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This paper reports on the theoretical investigation of charged Higgs bosons and their coupling to fermions within the Two Higgs Doublet Model (THDM). The study focuses on the discovery potential of charged Higgs bosons predicted in Types III and IV at the future Circular Hadron-Hadron Collider (FCC-hh) with a center-of-mass energy of (\sqrt{s} = 100) TeV. By analyzing their decays, couplings to fermions, branching ratios, and production cross-section via (pp \rightarrow tH^-), we investigate the signatures of charged Higgs bosons, including kinematical distributions based on the background processes of (b\bar{b}) quarks., Comment: 24 pages, 16 figures, 1 table

Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data.

In Bayesian optimization, a black-box function is maximized via the use of a surrogate model. We apply distributed Thompson sampling, using a Gaussian process as a surrogate model, to approach the multi-agent Bayesian optimization problem. In our distributed Thompson sampling implementation, each agent receives sampled points from neighbors, where the communication network is encoded in a graph; each agent utilizes a Gaussian process to model the objective function. We demonstrate a theoretical bound on Bayesian Simple Regret, where the bound depends on the size of the largest complete subgraph of the communication graph. Unlike in batch Bayesian optimization, this bound is applicable in cases where the communication graph amongst agents is constrained. When compared to sequential Thompson sampling, our bound guarantees faster convergence with respect to time as long as there is a fully connected subgraph of at least two agents. We confirm the efficacy of our algorithm with numerical simulations on traditional optimization test functions, illustrating the significance of graph connectivity on improving regret convergence., Comment: 9 pages

This paper presents Auriga Robotics' autonomous vehicle, developed at Shahid Beheshti University's Robotics and Intelligent Automation Lab, as part of the team's entry for the 2024 RoboCup IranOpen competition. The vehicle is a 1:10 scale car equipped with a custom-designed chassis, a stepper motor for precision, and a range of sensors for autonomous navigation. Key hardware includes ESP32 microcontrollers that manage motor control and sensor data acquisition. The software system integrates computer vision, including YOLOv8 for sign detection and PiNet for lane detection, combined with control algorithms such as the Stanley, PID, and Pure Pursuit controllers. The vehicle's design emphasizes real-time decision-making, environmental mapping, and efficient localization, ensuring its ability to navigate complex driving scenarios.

Microscopic examination of slides prepared from tissue samples is the primary tool for detecting and classifying cancerous lesions, a process that is time-consuming and requires the expertise of experienced pathologists. Recent advances in deep learning methods hold significant potential to enhance medical diagnostics and treatment planning by improving accuracy, reproducibility, and speed, thereby reducing clinicians' workloads and turnaround times. However, the necessity for vast amounts of labeled data to train these models remains a major obstacle to the development of effective clinical decision support systems. In this paper, we propose the integration of topological deep learning methods to enhance the accuracy and robustness of existing histopathological image analysis models. Topological data analysis (TDA) offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. While deep learning methods capture local information from images, TDA features provide complementary global features. Our experiments on publicly available histopathological datasets demonstrate that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.

Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, pre-processing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end to end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a pre-processing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks.

The detection of scintillation light in noble-liquid detectors is necessary for identifying neutrino interaction candidates from beam, astrophysical, or solar sources. Large monolithic detectors typically have highly efficient light sensors, like photomultipliers, mounted outside their electric field. This option is not available for modular detectors that wish to maximize their active volume. The ArgonCube light readout system detectors (ArCLights) are large-area thin-wavelength-shifting (WLS) panels that can operate in highly proximate modular detectors and within the electric field. The WLS plastic forming the bulk structure of the ArCLight has Tetraphenyl Butadiene (TPB) and sheets of dichroic mirror layered across its surface. It is coupled to a set of six silicon photomultipliers (SiPMs). This publication compares TPB coating techniques for large surface areas and describes quality control methods for large-scale production., Comment: 10 pages, 11 figures

This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze a comprehensive, 10-year dataset provided by a major health insurance organization to develop prediction models for multiple observation windows using traditional machine learning methods such as Random Forest and XGBoost as well as deep learning approaches such as Long Short-Term Memory (LSTM) networks. Our findings demonstrate that the LSTM model, particularly with a 24-month observation window, exhibits superior performance in predicting ESRD progression, outperforming existing models in the literature. We further apply SHapley Additive exPlanations (SHAP) analysis to enhance interpretability, providing insights into the impact of individual features on predictions at the individual patient level. This study underscores the value of leveraging administrative claims data for CKD management and predicting ESRD progression., Comment: 10pages, 4 figures, AMIA 2024

