Miracle-dz/UniFAR-FTUs huggingface dataset:
The Skin Barrier and Moisturization: Function, Disruption, and Mechanisms of Repair

Background: The anatomic layers of the skin are well-defined, and a functional model of the skin barrier has recently been described. Barrier disruption plays a key role in several skin conditions, and moisturization is recommended as an initial treatment in conditions such as atopic dermatitis. This review aimed to analyze the skin barrier in the context of the function model, with a focus on the mechanisms by which moisturizers support each of the functional layers of the skin barrier to promote homeostasis and repair. Summary: The skin barrier is comprised of four interdependent layers – physical, chemical, microbiologic, and immunologic – which maintain barrier structure and function. Moisturizers target disruption affecting each of these four layers through several mechanisms and were shown to improve transepidermal water loss in several studies. Occlusives, humectants, and emollients occlude the surface of the stratum corneum (SC), draw water from the dermis into the epidermis, and assimilate into the SC, respectively, in order to strengthen the physical skin barrier. Acidic moisturizers bolster the chemical skin barrier by supporting optimal enzymatic function, increasing ceramide production, and facilitating ideal conditions for commensal microorganisms. Regular moisturization may strengthen the immunologic skin barrier by reducing permeability and subsequent allergen penetration and sensitization. Key Messages: The physical, chemical, microbiologic, and immunologic layers of the skin barrier are each uniquely impacted in states of skin barrier disruption. Moisturizers target each of the layers of the skin barrier to maintain homeostasis and facilitate repair.
Towards Robust and Efficient Communications for Urban Air Mobility

For the realization of the future urban air mobility, reliable information exchange based on robust and efficient communication between all airspace participants will be one of the key factors to ensure safe operations. Especially in dense urban scenarios, the direct and fast information exchange between drones based on Drone-to-Drone communications is a promising technology for enabling reliable collision avoidance systems. However, to mitigate collisions and to increase overall reliability, unmanned aircraft still lack a redundant, higher-level safety net to coordinate and monitor traffic, as is common in today's civil aviation. In addition, direct and fast information exchange based on ad hoc communication is needed to cope with the very short reaction times required to avoid collisions and to cope with the the high traffic densities. Therefore, we are developing a \ac{d2d} communication and surveillance system, called DroneCAST, which is specifically tailored to the requirements of a future urban airspace and will be part of a multi-link approach. In this work we discuss challenges and expected safety-critical applications that will have to rely on communications for \ac{uam} and present our communication concept and necessary steps towards DroneCAST. As a first step towards an implementation, we equipped two drones with hardware prototypes of the experimental communication system and performed several flights around the model city to evaluate the performance of the hardware and to demonstrate different applications that will rely on robust and efficient communications.
Recovery of a time-dependent potential in hyperbolic equations on conformally transversally anisotropic manifolds

We study an inverse problem of determining a time-dependent potential appearing in the wave equation in conformally transversally anisotropic manifolds of dimension three or higher. These are compact Riemannian manifolds with boundary that are conformally embedded in a product of the real line and a transversal manifold. Under the assumption of the attenuated geodesic ray transform being injective on the transversal manifold, we prove the unique determination of time-dependent potentials from the knowledge of a certain partial Cauchy data set.
MAD: Meta Adversarial Defense Benchmark

Adversarial training (AT) is a prominent technique employed by deep learning models to defend against adversarial attacks, and to some extent, enhance model robustness. However, there are three main drawbacks of the existing AT-based defense methods: expensive computational cost, low generalization ability, and the dilemma between the original model and the defense model. To this end, we propose a novel benchmark called meta adversarial defense (MAD). The MAD benchmark consists of two MAD datasets, along with a MAD evaluation protocol. The two large-scale MAD datasets were generated through experiments using 30 kinds of attacks on MNIST and CIFAR-10 datasets. In addition, we introduce a meta-learning based adversarial training (Meta-AT) algorithm as the baseline, which features high robustness to unseen adversarial attacks through few-shot learning. Experimental results demonstrate the effectiveness of our Meta-AT algorithm compared to the state-of-the-art methods. Furthermore, the model after Meta-AT maintains a relatively high clean-samples classification accuracy (CCA). It is worth noting that Meta-AT addresses all three aforementioned limitations, leading to substantial improvements. This benchmark ultimately achieved breakthroughs in investigating the transferability of adversarial defense methods to new attacks and the ability to learn from a limited number of adversarial examples. Our codes and attacked datasets address will be available at https://github.com/PXX1110/Meta_AT.
Utilizing Whisper to Enhance Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids

