Miracle-dz/UniFAR-FTUs huggingface dataset:
FashionSAP: Symbols and Attributes Prompt for Fine-Grained Fashion Vision-Language Pre-Training

Fashion vision-language pre-training models have shown efficacy for a wide range of downstream tasks. However, general vision-language pre-training models pay less attention to fine-grained domain features, while these features are important in distinguishing the specific domain tasks from general tasks. We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics. Firstly, we propose the fashion symbols, a novel abstract fashion concept layer, to represent different fashion items and to generalize various kinds of fine- grained fashion features, making modelling fine-grained attributes more effective. Secondly, the attributes prompt method is proposed to make the model learn specific attributes of fashion items explicitly. We design proper prompt templates according to the format of fashion data. Comprehensive experiments are conducted on two public fashion benchmarks, i.e., FashionGen and FashionIQ, and FashionSAP gets SOTA performances for four popular fashion tasks. The ablation study also shows the proposed abstract fashion symbols, and the attribute prompt method enables the model to acquire fine-grained semantics in the fashion domain effectively. The obvious performance gains from FashionSAP provide a new baseline for future fashion task research.11The source code is available at https://github.com/hssip/FashionSAP
An Associativity Threshold Phenomenon in Set-Associative Caches

In an α-way set-associative cache, the cache is partitioned into disjoint sets of size α, and each item can only be cached in one set, typically selected via a hash function. Set-associative caches are widely used and have many benefits, e.g., in terms of latency or concurrency, over fully associative caches, but they often incur more cache misses. As the set size α decreases, the benefits increase, but the paging costs worsen. In this paper we characterize the performance of an α-way set-associative LRU cache of total size k, as a function of α = α(k). We prove the following, assuming that sets are selected using a fully random hash function: For α = ω(log k), the paging cost of an α-way set-associative LRU cache is within additive O(1) of that a fully-associative LRU cache of size (1-o(1))k, with probability 1 - 1 / poly (k), for all request sequences of length poly (k). For α = o(log k), and for all c = O(1) and r = O(1), the paging cost of an α-way set-associative LRU cache is not within a factor c of that a fully-associative LRU cache of size k/r, for some request sequence of length O(k1.01). For α = ω(log k), if the hash function can be occasionally changed, the paging cost of an α-way set-associative LRU cache is within a factor 1 + o(1) of that a fully-associative LRU cache of size (1-o(1))k, with probability 1 - 1/poly (k), for request sequences of arbitrary (e.g., super-polynomial) length. Some of our results generalize to other paging algorithms besides LRU, such as least-frequently used (LFU).
Two-chaotic system image encryption schemes

An image encryption algorithm based on Baker map and two-chaotic system (Logistic dynamic system and Lorenz dynamic system) is presented.The algorithm splits the original image into small blocks and uses a random point as start point in baker map.The two-chaotic sequence,built with logistic system and Lorenz equation,combines with baker map to implement image encryption.This new algorithm can reduce the length of encryption sequence and enhance security of encryption.Experiments show that the algorithm is sensitive to encryption keys and can resist differential attacks.
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened

In decentralized settings, the shuffle model of differential privacy has emerged as a promising alternative to the classical local model. Analyzing privacy amplification via shuffling is a critical component in both single-message and multi-message shuffle protocols. However, current methods used in these two areas are distinct and specific, making them less convenient for protocol designers and practitioners. In this work, we introduce variation-ratio reduction as a unified framework for privacy amplification analyses in the shuffle model. This framework utilizes total variation bounds of local messages and probability ratio bounds of other users' blanket messages, converting them to indistinguishable levels. Our results indicate that the framework yields tighter bounds for both single-message and multi-message encoders (e.g., with local DP, local metric DP, or multi-message randomizers). Specifically, for a broad range of local randomizers having extremal probability design, our amplification bounds are precisely tight. We also demonstrate that variation-ratio reduction is well-suited for parallel composition in the shuffle model and results in stricter privacy accounting for common sampling-based local randomizers. Our experimental findings show that, compared to existing amplification bounds, our numerical amplification bounds can save up to $30\%$ of the budget for single-message protocols, $75\%$ of the budget for multi-message protocols, and $75\%$-$95\%$ of the budget for parallel composition. Additionally, our implementation for numerical amplification bounds has only $\tilde{O}(n)$ complexity and is highly efficient in practice, taking just $10$ seconds for $n=10^8$ users. The code for our implementation can be found at \url{https://github.com/wangsw/PrivacyAmplification}.
Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusion approach to assess the operator's posture in real-time and to help avoid uncomfortable or unsafe positions. The system adjusts the robot's movements and informs the operator of any incorrect or potentially harmful postures, reducing the risk of accidents, strain, and musculoskeletal disorders. The analysis is personalized, taking into account the unique anthropometric characteristics of each operator, to ensure optimal ergonomics. The results of our experiments show that the proposed approach leads to improved human body postures and offers a promising solution for enhancing the ergonomics of operators in collaborative tasks.
Reinforcement Learning from Passive Data via Latent Intentions

Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passive data, despite not having reward or action labels, can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions: measuring how the likelihood of future outcomes change when the agent acts to achieve a particular task. We propose a temporal difference learning objective to learn about intentions, resulting in an algorithm similar to conventional RL, but which learns entirely from passive data. When optimizing this objective, our agent simultaneously learns representations of states, of policies, and of possible outcomes in an environment, all from raw observational data. Both theoretically and empirically, this scheme learns features amenable for value prediction for downstream tasks, and our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.
MMD-Regularized Unbalanced Optimal Transport

We study the unbalanced optimal transport (UOT) problem, where the marginal constraints are enforced using Maximum Mean Discrepancy (MMD) regularization. Our work is motivated by the observation that the literature on UOT is focused on regularization based on $\phi$-divergence (e.g., KL divergence). Despite the popularity of MMD, its role as a regularizer in the context of UOT seems less understood. We begin by deriving a specific dual of MMD-regularized UOT (MMD-UOT), which helps us prove several useful properties. One interesting outcome of this duality result is that MMD-UOT induces novel metrics, which not only lift the ground metric like the Wasserstein but are also sample-wise efficient to estimate like the MMD. Further, for real-world applications involving non-discrete measures, we present an estimator for the transport plan that is supported only on the given ($m$) samples. Under certain conditions, we prove that the estimation error with this finitely-supported transport plan is also $\mathcal{O}(1/\sqrt{m})$. As far as we know, such error bounds that are free from the curse of dimensionality are not known for $\phi$-divergence regularized UOT. Finally, we discuss how the proposed estimator can be computed efficiently using accelerated gradient descent. Our experiments show that MMD-UOT consistently outperforms popular baselines, including KL-regularized UOT and MMD, in diverse machine learning applications.
Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can greatly improve predictions.
NutriFD: Proving the medicinal value of food nutrition based on food-disease association and treatment networks

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach

Multiple choice questions (MCQs) are an efficient and common way to assess reading comprehension (RC). Every MCQ needs a set of distractor answers that are incorrect, but plausible enough to test student knowledge. Distractor generation (DG) models have been proposed, and their performance is typically evaluated using machine translation (MT) metrics. However, MT metrics often misjudge the suitability of generated distractors. We propose DISTO: the first learned evaluation metric for generated distractors. We validate DISTO by showing its scores correlate highly with human ratings of distractor quality. At the same time, DISTO ranks the performance of state-of-the-art DG models very differently from MT-based metrics, showing that MT metrics should not be used for distractor evaluation.
Representative pure risk estimation by using data from epidemiologic studies, surveys, and registries: estimating risks for minority subgroups

Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. ‘Pseudoweighting’ methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification and inefficient due to highly variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that post-stratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates, to produce efficient and robust pure risk estimation (i.e. a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the USA, our findings suggest that hazard ratios for minorities are not generalisable, and that surveys can have inadequate numbers of events for minorities. Post-stratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups.
EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation

