Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-Budget Differentially Private Best Arm Identification

We study best arm identification (BAI) in linear bandits in the fixed-budget regime under differential privacy constraints, when the arm rewards are supported on the unit interval. Given a finite budget $T$ and a privacy parameter $\varepsilon>0$, the goal is to minimise the error probability in finding the arm with the largest mean after $T$ sampling rounds, subject to the constraint that the policy of the decision maker satisfies a certain {\em $\varepsilon$-differential privacy} ($\varepsilon$-DP) constraint. We construct a policy satisfying the $\varepsilon$-DP constraint (called {\sc DP-BAI}) by proposing the principle of {\em maximum absolute determinants}, and derive an upper bound on its error probability. Furthermore, we derive a minimax lower bound on the error probability, and demonstrate that the lower and the upper bounds decay exponentially in $T$, with exponents in the two bounds matching order-wise in (a) the sub-optimality gaps of the arms, (b) $\varepsilon$, and (c) the problem complexity that is expressible as the sum of two terms, one characterising the complexity of standard fixed-budget BAI (without privacy constraints), and the other accounting for the $\varepsilon$-DP constraint. Additionally, we present some auxiliary results that contribute to the derivation of the lower bound on the error probability. These results, we posit, may be of independent interest and could prove instrumental in proving lower bounds on error probabilities in several other bandit problems. Whereas prior works provide results for BAI in the fixed-budget regime without privacy constraints or in the fixed-confidence regime with privacy constraints, our work fills the gap in the literature by providing the results for BAI in the fixed-budget regime under the $\varepsilon$-DP constraint.
Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum Software Development Project

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts . Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile Quantum Software Project Success Prediction Model (AQSSPM). Methodology: Firstly, w e identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Na i ve Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterati ons. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile quantum projects considering the cost factor of a particular project
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening

The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. The advent of Large Language Models (LLMs) has notably enhanced the efficacy of these systems, showcasing their robust generalization abilities across diverse language-related tasks. Accompanying these developments are various agents based on LLMs, which facilitate their application in practical scenarios. This paper introduces a novel LLM-based agent framework for resume screening, aimed at enhancing efficiency and time management in recruitment processes. Our framework is distinct in its ability to efficiently summarize and grade each resume from a large dataset. Moreover, it utilizes LLM agents for decision-making, determining which candidates receive job offers, or which ones to bring in for interviews. To evaluate our framework, we constructed a dataset from actual resumes and conducted simulate a resume screening process. Subsequently, the outcomes of the simulation experiment were compared and subjected to detailed analysis. The results demonstrate that our automated resume screening framework is 11 times faster than traditional manual methods. Furthermore, by fine-tuning the LLMs, we observed a significant improvement in the F1 score, reaching 87.73\%, during the resume sentence classification phase. In the resume summarization and grading phase, our fine-tuned model surpassed the baseline performance of the GPT-3.5 model. Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data

Building cross-modal applications is challenging due to limited paired multi-modal data. Recent works have shown that leveraging a pre-trained multi-modal contrastive representation space enables cross-modal tasks to be learned from uni-modal data. This is based on the assumption that contrastive optimization makes embeddings from different modalities interchangeable. However, this assumption is under-explored due to the poorly understood geometry of the multi-modal contrastive space, where a modality gap exists. In our study, we provide a theoretical explanation of this space's geometry and introduce a three-step method, $C^3$ (Connect, Collapse, Corrupt), to bridge the modality gap, enhancing the interchangeability of embeddings. Our $C^3$ method significantly improves cross-modal learning from uni-modal data, achieving state-of-the-art results on zero-shot image / audio / video captioning and text-to-image generation.
The Seasonal and Interannual Variation of Diurnal Precipitation over the Tibetan Plateau and Its Downstream Regions Observed by the Tropical Rainfall Measuring Mission