We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tasks, supporting context lengths of up to 8192 tokens. The model includes a set of task-specific Low-Rank Adaptation (LoRA) adapters to generate high-quality embeddings for query-document retrieval, clustering, classification, and text matching. Evaluation on the MTEB benchmark shows that jina-embeddings-v3 outperforms the latest proprietary embeddings from OpenAI and Cohere on English tasks, while achieving superior performance compared to multilingual-e5-large-instruct across all multilingual tasks. With a default output dimension of 1024, users can flexibly reduce the embedding dimensions to as low as 32 without compromising performance, enabled by Matryoshka Representation Learning., Comment: 20 pages, pp11-13 references, pp14-20 appendix and experiment tables

Subordinators are infinitely divisible distributions on the positive half-line. They are often used as mixing distributions in Poisson mixtures. We show that appropriately scaled Poisson mixtures can approximate the mixing subordinator and we derive a rate of convergence in $L^p$ for each $p\in[1,\infty]$. This includes the Kolmogorov and Wasserstein metrics as special cases. As an application, we develop an approach for approximate simulation of the underlying subordinator. In the interest of generality, we present our results in the context of more general mixtures, specifically those that can be represented as differences of randomly stopped L\'evy processes. Particular focus is given to the case where the subordinator belongs to the class of tempered stable distributions.

This paper aims to investigate the impact of interference in social network algorithms via user-bot interactions, focusing on the Stochastic Bounded Confidence Model (SBCM). This paper explores two approaches: positioning bots controlled by agents into the network and targeted advertising under various circumstances, operating with an advertising budget. This study integrates the Deep Deterministic Policy Gradient (DDPG) algorithm and its variants to experiment with different Deep Reinforcement Learning (DRL). Finally, experimental results demonstrate that this approach can result in efficient opinion shaping, indicating its potential in deploying advertising resources on social platforms., Comment: 5 pages, 3 figures, 2 tables

Diabetic retinopathy (DR) is a consequence of diabetes mellitus characterized by vascular damage within the retinal tissue. Timely detection is paramount to mitigate the risk of vision loss. However, training robust grading models is hindered by a shortage of annotated data, particularly for severe cases. This paper proposes a framework for controllably generating high-fidelity and diverse DR fundus images, thereby improving classifier performance in DR grading and detection. We achieve comprehensive control over DR severity and visual features (optic disc, vessel structure, lesion areas) within generated images solely through a conditional StyleGAN, eliminating the need for feature masks or auxiliary networks. Specifically, leveraging the SeFa algorithm to identify meaningful semantics within the latent space, we manipulate the DR images generated conditionally on grades, further enhancing the dataset diversity. Additionally, we propose a novel, effective SeFa-based data augmentation strategy, helping the classifier focus on discriminative regions while ignoring redundant features. Using this approach, a ResNet50 model trained for DR detection achieves 98.09% accuracy, 99.44% specificity, 99.45% precision, and an F1-score of 98.09%. Moreover, incorporating synthetic images generated by conditional StyleGAN into ResNet50 training for DR grading yields 83.33% accuracy, a quadratic kappa score of 87.64%, 95.67% specificity, and 72.24% precision. Extensive experiments conducted on the APTOS 2019 dataset demonstrate the exceptional realism of the generated images and the superior performance of our classifier compared to recent studies., Comment: 30 pages, 17 figures