Automated assessment of speech intelligibility in hearing aid (HA) devices is of great importance. Our previous work introduced a non-intrusive multi-branched speech intelligibility prediction model called MBI-Net, which achieved top performance in the Clarity Prediction Challenge 2022. Based on the promising results of the MBI-Net model, we aim to further enhance its performance by leveraging Whisper embeddings to enrich acoustic features. In this study, we propose two improved models, namely MBI-Net+ and MBI-Net++. MBI-Net+ maintains the same model architecture as MBI-Net, but replaces self-supervised learning (SSL) speech embeddings with Whisper embeddings to deploy cross-domain features. On the other hand, MBI-Net++ further employs a more elaborate design, incorporating an auxiliary task to predict frame-level and utterance-level scores of the objective speech intelligibility metric HASPI (Hearing Aid Speech Perception Index) and multi-task learning. Experimental results confirm that both MBI-Net++ and MBI-Net+ achieve better prediction performance than MBI-Net in terms of multiple metrics, and MBI-Net++ is better than MBI-Net+.
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems

Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feature monitoring and verification in recommendation use cases. Drifter addresses limitations of existing methods by delivering agile, responsive, and adaptable data quality monitoring, enabling real-time root cause analysis, drift detection and insights into problematic production events. Integrating state-of-the-art online feature ranking for sparse data and anomaly detection ideas, Drifter is highly scalable and resource-efficient, requiring only two threads and less than a gigabyte of RAM per production deployments that handle millions of instances per minute. Evaluation on real-world data sets demonstrates Drifter's effectiveness in alerting and mitigating data quality issues, substantially improving reliability and performance of real-time live recommender systems.
Selective Volume Mixup for Video Action Recognition

The recent advances in Convolutional Neural Networks (CNNs) and Vision Transformers have convincingly demonstrated high learning capability for video action recognition on large datasets. Nevertheless, deep models often suffer from the overfitting effect on small-scale datasets with a limited number of training videos. A common solution is to exploit the existing image augmentation strategies for each frame individually including Mixup, Cutmix, and RandAugment, which are not particularly optimized for video data. In this paper, we propose a novel video augmentation strategy named Selective Volume Mixup (SV-Mix) to improve the generalization ability of deep models with limited training videos. SV-Mix devises a learnable selective module to choose the most informative volumes from two videos and mixes the volumes up to achieve a new training video. Technically, we propose two new modules, i.e., a spatial selective module to select the local patches for each spatial position, and a temporal selective module to mix the entire frames for each timestamp and maintain the spatial pattern. At each time, we randomly choose one of the two modules to expand the diversity of training samples. The selective modules are jointly optimized with the video action recognition framework to find the optimal augmentation strategy. We empirically demonstrate the merits of the SV-Mix augmentation on a wide range of video action recognition benchmarks and consistently boot the performances of both CNN-based and transformer-based models.
Prospects from TESS and Gaia to Constrain the Flatness of Planetary Systems

The mutual inclination between planets orbiting the same star provides key information to understand the formation and evolution of multiplanet systems. In this work, we investigate the potential of Gaia astrometry in detecting and characterizing cold Jupiters in orbits exterior to the currently known Transiting Exoplanet Survey Satellite (TESS) planet candidates. According to our simulations, out of the ∼3350 systems expected to have cold Jupiter companions, Gaia, by its nominal 5 yr mission, should be able to detect ∼200 cold Jupiters and measure the orbital inclinations with a precision of σcosi<0.2 in ∼120 of them. These numbers are estimated under the assumption that the orbital orientations of the CJs follow an isotropic distribution, but these only vary slightly for less broad distributions. We also discuss the prospects from radial velocity follow-ups to better constrain the derived properties and provide a package to do quick forecasts using our Fisher matrix analysis. Overall, our simulations show that Gaia astrometry of cold Jupiters orbiting stars with TESS planets can distinguish dynamically cold (mean mutual inclination ≲5°) from dynamically hot systems (mean mutual inclination ≳20°), placing a new set of constraints on their formation and evolution.
A Read Margin Enhancement Circuit With Dynamic Bias Optimization for MRAM