Abstract Using nine years of Tropical Rainfall Measuring Mission (TRMM) 2A25 data, based on the probability density function of rainfall, a comparative analysis of the diurnal cycle and its seasonal and interannual variation for convective rain, stratiform rain, and total rain is made between the Tibetan Plateau and the downstream Yangtze River basin and East China Sea. The diurnal convective rain is stronger than the diurnal stratiform rain over the Yangtze River basin, and the convective rain peaks in the afternoon when the stratiform rain maximum happens in the early morning. Convective rain and stratiform rain both peak in the early morning over the East China Sea. The diurnal total rain over the Tibetan Plateau is stronger than its downstream regions. The diurnal cycle appears quite different among the four seasons over the Yangtze River basin, and the seasonal variation of diurnal convective rain is more apparent than diurnal stratiform rain. The seasonal variation of the diurnal cycle is weak over the East China Sea and Tibetan Plateau. The maximum of total rain happens in the afternoon during 1998–2002 over the Yangtze River basin, while it peaks in the early morning during 2003–2006, but no obvious phase differences can be found among years in the diurnal rain over the East China Sea and over the Tibetan Plateau.
Multitask Learning in Minimally Invasive Surgical Vision: A Review

Minimally invasive surgery (MIS) has revolutionized many procedures and led to reduced recovery time and risk of patient injury. However, MIS poses additional complexity and burden on surgical teams. Data-driven surgical vision algorithms are thought to be key building blocks in the development of future MIS systems with improved autonomy. Recent advancements in machine learning and computer vision have led to successful applications in analyzing videos obtained from MIS with the promise of alleviating challenges in MIS videos. Surgical scene and action understanding encompasses multiple related tasks that, when solved individually, can be memory-intensive, inefficient, and fail to capture task relationships. Multitask learning (MTL), a learning paradigm that leverages information from multiple related tasks to improve performance and aid generalization, is wellsuited for fine-grained and high-level understanding of MIS data. This review provides an overview of the current state-of-the-art MTL systems that leverage videos obtained from MIS. Beyond listing published approaches, we discuss the benefits and limitations of these MTL systems. Moreover, this manuscript presents an analysis of the literature for various application fields of MTL in MIS, including those with large models, highlighting notable trends, new directions of research, and developments.
Large Language Models are Null-Shot Learners

This paper presents null-shot prompting. Null-shot prompting exploits hallucination in large language models (LLMs) by instructing LLMs to utilize information from the"Examples"section that never exists within the provided context to perform a task. While reducing hallucination is crucial and non-negligible for daily and critical uses of LLMs, we propose that in the current landscape in which these LLMs still hallucinate, it is possible, in fact, to exploit hallucination to increase performance in performing tasks compared to standard zero-shot prompting. Experiments with eight LLMs show improvements in performance across the majority of eight datasets, including reading comprehension, arithmetic reasoning, and closed-book question answering. The observed inconsistency in increased relative performance across the LLMs also potentially indicates a different degree of inherent hallucination in each model. These differences show that it is possible to utilize null-shot prompting as a way to detect degrees of hallucination in LLMs using existing benchmarking datasets. We also perform ablation studies, including experimenting with a modified version of null-shot prompting that incorporates ideas from zero-shot chain-of-thought prompting, which shows different trends of results.
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training

Medical image segmentation aims to identify and locate abnormal structures in medical images, such as chest radiographs, using deep neural networks. These networks require a large number of annotated images with fine-grained masks for the regions of interest, making pre-training strategies based on classification datasets essential for sample efficiency. Based on a large-scale medical image classification dataset, our work collects explanations from well-trained classifiers to generate pseudo labels of segmentation tasks. Specifically, we offer a case study on chest radiographs and train image classifiers on the CheXpert dataset to identify 14 pathological observations in radiology. We then use Integrated Gradients (IG) method to distill and boost the explanations obtained from the classifiers, generating massive diagnosis-oriented localization labels (DoLL). These DoLL-annotated images are used for pre-training the model before fine-tuning it for downstream segmentation tasks, including COVID-19 infectious areas, lungs, heart, and clavicles. Our method outperforms other baselines, showcasing significant advantages in model performance and training efficiency across various segmentation settings.
Graph-based vulnerability assessment of resting-state functional brain networks in full-term neonates