In this paper we show that, if an increasing sequence $\Lambda=(\lambda_k)_{k\in\mathbb{Z}}$ has gaps going to infinity $\lambda_{k+1}-\lambda_k\to +\infty$ when $k\to\pm\infty$, then for every $T>0$ and every sequence $(a_k)_{k\in\mathbb{Z}}$ and every $N\geq 1$, $$ A\sum_{k=0}^N\frac{|a_k|}{1+k}\leq\frac{1}{T}\int_{-T/2}^{T/2} \left|\sum_{k=0}^N a_k e^{2i\pi\lambda_k t}\right|\,\mbox{d}t$$ further, if $\sum_{k\in\mathbb{Z}}\dfrac{1}{1+|\lambda_k|}<+\infty$,$$ B\max_{|k|\leq N}|a_k|\leq\frac{1}{T}\int_{-T/2}^{T/2} \left|\sum_{k=-N}^N a_k e^{2i\pi\lambda_k t}\right|\,\mbox{d}t $$ where $A,B$ are constants that depend on $T$ and $\Lambda$ only. The first inequality was obtained by Nazarov for $T>1$ and the second one by Ingham for $T\geq 1$ under the condition that $\lambda_{k+1}-\lambda_k\geq 1$. The main novelty is that if those gaps go to infinity, then $T$ can be taken arbitrarily small. The result is new even when the $\lambda_k$'s are integers where it extends a result of McGehee, Pigno and Smith. The results are then applied to observability of Schr\"odinger equations with moving sensors.

This paper describes Auriga's @Work team and their robot, developed at Shahid Beheshti University Faculty of Electrical Engineering's Robotics and Intelligent Automation Lab for RoboCup 2024 competitions. The robot is designed for industrial tasks, optimizing efficiency in repetitive or hazardous environments. It features a 4-wheel Mecanum system for omnidirectional movement and a 5-degree-of-freedom manipulator arm with a 3D-printed gripper for object handling and navigation. The electronics include custom boards with ESP32 microcontrollers and an Nvidia Jetson Nano for real-time control. Key software components include Hector SLAM for mapping, A* path planning, and YOLO for object detection, supported by integrated sensors for enhanced navigation and collision avoidance.

The Standard Model (SM) is lepton flavor universal, and the recent measurements of lepton flavor universality in $B \to (K,K^*)\ell^{+}\ell^{-}$, for $\ell = \mu, \; e$, decays now lie close to the SM predictions. However, this is not the case for the $\tau$ to $\mu$ ratios in these decays, where there is still some window open for the new physics (NP), and to accommodate them various extensions to the SM are proposed. It will be interesting to identify some observables which are not only sensitive on the parametric space of such NP models but also have some discriminatory power. We find that the polarization of the $K^{*}$ may play an important role, therefore, we have computed the unpolarized and polarized lepton flavor universality ratios of $\tau$ to $\mu$ in $B\to K^{*}\ell^{+}\ell^{+}$, $\ell= \mu, \tau$ decays. The calculation shows that in most of the cases, the values of the various proposed observables fall within the current experimental sensitivity, and their study at some on going and future experiments will serve as a tool to segregate the variants of the NP models., Comment: 18 pages, 7 figures

We analysed six primary transits of the ultra-hot Jupiter KELT-9,b obtained with the HARPS-N high-resolution spectrograph in the context of the Global Architecture of Planetary Systems (GAPS2) project, to characterise the atmosphere via single-line analysis. We extracted the transmission spectrum of each individual line by comparing the master out-of-transit spectrum with the in-transit spectra and computing the weighted average of the tomography in the planet reference frame. We corrected for the centre-to-limb variation and the Rossiter-McLaughlin effect by modelling the region of the star disc obscured by the planet during the transit and subtracting it from the master-out spectrum. We detected all six observable lines of the Balmer series within the HARPS-N wavelength range, from H$\alpha$ to H$\zeta$, with a significance exceeding 5$\sigma$. We focussed on metal species, detecting Na I, Ca I, Ca II, Fe I, Fe II, Mg I, Ti II, Sc II, and Cr II lines. This is the first detection in the atmosphere of an exoplanet of H$\epsilon$ and H$\zeta$ lines, as well as of individual lines of Sc II and Cr II. Our detections are supported by a comparison with published synthetic transmission spectra of KELT-9b obtained accounting for non-local thermodynamic equilibrium effects. The results underline the presence of a systematic blueshift due to night-side to day-side winds. The single-line analysis allowed us not only to assess the presence of atomic species in the atmosphere of KELT-9 b, but also to further characterise the local stratification of the atmosphere. Coupling the height distribution of the detected species with the velocity shift retrieved, we acknowledged the height distribution of night-side to day-side winds. Moreover, the study of the rotational broadening of different species supports the prediction of a tidally locked planet rotating as a rigid body.

