for effective, immersive medical education, with potential benefits for remote and rural healthcare providers.

We propose Ruby Teaming, a method that improves on Rainbow Teaming by including a memory cache as its third dimension. The memory dimension provides cues to the mutator to yield better-quality prompts, both in terms of attack success rate (ASR) and quality diversity. The prompt archive generated by Ruby Teaming has an ASR of 74%, which is 20% higher than the baseline. In terms of quality diversity, Ruby Teaming outperforms Rainbow Teaming by 6% and 3% on Shannon's Evenness Index (SEI) and Simpson's Diversity Index (SDI), respectively.

Computer models are widely used to study complex real world physical systems. However, there are major limitations to their direct use including: their complex structure; large numbers of inputs and outputs; and long evaluation times. Bayesian emulators are an effective means of addressing these challenges providing fast and efficient statistical approximation for computer model outputs. It is commonly assumed that computer models behave like a ``black-box'' function with no knowledge of the output prior to its evaluation. This ensures that emulators are generalisable but potentially limits their accuracy compared with exploiting such knowledge of constrained or structured output behaviour. We assume a ``grey-box'' computer model and establish a hierarchical emulation framework encompassing structured emulators which exploit known constrained and structured behaviour of constituent computer model outputs. This achieves greater physical interpretability and more accurate emulator predictions. This research is motivated by and applied to the commercially important TNO OLYMPUS Well Control Optimisation Challenge from the petroleum industry. We re-express this as a decision support under uncertainty problem. First, we reduce the computational expense of the analysis by identifying a representative subset of models using an efficient multi-model ensemble subsampling technique. Next we apply our hierarchical emulation methodology to the expected Net Present Value utility function with well control decision parameters as inputs., Comment: 41 pages, 14 figures

Electromagnetic quantities such as energy density, momentum, spin, and helicity bring meaning and intuition to electromagnetism and possess intricate interrelations, particularly prominent in complex non-paraxial near-fields. These quantities are conventionally expressed using electric and magnetic field vectors, yet the electric-magnetic basis is one among other often overlooked alternatives, including parallel-antiparallel and right-left-handed helicity bases, related to the parity and duality symmetries of electromagnetism. Projecting time-harmonic electromagnetic fields into a variety of bases allows re-interpreting established quantities and reveals underlying mathematical structures: a Bloch sphere which describes asymmetries in electromagnetic energy, a systematic path to unify and uncover relations between electromagnetic quantities, and the unlocking of symmetry-driven equations in light-matter interaction., Comment: 6 pages, 2 figures, 3 tables

Objective: We present a simple parameter, calculated from a single wearable sensor, that can be used to objectively measure disease severity in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) or Long COVID. We call this parameter UpTime. Methods: Prior research has shown that the amount of time a person spends upright, defined as lower legs vertical with feet on the floor, correlates strongly with ME/CFS disease severity. We use a single commercial inertial measurement unit (IMU) attached to the ankle to calculate the percentage of time each day that a person spends upright (i.e., UpTime) and number of Steps/Day. As Long COVID shares symptoms with ME/CFS, we also apply this method to determine Long COVID disease severity. We performed a trial with 55 subjects broken into three cohorts, healthy controls, ME/CFS, and Long COVID. Subjects wore the IMU on their ankle for a period of 7 days. UpTime and Steps/Day were calculated each day and results compared between cohorts. Results: UpTime effectively distinguishes between healthy controls and subjects diagnosed with ME/CFS ($\mathbf{p = 0.00004}$) and between healthy controls and subjects diagnosed with Long COVID ($\mathbf{p = 0.01185}$). Steps/Day did distinguish between controls and subjects with ME/CFS ($\mathbf{p = 0.01}$) but did not distinguish between controls and subjects with Long COVID ($\mathbf{p = 0.3}$). Conclusion: UpTime is an objective measure of ME/CFS and Long COVID severity. UpTime can be used as an objective outcome measure in clinical research and treatment trials. Significance: Objective assessment of ME/CFS and Long COVID disease severity using UpTime could spur development of treatments by enabling the effect of those treatments to be easily measured.