Network disruption during early brain development can result in long-term cognitive impairments. In this study, we investigated rich-club organization in resting-state functional brain networks in full-term neonates using a multiscale connectivity analysis. We further identified the most influential nodes, also called spreaders, having higher impacts on the flow of information throughout the network. The network vulnerability to damage to rich-club (RC) connectivity within and between resting-state networks was also assessed using a graph-based vulnerability analysis. Our results revealed a rich club organization and small-world topology for resting-state functional brain networks in full term neonates, regardless of the network size. Interconnected mostly through short-range connections, functional rich-club hubs were confined to sensory-motor, cognitive-attention-salience (CAS), default mode, and language-auditory networks with an average cross-scale overlap of 36%, 20%, 15% and 12%, respectively. The majority of the functional hubs also showed high spreading potential, except for several non-RC spreaders within CAS and temporal networks. The functional networks exhibited high vulnerability to loss of RC nodes within sensorimotor cortices, resulting in a significant increase and decrease in network segregation and integration, respectively. The network vulnerability to damage to RC nodes within the language-auditory, cognitive-attention-salience, and default mode networks was also significant but relatively less prominent. Our findings suggest that the network integration in neonates can be highly compromised by damage to RC connectivity due to brain immaturity.
Generation mechanism and beaming of Jovian nKOM from 3D numerical modeling of Juno/Waves observations

The narrowband kilometric radiation (nKOM) is a Jovian low-frequency radio component identified as a plasma emission produced in the region of the Io plasma torus. Measurements from the Waves instrument onboard the Juno spacecraft permitted to establish the distribution of nKOM occurrence and intensity as a function of frequency and latitude. We have developed a 3D geometrical model that can simulate at large scale the plasma emissions occurrence observed by a spacecraft based on an internal Jovian magnetic field model and a diffusive equilibrium model of the plasma density in Jupiter's inner magnetosphere. With this model, we propose a new method to discriminate the generation mechanism, wave mode, beaming and radio source location of plasma emissions. Here, this method is applied to the study of the nKOM observed from all latitudes by the Juno/Waves experiment to identify which conditions reasonably reproduce the observed occurrence distribution versus frequency and latitude. The results allow us to exclude the two main nKOM models published so far, and to show that the emission must be produced at the local plasma frequency and beamed along its local gradient in the direction of decreasing frequencies. We also propose that depending on its latitude, Juno observes two distinct kinds of nKOM: the low frequency nKOM in ordinary mode at high latitudes and high frequency nKOM on extraordinary mode at low latitudes. Both radio source locations are found to be distributed near the centrifugal equator from the outer edge to the inner edge of the Io plasma torus.
Harnessing Orthogonality to Train Low-Rank Neural Networks

This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training. Our investigation reveals that an orthogonal basis within each multidimensional weight's SVD representation stabilizes during training. Building upon this, we introduce Orthogonality-Informed Adaptive Low-Rank (OIALR) training, a novel training method exploiting the intrinsic orthogonality of neural networks. OIALR seamlessly integrates into existing training workflows with minimal accuracy loss, as demonstrated by benchmarking on various datasets and well-established network architectures. With appropriate hyperparameter tuning, OIALR can surpass conventional training setups, including those of state-of-the-art models.
Development of technologies for enhancing the utilization of guar bean (Cyamopsis tetragonoloba) in grain based foods

The drought resistant and lesser explored legume, Guar bean (Cyamopsis tetragonoloba), was used to prepare muffins with higher protein content. In present study, the muffins were prepared using three varieties of treated guar flour i.e. G 80, Ageta 112, HG 365 at supplementation levels 10, 15 and 5 per cent respectively, for storage studies under different conditions. The moisture content, water activity (aw), free fatty acids, peroxide value, microbial analysis and sensory evaluation were done at a regular interval of seven days. Moisture content of muffins increased significantly with increase in time of storage under both ambient and refrigerated conditions. Water activity value increased with increase in time period under both the storage conditions. Addition of guar flour to muffin had no effect on the water activity that increased gradually upto 0.83. Guar supplemented muffins showed higher moisture content than control and maximum in G 80 supplemented muffins. Formation of free fatty acids (% oleic acid) and peroxide value increased with increased storage period. The changes in values were more readily pronounced at ambient temperatures. The overall acceptability was maximum for Ageta 112 supplemented muffins however the acceptability reduced with increased storage. The microbial analysis showed that refrigerated muffins were acceptable even at 28 th day of storage analysis while the muffins at ambient conditions showed lesser shelf life. Muffins supplemented with HG 365 were spoiled at 21 st day of storage analysis under ambient storage conditions.
Reproducibility of real-time time-dependent density functional theory calculations of electronic stopping power in warm dense matter