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this work we propose a number of incremental improvements to the ColBERT model architecture and training pipeline, using methods shown to work in the more mature single-vector embedding model training paradigm, particularly those that apply to heterogeneous multilingual data or boost efficiency with little tradeoff. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks., Comment: 8 pages, references at pp7,8; EMNLP workshop submission

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement., Comment: Published in Journal of Field Robotics

The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its impact on public health systems. In response, this study delves into the application of advanced machine learning (ML) techniques for predicting COVID-19 infection probability. We conducted a rigorous investigation into the efficacy of various ML models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with 3200 allocated for training and 800 for testing, our experiment offers comprehensive insights into the performance of these models in COVID-19 prediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. This underscores the efficacy of deep learning approaches in leveraging complex data patterns to identify COVID-19 infections accurately. This study underscores the critical role of machine learning, particularly deep learning methodologies, in augmenting early detection efforts amidst the ongoing pandemic. The success of DNN in accurately predicting COVID-19 infection probability highlights the importance of continued research and development in leveraging advanced technologies to combat infectious diseases.

This paper explores machine learning (ML) models for classifying lung cancer levels to improve diagnostic accuracy and prognosis. Through parameter tuning and rigorous evaluation, we assess various ML algorithms. Techniques like minimum child weight and learning rate monitoring were used to reduce overfitting and optimize performance. Our findings highlight the robust performance of Deep Neural Network (DNN) models across all phases. Ensemble methods, including voting and bagging, also showed promise in enhancing predictive accuracy and robustness. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges, indicating a need for further refinement. Overall, our study provides insights into ML-based lung cancer classification, emphasizing the importance of parameter tuning to optimize model performance and improve diagnostic accuracy in oncological care.

We report on the rejuvenation of thin films of polystyrene (PS) as they are heated from stable glassy states - prepared either through vapour deposition or physical aging. For films with thickness $h \gtrsim 200$ nm, the rejuvenation of vapour deposited stable PS glass films follow behaviour well-documented for other stable glasses. For films with thickness $h \lesssim 160$nm the behaviour of the vapour deposited films becomes more complicated. This behaviour is characterized for different film thicknesses. The results are compared with vapour deposited films that are aged after rejuvenation or spincast and aged. Collectively, the results of these studies strongly suggest two distinct mechanisms that can result in rejuvenation, and hence two distinct relaxation processes that can couple to the material density. The two physical mechanisms can provide insight into poorly understood or apparently contradictory aspects of $T_g$ measurements in thin PS films.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

We calculate the transmission coefficient of the super-Janus interface conformal field theory, both at weak and at strong coupling, where latter is described holographically as a domain-wall solution on AdS$_2\times S^2\times M_4\times\Sigma$. Surprisingly we find perfect agreement between the free and strong coupling answer, mirroring a similar unexpected equivalence previously found for the entanglement entropy.

Large language models (LLMs) are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. Despite their utility in a number of downstream NLP tasks, ample research has shown that LLMs are incapable of performing reasoning in tasks that require quantification over and the manipulation of symbolic variables (e.g., planning and problem solving); see for example [25][26]. In this document, however, we will focus on testing LLMs for their language understanding capabilities, their supposed forte. As we will show here, the language understanding capabilities of LLMs have been widely exaggerated. While LLMs have proven to generate human-like coherent language (since that's how they were designed), their language understanding capabilities have not been properly tested. In particular, we believe that the language understanding capabilities of LLMs should be tested by performing an operation that is the opposite of 'text generation' and specifically by giving the LLM snippets of text as input and then querying what the LLM "understood". As we show here, when doing so it will become apparent that LLMs do not truly understand language, beyond very superficial inferences that are essentially the byproduct of the memorization of massive amounts of ingested text.

Many fascinating properties discovered in graphene and transition metal dichalcogenide (TMD) moir\'e superlattices originate from flat bands and enhanced many-body effects. Here, we discover new many-electron excited states in TMD homobilayers. As optical resonances evolve with twist angle and doping in MoSe$_2$ bilayers, a unique type of ``charge-transfer" trions is observed when gradual changes in atomic alignment between the layers occur. In real space, the optically excited electron-hole pair mostly resides in a different site from the doped hole in a moir\'e supercell. In momentum space, the electron-hole pair forms in the single-particle-band $K$-valley, while the hole occupies the $\Gamma$-valley. The rich internal structure of this trion resonance arises from the ultra-flatness of the first valence band and the distinct influence of moir\'e potential modulation on holes and excitons. Our findings open new routes to realizing photon-spin transduction or implementing moir\'e quantum simulators with independently tunable fermion and boson densities., Comment: 11 pages, 10 figures