This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS). Large-size remote sensing images (LRIs) can lead to GPU memory exhaustion due to their extremely large size, which has been handled in previous works through either global-local fusion or multi-stage refinement, both of which are limited in their ability to fully exploit the abundant information available in LRIs. Unlike them, EHSNet features three memory-friendly modules to utilize the characteristics of LRIs: a long-range dependency module to develop long-range spatial context, an efficient cross-correlation module to build holistic contextual relationships, and a boundary-aware enhancement module to preserve complete object boundaries. Moreover, EHSNet manages to process holistic LRISS with the aid of memory offloading. To the best of our knowledge, EHSNet is the first method capable of performing holistic LRISS. To make matters better, EHSNet outperforms previous state-of-the-art competitors by a significant margin of +5.65 mIoU on FBP and +4.28 mIoU on Inria Aerial, demonstrating its effectiveness. We hope that EHSNet will provide a new perspective for LRISS. The code and models will be made publicly available.
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).
지방우세 딕슨기법을 이용한 슬관절 자기공명영상 지방신호분율과 골밀도 간의 상관관계 분석: 예비 연구

목적 MRI Dixon method (이하 DIXON) 기법을 이용하여 슬관절 지방우세 골조직의 지방신호분율(fat signal fraction; 이하 FF)을 구하고 골밀도(bone mineral density; 이하 BMD)와 상관관계 분석을 시행하였다. 대상과 방법 본원 슬관절 DIXON 영상을 촬영한 환자들 중 일 년 이내의 dual energy X-ray absorptiometry가 있는 환자 93명을 후향적으로 분석하였다. DIXON 영상에서 원위부 대퇴골 골간단(femur metaphyseal; 이하 Fm)과 근위부 경골 골간단(tibia metaphyseal; 이하 Tm)의 FF를 계산하였다. FF와 BMD 간 상관관계를 분석하였다. 또, 요추(lumbar spine; 이하 L), 대퇴경부(femoral neck; 이하 FN), 총대퇴골(femur total; 이하 FT)의 BMD 별로 나누고 FF에 대하여 Kruskal-Wallis H test를 시행하였다. 결과 전체 환자군에서 TmFF와 FN-BMD 사이에 유의미한 음의 상관관계를 보였다(r = -0.26, p < 0.05). 여성 환자에서는 TmFF가 FN-BMD, FT-BMD 및 L-BMD와 음의 상관관계를 보였다(r = -0.38, -0.28과 -0.27, p < 0.05). 남성 환자에서는 FmFF가 FN-BMD 및 FT-BMD와 음의 상관관계를 보였다(r = -0.58과 r = -0.42, p < 0.05). 여성 환자들을 세 군으로 나누었을 때 TmFF에 유의한 차이를 보였다(p < 0.05). 남성 환자들을 두 군으로 나누었을 때, FmFF이 군 간에 유의미한 차이를 보였다(p < 0.05). 결론 슬관절 주변의 FF와 BMD는 음의 상관관계를 보였으며, DIXON을 이용한 FF 측정이 BMD 스크리닝에 사용될 수 있는 가능성을 보였다.
Prader-Willi and Angelman Syndromes: Mechanisms and Management

Abstract Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are genetic imprinting disorders resulting from absent or reduced expression of paternal or maternal genes in chromosome 15q11q13 region, respectively. The most common etiology is deletion of the maternal or paternal 15q11q13 region. Methylation is the first line for molecular diagnostic testing; MS-MLPA is the most sensitive test. The molecular subtype of PWS/AS provides more accurate recurrence risk information for parents and for the individual affected with the condition. Management should include a multidisciplinary team by various medical subspecialists and therapists. Developmental and behavioral management of PWS and AS in infancy and early childhood includes early intervention services and individualized education programs for school-aged children. Here, we compare and discuss the mechanisms, pathophysiology, clinical features, and management of the two imprinting disorders, PWS and AS.
Search for Anomalous Production of Photon + Jets + Missing Transverse Energy in $p\bar{p}$ Collisions at $\sqrt{s}=1.96$~TeV Using the CDF II Detector