This brief introduces a read bias circuit to improve readout yield of magnetic random access memories (MRAMs). A dynamic bias optimization (DBO) circuit is proposed to enable the real-time tracking of the optimal read voltage across processvoltage-temperature (PVT) variations within an MRAM array. It optimizes read performance by adjusting the read bias voltage dynamically for maximum sensing margin. Simulation results on a 28-nm 1Mb MRAM macro show that the tracking accuracy of the proposed DBO circuit remains above 90% even when the optimal sensing voltage varies up to 50%. Such dynamic tracking strategy further results in up to two orders of magnitude reduction in the bit error rate with respect to different variations, highlighting its effectiveness in enhancing MRAM performance and reliability.
Sharp Phase Transition for Multi Overlap Gap Property in Ising p-Spin Glass and Random k-SAT Models

The Ising $p$-spin glass and the random $k$-SAT models exhibit symmetric multi Overlap Gap Property ($m$-OGP), an intricate geometrical property which is a rigorous barrier against many important classes of algorithms. We establish that for both models, the symmetric $m$-OGP undergoes a sharp phase transition and we identify the phase transition point for each model: for any $m\in\mathbb{N}$, there exists $\gamma_m$ (depending on the model) such that the model exhibits $m$-OGP for $\gamma>\gamma_m$ and the $m$-OGP is provably absent for $\gamma<\gamma_m$, both with high probability, where $\gamma$ is some natural parameter of the model. Our results for the Ising $p$-spin glass model are valid for all large enough $p$ that remain constant as the number $n$ of spins grows, $p=O(1)$; our results for the random $k$-SAT are valid for all $k$ that grow mildly in the number $n$ of Boolean variables, $k=\Omega(\ln n)$. To the best of our knowledge, these are the first sharp phase transition results regarding the $m$-OGP. Our proofs are based on an application of the second moment method combined with a concentration property regarding a suitable random variable. While a standard application of the second moment method fails, we circumvent this issue by leveraging an elegant argument of Frieze~\cite{frieze1990independence} together with concentration.
Impact of Serotonin Pathway Gene Polymorphisms and Serotonin Levels in Suicidal Behaviour

Objective: Suicide is a significant public health issue and a major cause of death in all ages worldwide. Previous studies have shown the involvement of genetics in suicidal behaviour. This study aimed to assess the role of the genetic variants of the serotonin transporter genes (5HTTLPR, SLC6A4 intron 2) and receptor gene (5HTR2AT102C) in individuals who died of suicide. The study compares the serum levels of serotonin between the cases and controls. Methods: We conducted a case-control study with 120 cases and 126 controls. Socio-economic details of the subjects were collected using a semi-structured proforma, and psychological autopsy was used to collect details of medical and other clinical conditions. Blood was drawn after taking informed consent and serum levels of serotonin were estimated by enzyme-linked immunosorbent assay. Genotyping was performed using appropriate primers followed by polymerase chain reaction and a restriction fragment length polymorphism. Results: Mean age was 32.59 ± 12.58 for cases and 33.64 ± 9.78 for controls. The risk-associated LL genotype of 5HTTLPR was higher among cases. The heterozygous 12/10 genotype of SLC6A4 intron 2 polymorphism was increased among controls. Serum levels of serotonin were lower among cases. Variant genotypes of all the 3 polymorphisms showed significant interaction (OR = 39.26) indicating that this model may increase suicidal tendency. Conclusion: The findings of this study suggest that low serum levels of serotonin and two variants of the serotonin gene may influence suicide behaviour in a south Indian population.
Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world problems at the intersection of symbolic and statistical AI. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature (NP^PP-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from suboptimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC outperforms several baselines both in solution quality and running time.
NET Institute*