Natural supersymmetry (SUSY) with light higgsinos is perhaps the most plausible of all weak scale SUSY models while a variety of motivations point to (right) tau sleptons as the lightest of all the sleptons. We examine a SUSY model line with rather light right-staus embedded within natural SUSY. For light stau_1 of a few hundred GeV, then the decays stau_1 -> \tau\tchi_{1,2}^0 and \nu_\tau\tchi_1^- occur at comparable rates where the (higgsino-like) \tchi_1^\pm and \tchi_2^0 release only small visible energy: in this case, the expected \tau^+\tau^- +\eslt signature is diminished from usual expectations due to the presence of the nearly invisible decay mode \ttau_1 -> \nu_\tau\tchi_1^-. However, once m_{\ttau_1}> ~m(bino), then decays to binos such as \ttau_1 -> \tau\tchi_3^0 open up where \tchi_3^0 decays to higgsinos plus W^\pm, Z^0 and h at comparable rates. For these heavier staus, then stau pair production gives rise to diboson+\eslt events which may contain 0, 1 or 2 additional hard \tau leptons. From these considerations, we examine the potential for future discovery of tau-slepton pair production at high-luminosity LHC. While we do not find a 5\sigma HL-LHC discovery reach for 3000 fb^{-1}, we do find a 95\% CL exclusion reach, ranging between m_{\ttau_1}:100-450 GeV for m_{\tchi_1^0}~ 100 GeV. This latter reach disappears for m_{\tchi_1^0}>~ 200 GeV., Comment: 20 pages including 7 .png figures; version 2 should coincide with published version and fixes several typos

Large multimodal models extend the impressive capabilities of large language models by integrating multimodal understanding abilities. However, it is not clear how they can emulate the general intelligence and reasoning ability of humans. As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a collection of 2000 puzzle instances based on abstract patterns. With this dataset, we evaluate large multimodal models with abstract patterns based on fundamental concepts, including colors, numbers, sizes, and shapes. Through our experiments on state-of-the-art large multimodal models, we find that they are not able to generalize well to simple abstract patterns. Notably, GPT-4V achieves a score of 46.4% on single-concept puzzles, which shows that state-of-the-art models struggle on our dataset. To diagnose the reasoning challenges in large multimodal models, we progressively guide the models with our ground truth reasoning explanations for visual perception, inductive reasoning, and deductive reasoning. Our systematic analysis finds that the main bottlenecks of GPT-4V are weaker visual perception and inductive reasoning abilities. Through this work, we hope to shed light on the limitations of large multimodal models and how they can better emulate human cognitive processes in the future. Our data and code are available at https://puzzlevqa.github.io, Comment: ACL 2024 Camera Ready

This paper introduces the novel task of multimodal puzzle solving, framed within the context of visual question-answering. We present a new dataset, AlgoPuzzleVQA designed to challenge and evaluate the capabilities of multimodal language models in solving algorithmic puzzles that necessitate both visual understanding, language understanding, and complex algorithmic reasoning. We create the puzzles to encompass a diverse array of mathematical and algorithmic topics such as boolean logic, combinatorics, graph theory, optimization, search, etc., aiming to evaluate the gap between visual data interpretation and algorithmic problem-solving skills. The dataset is generated automatically from code authored by humans. All our puzzles have exact solutions that can be found from the algorithm without tedious human calculations. It ensures that our dataset can be scaled up arbitrarily in terms of reasoning complexity and dataset size. Our investigation reveals that large language models (LLMs) such as GPT4V and Gemini exhibit limited performance in puzzle-solving tasks. We find that their performance is near random in a multi-choice question-answering setup for a significant number of puzzles. The findings emphasize the challenges of integrating visual, language, and algorithmic knowledge for solving complex reasoning problems.

Observations and theoretical principles suggest that electromagnetic waves, including light, travel more slowly in dielectric media than in vacuum. Maxwell's equations, incorporating material dependent permittivity and permeability, elegantly capture this effect. Previous studies indicate that the observed slower speed is due to interference effects, with the actual electromagnetic fields in the dielectric propagating at the speed of light in vacuum. However, these studies did not provide explicit expressions for the field components moving at this speed. The aim of the present study is to address this gap by analysing the structure of the electromagnetic field components within the dielectric. We examine how these components, each traveling at the speed of light in vacuum, interact to produce a net field that appears to propagate more slowly. Our findings show that the observed slower propagation in dielectric media results from interference between two types of waves: a forward moving incident wave and a set of secondary waves, moving both in the forward and backward directions, and induced by the interaction of the incident wave with the dielectric medium. Both the incident wave and the secondary waves travel at the speed of light in vacuum. Importantly, we observe that the reflected wave caused by the impedance discontinuity at the boundary of the dielectric medium arises from secondary waves moving in the direction opposite to the incident wave., Comment: 12 pages, 4 figures