Real-time time-dependent density functional theory (TDDFT) is widely considered to be the most accurate available method for calculating electronic stopping powers from first principles, but there have been relatively few assessments of the consistency of its predictions across different implementations. This problem is particularly acute in the warm dense regime, where computational costs are high and experimental validation is rare and resource intensive. We report a comprehensive cross-verification of stopping power calculations in conditions relevant to inertial confinement fusion conducted using four different TDDFT implementations. We find excellent agreement among both the post-processed stopping powers and relevant time-resolved quantities for alpha particles in warm dense hydrogen. We also analyze sensitivities to a wide range of methodological details, including the exchange-correlation model, pseudopotentials, initial conditions, observable from which the stopping power is extracted, averaging procedures, projectile trajectory, and finite-size effects. We show that among these details, pseudopotentials, trajectory-dependence, and finite-size effects have the strongest influence, and we discuss different strategies for controlling the latter two considerations.
Code Simulation Challenges for Large Language Models

We investigate the extent to which Large Language Models (LLMs) can simulate the execution of computer code and algorithms. We begin by looking at straight line programs, and show that current LLMs demonstrate poor performance even with such simple programs -- performance rapidly degrades with the length of code. We then investigate the ability of LLMs to simulate programs that contain critical paths and redundant instructions. We also go beyond straight line program simulation with sorting algorithms and nested loops, and we show the computational complexity of a routine directly affects the ability of an LLM to simulate its execution. We observe that LLMs execute instructions sequentially and with a low error margin only for short programs or standard procedures. LLMs' code simulation is in tension with their pattern recognition and memorisation capabilities: on tasks where memorisation is detrimental, we propose a novel prompting method to simulate code execution line by line. Empirically, our new Chain of Simulation (CoSm) method improves on the standard Chain of Thought prompting approach by avoiding the pitfalls of memorisation.
TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras

Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and improve detection performance at night. However, event-based images lack color and texture compared to images from a conventional RGB camera. Considering that, data fusion between event-based and conventional cameras can combine the strengths of both modalities. For this purpose, extrinsic calibration is necessary. To the best of our knowledge, no targetless calibration between event-based and RGB cameras can handle multiple moving objects, nor does data fusion optimized for the domain of roadside ITS exist. Furthermore, synchronized event-based and RGB camera datasets considering roadside perspective are not yet published. To fill these research gaps, based on our previous work, we extended our targetless calibration approach with clustering methods to handle multiple moving objects. Furthermore, we developed an early fusion, simple late fusion, and a novel spatiotemporal late fusion method. Lastly, we published the TUMTraf Event Dataset, which contains more than 4,111 synchronized event-based and RGB images with 50,496 labeled 2D boxes. During our extensive experiments, we verified the effectiveness of our calibration method with multiple moving objects. Furthermore, compared to a single RGB camera, we increased the detection performance of up to +9 % mAP in the day and up to +13 % mAP during the challenging night with our presented event-based sensor fusion methods. The TUMTraf Event Dataset is available at https://innovation-mobility.com/tumtraf-dataset.
Towards large-scale quantum optimization solvers with few qubits

We introduce a variational quantum solver for combinatorial optimizations over $m=\mathcal{O}(n^k)$ binary variables using only $n$ qubits, with tunable $k>1$. The number of parameters and circuit depth display mild linear and sublinear scalings in $m$, respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. This leads to unprecedented quantum-solver performances. For $m=7000$, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for $m=2000$, an experiment with $n=17$ trapped-ion qubits featured MaxCut approximation ratios estimated to be beyond the hardness threshold $0.941$. To our knowledge, this is the highest quality attained experimentally on such sizes. Our findings offer a novel heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near term quantum devices.