Fine-grained understanding of objects, attributes, and relationships between objects is crucial for visual-language models (VLMs). Existing benchmarks primarily focus on evaluating VLMs' capability to distinguish between two very similar \textit{captions} given an image. In this paper, we introduce a new, challenging benchmark termed \textbf{Vis}ual \textbf{Min}imal-Change Understanding (VisMin), which requires models to predict the correct image-caption match given two images and two captions. The image pair and caption pair contain minimal changes, i.e., only one aspect changes at a time from among the following: \textit{object}, \textit{attribute}, \textit{count}, and \textit{spatial relation}. These changes test the models' understanding of objects, attributes (such as color, material, shape), counts, and spatial relationships between objects. We built an automatic framework using large language models and diffusion models, followed by a rigorous 4-step verification process by human annotators. Empirical experiments reveal that current VLMs exhibit notable deficiencies in understanding spatial relationships and counting abilities. We also generate a large-scale training dataset to finetune CLIP and Idefics2, showing significant improvements in fine-grained understanding across benchmarks and in CLIP's general image-text alignment. We release all resources, including the benchmark, training data, and finetuned model checkpoints, at \url{https://vismin.net/}., Comment: Project URL at https://vismin.net/

We study the problem of online binary classification in settings where strategic agents can modify their observable features to receive a positive classification. We model the set of feasible manipulations by a directed graph over the feature space, and assume the learner only observes the manipulated features instead of the original ones. We introduce the Strategic Littlestone Dimension, a new combinatorial measure that captures the joint complexity of the hypothesis class and the manipulation graph. We demonstrate that it characterizes the instance-optimal mistake bounds for deterministic learning algorithms in the realizable setting. We also achieve improved regret in the agnostic setting by a refined agnostic-to-realizable reduction that accounts for the additional challenge of not observing agents' original features. Finally, we relax the assumption that the learner knows the manipulation graph, instead assuming their knowledge is captured by a family of graphs. We derive regret bounds in both the realizable setting where all agents manipulate according to the same graph within the graph family, and the agnostic setting where the manipulation graphs are chosen adversarially and not consistently modeled by a single graph in the family.

Objective: Virtual Reality (VR) is a technological interface that allows users to interact with a simulated environment. VR has been used extensively for mental health and clinical research. Mental health disorders are globally burdening health problems in the world. According to the Psychological Interventions Implementation Manual published by WHO on 6th March 2024, around one in eight people in the world lived with a mental disorder. This literature review is synthesized to find out the effects of VR therapy on stress, anxiety and depression. Method: We used Google Scholar database using keywords of VR, stress, anxiety and depression. Publication from last ten years (2014 to 1024) are considered. Researches only in the English language are included. All the papers and articles with the keyword VR missing were rejected. Result: Google Scholar yielded 17,700 results from our keywords. Nine studies met our search criteria that are included in this review. Out of nine, five studies encountered mental stress and gave effective results in reducing it by VR therapy. The other four targeted mood disorders, Social anxiety disorders, depression, loss of happiness and sleep deprivation. They also showed immense potential in reducing mental illness while using VR. Conclusion: Findings are in favor of the effectiveness of VR in reducing stress, anxiety and depression. Still, it is insufficient evidence to consider VR as solely independent treatment over the traditional medication. In future, the limitations can be overcome to relying on VR and using it in hospitals as a reliable source of cure for mental illness., Comment: 12 pages, 3 figures, 2 tables

X-ray polarimetry of solar flares is still a not well established field of observation of our star. Past polarimeters were not able to measure with a high significance the polarization in X-rays from solar flares. Moreover, they had no imaging capabilities and measured only the polarization by integrating on all the image of the source. We propose a mission concept based on a gas photoelectric polarimeter, coupled with multilayer lobster-eye optics, to perform imaging-spectro-polarimetry of solar flares while monitoring the entire solar disc., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024

DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) have saturated in speed and are hard to parallelize. This paper focuses on solving DCOPF problems using gradient-based routines on Graphics Processing Units (GPUs), which have massive parallelization capability. To formulate these problems, we pose a Lagrange dual associated with DCOPF (linear and quadratic cost curves), and then we explicitly solve the inner (primal) minimization problem with a dual norm. The resulting dual problem can be efficiently iterated using projected gradient ascent. After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems. We provide reliable and tight lower bounds for these problems with, at best, 5.4x speedup over a conventional solver.