A model-independent signature-based search for physics beyond the Standard Model is performed in the photon~+ jets~+ missing transverse energy channel in \ppbar collisions at a center of mass energy of 1.96 TeV using the CDF II detector. Events with a photon + jets are predicted by the Standard Model and also by many theoretical models beyond the Standard Model. In the Standard Model, the main mechanisms for photon~+ jets production include quark-antiquark annihilation and quark-gluon scattering. No intrinsic missing transverse energy is present in any of these Standard Model processes. In this search, photon~+ $$\geq$$1 jet and photon~+ $$\geq$$2 jet events are analyzed with and without a minimum requirement on the missing transverse energy. Numerous mass distributions and kinematic distributions are studied and no significant excess over the background prediction is found. All results indicate good agreement with expectations of the Standard Model.
Semi-Markov shock models with additive damage

We examine a replacement model for a semi-Markov shock model with additive damage. Sufficient conditions are given for the optimality of control limit policies. The paper generalizes and unifies previous research in the area. In addition, we investigate in detail the practical modelling and computational aspects of the replacement problem using a semi-Markov modelling structure.
Flexible latent trait aggregation to analyze employability after the Ph.D. in Italy

The analysis of satisfaction, employability and economic perspectives after the Ph.D. in Italy has not received adequate attention in the past, especially in terms of comparison among universities. To analyze these aspects, in this paper we consider data from the survey ‘Statistica in TEma di Laureati e LAvoro’ on doctors who achieved the title on 2007, 2008 and 2009 [CILEA, Laureati STELLA, indagine occupazionale post-dottorato, dottori di ricerca 2007–2008, Tech. Rep., CILEA, Segrate, 2010; CILEA, Laureati STELLA, indagine occupazionale post-dottorato, dottori di ricerca 2008–2009, Tech. Rep., CILEA, Segrate, 2011]. To deal with the complex, multidimensional nature of the concept, we propose a flexible two-step procedure for the construction of a composite indicator, and make a first attempt to rank some Italian universities. In the first step, indicators for single dimensions are derived from cumulative link models with proportional odds. In the second step, aggregation through standard, ad hoc methods is proposed.
Glucocorticoid therapy - success or illusion

Glikokortykosteroidy są obecnie najcześciej stosowanymi w reumatologii lekami przeciwzapalnymi. Efekt hamujący zachodzi na wszystkich etapach procesu, tzn. od indukcji po ekspresje. Niewlaściwe stosowanie tych lekow prowadzi do objawow niepoządanych. Najwiekszy postep obserwuje sie w rozszyfrowaniu mechanizmu dzialania glikokortykosteroidow, zarowno w transmisji sygnalu, jak i w koncowym genowym i pozagenowym efekcie dzialania. Przyszlośc terapii zalezy od wprowadzenia preparatow sterujących receptorem glikokortykosteroidow, tzn. dzialających selektywnie z ominieciem drogi przez DNA. S u m m a r y
Terahertz imaging system with resonant tunneling diodes

We report a feasibility study of a terahertz imaging system with resonant tunneling diodes (RTDs) that oscillate at 0.30 THz. A pair of RTDs acted as an emitter and a detector in the system. Terahertz reflection images of opaque samples were acquired with our RTD imaging system. A spatial resolution of 1 mm, which is equal to the wavelength of the RTD emitter, was achieved. The signal-to-noise ratio (SNR) of the reflection image was improved by 6 dB by using polarization optics that reduced interference effects. Additionally, the coherence of the RTD enabled a depth resolution of less than 3 µm to be achieved by an interferometric technique. Thus, RTDs are an attractive candidate for use in small THz imaging systems.