While much is known about optimal design of auctions within the context of a single item for sale, little is known about optimal design of large platform markets like eBay and auto auction houses that house large numbers of concurrent auctions. We attempt a macro-level empirical market design exercise by combining a unique dataset on tablet sales collected from eBay over the course of a year with methodologies developed by Bodoh-Creed (2011) and Backus and Lewis (2013). The former proposes a tractable approach to studying dynamic auction markets when the number of participants on both sides is sufficiently large. The model also delivers predictions on optimal fee schedule design – specifically, in terms of listing fees and percentage charges for a sale – for an auction house wishing to maximize profits by attracting the appropriate mix of buyers and sellers into the market. We begin by empirically investigating the key assumptions of the model which deliver (computational and empirical) tractability, and find that they are reasonable. We then estimate consumer demand, market supply, and the distributions of market entrants (this part still in progress). These figures are plugged into the BodohCreed (2011) framework in order to compute optimal fee schedules and draw comparisons to actual fee schedules, as well as to make policy prescriptions. Date: Current version: September 2013.
Statistically-Driven Alignment-Based Multiword Expression Identification for Technical Domains

Multiword Expressions (MWEs) are one of the stumbling blocks for more precise Natural Language Processing (NLP) systems. Particularly, the lack of coverage of MWEs in resources can impact negatively on the performance of tasks and applications, and can lead to loss of information or communication errors. This is especially problematic in technical domains, where a significant portion of the vocabulary is composed of MWEs. This paper investigates the use of a statistically-driven alignment-based approach to the identification of MWEs in technical corpora. We look at the use of several sources of data, including parallel corpora, using English and Portuguese data from a corpus of Pediatrics, and examining how a second language can provide relevant cues for this tasks. We report results obtained by a combination of statistical measures and linguistic information, and compare these to the reported in the literature. Such an approach to the (semi-)automatic identification of MWEs can considerably speed up lexicographic work, providing a more targeted list of MWE candidates.
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose

How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose
Management Research and Practice

There are various approaches to the measure quality of e-services. They have mentioned many dimensions of e-service quality provided by service organisations i.e. banking, restaurant, investment, trade etc. However, sometimes there may be confusions in selecting appropriate dimensions to assess e-services. Therefore, in the present paper author has tried to identify some important dimensions of service quality through extensive review of literature which can be used to assess overall quality of e-service. According to this review author found that, System availability, e-fulfilment, efficiency, cost-effectiveness, responsiveness, assurance, convenience and contact, perceived value and brand reputation are most important service quality dimensions which can be use to assess service quality of e-service and customers‟ satisfaction in e-service settings.
Self-supervised Multi-view Clustering in Computer Vision: A Survey

Multi-view clustering (MVC) has had significant implications in cross-modal representation learning and data-driven decision-making in recent years. It accomplishes this by leveraging the consistency and complementary information among multiple views to cluster samples into distinct groups. However, as contrastive learning continues to evolve within the field of computer vision, self-supervised learning has also made substantial research progress and is progressively becoming dominant in MVC methods. It guides the clustering process by designing proxy tasks to mine the representation of image and video data itself as supervisory information. Despite the rapid development of self-supervised MVC, there has yet to be a comprehensive survey to analyze and summarize the current state of research progress. Therefore, this paper explores the reasons and advantages of the emergence of self-supervised MVC and discusses the internal connections and classifications of common datasets, data issues, representation learning methods, and self-supervised learning methods. This paper does not only introduce the mechanisms for each category of methods but also gives a few examples of how these techniques are used. In the end, some open problems are pointed out for further investigation and development.
Proposal of recommended experimental protocols for in vitro and in vivo evaluation methods of boron agents for neutron capture therapy

Abstract Recently, boron neutron capture therapy (BNCT) has been attracting attention as a minimally invasive cancer treatment. In 2020, the accelerator-based BNCT with L-BPA (Borofalan) as its D-sorbitol complex (Steboronine®) for head and neck cancers was approved by Pharmaceutical and Medical Devices Agency for the first time in the world. As accelerator-based neutron generation techniques are being developed in various countries, the development of novel tumor-selective boron agents is becoming increasingly important and desired. The Japanese Society of Neutron Capture Therapy believes it is necessary to propose standard evaluation protocols at each stage in the development of boron agents for BNCT. This review summarizes recommended experimental protocols for in vitro and in vivo evaluation methods of boron agents for BNCT based on our experience with L-BPA approval.