In this effort we fine tuned different models based on BERT to detect the gender polarity of twitter accounts. We specially focused on analyzing the effect of using emojis and emoticons in performance of our model in classifying task. We were able to demonstrate that the use of these none word inputs alongside the mention of other accounts in a short text format like tweet has an impact in detecting the account holder's gender.

Temperature coefficient of resistance (TCR) of a bolometer can be tuned by modifying the thermal conductance of an absorbing materials since they sense radiations via the temperature change in the absorber. However, the thermal conductance of the absorber can be reduced by engineering the appropriate thermal isolation, which can be an ultimate solution towards making a highly sensitive thermal detector. Here, we have developed an atomically thin 2D bolometer detector made up of a mechanically transferred suspended multilayer-MoS2 flake, eliminating the use of challenging thin-film fabrication process. The strength of our detector lies on the two factors: its large surface-to-volume window to absorb the radiations; the suspended configuration which prevents the heat dissipation through the substrate and therefore reduces the thermal conductance. The bolometric response of the detector is tested in both modes, via the photoresponse and the thermal response. The prototype is found to exhibit a very high TCR ~ -9.5%/K with the least achievable thermal noise-equivalent power (NEP) ~ 0.61 pWHz-1/2, in ambient conditions at 328 K., Comment: 22 pages, 9 figures

We argue that the relative success of large language models (LLMs) is not a reflection on the symbolic vs. subsymbolic debate but a reflection on employing a successful bottom-up strategy of a reverse engineering of language at scale. However, and due to their subsymbolic nature whatever knowledge these systems acquire about language will always be buried in millions of weights none of which is meaningful on its own, rendering such systems utterly unexplainable. Furthermore, and due to their stochastic nature, LLMs will often fail in making the correct inferences in various linguistic contexts that require reasoning in intensional, temporal, or modal contexts. To remedy these shortcomings we suggest employing the same successful bottom-up strategy employed in LLMs but in a symbolic setting, resulting in explainable, language-agnostic, and ontologically grounded language models., Comment: 12 pages, 4 figures. arXiv admin note: text overlap with arXiv:2308.14199, arXiv:2306.00017

A major challenge in defending against adversarial attacks is the enormous space of possible attacks that even a simple adversary might perform. To address this, prior work has proposed a variety of defenses that effectively reduce the size of this space. These include randomized smoothing methods that add noise to the input to take away some of the adversary's impact. Another approach is input discretization which limits the adversary's possible number of actions. Motivated by these two approaches, we introduce a new notion of adversarial loss which we call distributional adversarial loss, to unify these two forms of effectively weakening an adversary. In this notion, we assume for each original example, the allowed adversarial perturbation set is a family of distributions (e.g., induced by a smoothing procedure), and the adversarial loss over each example is the maximum loss over all the associated distributions. The goal is to minimize the overall adversarial loss. We show generalization guarantees for our notion of adversarial loss in terms of the VC-dimension of the hypothesis class and the size of the set of allowed adversarial distributions associated with each input. We also investigate the role of randomness in achieving robustness against adversarial attacks in the methods described above. We show a general derandomization technique that preserves the extent of a randomized classifier's robustness against adversarial attacks. We corroborate the procedure experimentally via derandomizing the Random Projection Filters framework of \cite{dong2023adversarial}. Our procedure also improves the robustness of the model against various adversarial attacks.

Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-clip-v1 model to achieve the state-of-the-art performance on both text-image and text-text retrieval tasks., Comment: 4 pages, MFM-EAI@ICML2024

Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterative search algorithms and study the classical maximum coverage problem in graphs with chance constraints. Our goal is to evolve reliable chance constraint settings for a given graph where the performance of algorithms differs significantly not just in expectation but with high confidence. This allows to better learn and understand how different types of algorithms can deal with different types of constraint settings and supports automatic algorithm selection. We develop an evolutionary algorithm that provides sets of chance constraints that differentiate the performance of two stochastic search algorithms with high confidence. We initially use traditional approximation ratio as the fitness function of (1+1)~EA to evolve instances, which shows inadequacy to generate reliable instances. To address this issue, we introduce a new measure to calculate the performance difference for two algorithms, which considers variances of performance ratios. Our experiments show that our approach is highly successful in solving the instability issue of the performance ratios and leads to evolving reliable sets of chance constraints with significantly different performance for various types of algorithms.

Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a coherent nonlinear optical process with a strict symmetry selectivity and has several advantages over traditional fluorescence methods. We report the tomographic retrieval of the 3D second-order nonlinear optical susceptibility using two-dimensional holographic measurements of the SHG fields at different illumination angles and polarization states. The method is a generalization of the conventional linear ODT to the nonlinear scenario. We demonstrate the method with a numerically simulated nanoparticle distribution and an experiment with muscle tissue fibers. Our results show that SHG ODT does not only provide an effective contrast mechanism for label-free imaging but also due to the symmetry requirement enables the visualization of properties that are not otherwise accessible.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Several discrepancies in the decay of B-meson decay have drawn a lot of interest in the leptoquarks (LQ), making them an exciting discovery. The current research aims to discover the pair-production of leptoquarks that links strongly to the third generation of quarks and leptons at the center of mass energy $\sqrt{s}$=14 TeV, via proton-proton collisions at the Large Hadron Collider (LHC). Based on the lepton-quark coupling parameters and branching fractions, we separated our search into various benchmark points. The leading order (LO) signals and background processes are generated, while parton showering and hadronization is also performed to simulate the detector effects. The Boosted Decision Trees (BDTs), Multilayer Perceptron (MLP), and Likelihood (LH) methods are effective in improving signal-background discrimination compared to traditional cut-based analysis. The results indicate that these machine learning methods can significantly enhance the sensitivity in probing for new physics signals, such as LQs, at two different integrated luminosities. Specifically, the use of BDTs, MLP, and LH has led to higher signal significances and improved signal efficiency in both hadronic and semi-leptonic decay modes. The results suggest that the LQ masses of 500 GeV and 2.0 TeV in fully hadronic decay modes can be accurately probed with signal significance 176.70 (17.6) and 184.27 (0.01) for MVA (cut-based) at 1000 $fb^{-1}$, respectively. Similarly, in semi-leptonic decay mode the signal significance values are 168.56 and 181.89 at lowest and highest selected LQ masses respectively for MVA method only. The enhanced numbers by a factor of 2 are also reported at 3000 $fb^{-1}$., Comment: 20 pages, 14 figures, VI tables

In this paper we describe ECG-SMART-NET for identification of occlusion myocardial infarction (OMI). OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate referral for cardiac catheterization to restore blood flow to the heart. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram (ECG) and can be potentially fatal if not identified in a timely fashion. Previous works on this topic are scarce, and current state-of-the-art evidence suggests that both random forests with engineered features and convolutional neural networks (CNNs) are promising approaches to improve the ECG detection of OMI. While the ResNet architecture has been successfully adapted for use with ECG recordings, it is not ideally suited to capture informative temporal features within each lead and the spatial concordance or discordance across leads. We propose a clinically informed modification of the ResNet-18 architecture. The model first learns temporal features through temporal convolutional layers with 1xk kernels followed by a spatial convolutional layer, after the residual blocks, with 12x1 kernels to learn spatial features. The new ECG-SMART-NET was benchmarked against the original ResNet-18 and other state-of-the-art models on a multisite real-word clinical dataset that consists of 10,893 ECGs from 7,297 unique patients (rate of OMI = 6.5%). ECG-SMART-NET outperformed other models in the classification of OMI with a test AUC score of 0.889 +/- 0.027 and a test average precision score of 0.587 +/- 0.087., Comment: 7 pages, 7 figures, 5 tables

CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programmed workloads on such systems is indispensable as one single program typically cannot fully exploit all available resources. At the same time, power consumption is a key issue and often requires optimizing power allocations to the CPU and GPU while enforcing a total power constraint, in particular when the power/thermal requirements are strict. The result is a system-wide optimization problem with several knobs. In particular we focus on (1) co-scheduling decisions, i.e., selecting programs to co-locate in a space sharing manner; (2) resource partitioning on both CPUs and GPUs; and (3) power capping on both CPUs and GPUs. We solve this problem using predictive performance modeling using machine learning in order to coordinately optimize the above knob setups. Our experiential results using a real system show that our approach achieves up to 67% of speedup compared to a time-sharing-based scheduling with a naive power capping that evenly distributes power budgets across components.