Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Deep Reinforcement Learning for Adaptive Medium Access and Modulation in Shared Spectrum

Spectrum scarcity has led to growth in the use of unlicensed spectrum for cellular systems. This motivates intelligent adaptive approaches to spectrum access for both WiFi and 5G that improve upon traditional carrier sensing and listen-before-talk methods. We study decentralized contention-based medium access for base stations (BSs) of a single Radio Access Technology (RAT) operating on unlicensed shared spectrum. We devise a learning-based algorithm for both contention and adaptive modulation that attempts to maximize a network-wide downlink throughput objective. We formulate and develop novel distributed implementations of two deep reinforcement learning approaches - Deep Q Networks and Proximal Policy Optimization - modelled on a two stage Markov decision process. Empirically, we ﬁnd the (proportional fairness) reward accumulated by the policy gradient approach to be signiﬁcantly higher than even a genie-aided adaptive energy detection threshold. Our approaches are further validated by improved sum and peak throughput. The scalability of our approach to large networks is demonstrated via an improved cumulative reward earned on both indoor and outdoor layouts with a large number of BSs.
Using Physiological Information to Classify Task Difficulty in Human-Swarm Interaction

Human-swarm interaction has recently gained attention due to its plethora of new applications in disaster relief, surveillance, rescue, and exploration. However, if the task difficulty increases, the performance of the human operator decreases, thereby decreasing the overall efficacy of the human-swarm team. Thus, it is critical to identify the task difficulty and adaptively allocate the task to the human operator to maintain optimal performance. In this direction, we study the classification of task difficulty in a human-swarm interaction experiment performing a target search mission. The human may control platoons of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to search a partially observable environment during the target search mission. The mission complexity is increased by introducing adversarial teams that humans may only see when the environment is explored. While the human is completing the mission, their brain activity is recorded using an electroencephalogram (EEG), which is used to classify the task difficulty. We have used two different approaches for classification: A feature-based approach using coherence values as input and a deep learning-based approach using raw EEG as input. Both approaches can classify the task difficulty well above the chance. The results showed the importance of the occipital lobe (O1 and O2) coherence feature with the other brain regions. Moreover, we also study individual differences (expert vs. novice) in the classification results. The analysis revealed that the temporal lobe in experts (T4 and T3) is predominant for task difficulty classification compared with novices.
Regret Lower Bound and Optimal Algorithm for High-Dimensional Contextual Linear Bandit

In this paper, we consider the multi-armed bandit problem with high-dimensional features. First, we prove a minimax lower bound, $\mathcal{O}\big((\log d)^{\frac{\alpha+1}{2}}T^{\frac{1-\alpha}{2}}+\log T\big)$, for the cumulative regret, in terms of horizon $T$, dimension $d$ and a margin parameter $\alpha\in[0,1]$, which controls the separation between the optimal and the sub-optimal arms. This new lower bound unifies existing regret bound results that have different dependencies on T due to the use of different values of margin parameter $\alpha$ explicitly implied by their assumptions. Second, we propose a simple and computationally efficient algorithm inspired by the general Upper Confidence Bound (UCB) strategy that achieves a regret upper bound matching the lower bound. The proposed algorithm uses a properly centered $\ell_1$-ball as the confidence set in contrast to the commonly used ellipsoid confidence set. In addition, the algorithm does not require any forced sampling step and is thereby adaptive to the practically unknown margin parameter. Simulations and a real data analysis are conducted to compare the proposed method with existing ones in the literature.
User-Defined Functions for HDF5

Scientific datasets are known for their challenging storage demands and the associated processing pipelines that transform their information. Some of those processing tasks include filtering, cleansing, aggregation, normalization, and data format translation -- all of which generate even more data. In this paper, we present an infrastructure for the HDF5 file format that enables dataset values to be populated on the fly: task-related scripts can be attached into HDF5 files and only execute when the dataset is read by an application. We provide details on the software architecture that supports user-defined functions (UDFs) and how it integrates with hardware accelerators and computational storage. Moreover, we describe the built-in security model that limits the system resources a UDF can access. Last, we present several use cases that show how UDFs can be used to extend scientific datasets in ways that go beyond the original scope of this work.
Toward Efficient and Robust Multiple Camera Visual-inertial Odometry

Efficiency and robustness are the essential criteria for the visual-inertial odometry (VIO) system. To process massive visual data, the high cost on CPU resources and computation latency limits VIO's possibility in integration with other applications. Recently, the powerful embedded GPUs have great potentials to improve the front-end image processing capability. Meanwhile, multi-camera systems can increase the visual constraints for back-end optimization. Inspired by these insights, we incorporate the GPU-enhanced algorithms in the field of VIO and thus propose a new front-end with NVIDIA Vision Programming Interface (VPI). This new front-end then enables multi-camera VIO feature association and provides more stable back-end pose optimization. Experiments with our new front-end on monocular datasets show the CPU resource occupation rate and computational latency are reduced by 40.4% and 50.6% without losing accuracy compared with the original VIO. The multi-camera system shows a higher VIO initialization success rate and better robustness overall state estimation.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration

We introduce iFᴀᴄᴇᴛSᴜᴍ, a web application for exploring topical document collections. iFᴀᴄᴇᴛSᴜᴍ integrates interactive summarization together with faceted search, by providing a novel faceted navigation scheme that yields abstractive summaries for the user’s selections. This approach offers both a comprehensive overview as well as particular details regard-ing subtopics of choice. The facets are automatically produced based on cross-document coreference pipelines, rendering generic concepts, entities and statements surfacing in the source texts. We analyze the effectiveness of our application through small-scale user studies that suggest the usefulness of our tool.
Characterizing random one-dimensional media with an embedded reflector via scattered waves

We show in random matrix theory, microwave measurements, and computer simulations that the mean free path of a random medium and the strength and position of an embedded reflector can be determined from radiation scattered by the system. The mean free path and strength of the reflector are determined from the statistics of transmission. The statistics of transmission are independent of the position of the reflector. The reflector’s position can be found, however, from the average dwell time for waves incident from one side of the sample.
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction

State-of-the-art NLP models can adopt shallow heuristics that limit their generalization capability (McCoy et al., 2019). Such heuristics include lexical overlap with the training set in Named-Entity Recognition (Taille et al., 2020) and Event or Type heuristics in Relation Extraction (Rosenman et al., 2020). In the more realistic end-to-end RE setting, we can expect yet another heuristic: the mere retention of training relation triples. In this paper we propose two experiments confirming that retention of known facts is a key factor of performance on standard benchmarks. Furthermore, one experiment suggests that a pipeline model able to use intermediate type representations is less prone to over-rely on retention.
The Transfer Matrix Method and the Theory of Finite Periodic Systems. From Heterostructures to Superlattices

Historically, the physics, energy spectrum, and wave functions of superlattices have had different levels of understanding. Unlike the standard approaches, based on theorems for infinite systems, the theory of finite periodic systems (TFPS) constitutes a genuine quantum approach, with closed analytical results. In this theory the finite number of unit‐cells is an essential condition, while the number of propagating modes, as well as, the potential profiles (or refractive indices) are arbitrary. In this work, the transfer matrix definitions, symmetry properties, group representations, and relations with the scattering amplitudes are reviewed. The derivation of multichannel matrix polynomials (which reduce to Chebyshev polynomials in the one‐propagating mode limit), analytical formulas for resonant states, energy eigenvalues, eigenfunctions, parity symmetries, and discrete dispersion relations for superlattices with different confinement characteristics are reviewed. After showing the inconsistencies and limitations of hybrid approaches that combine the transfer‐matrix method with Floquet's theorem, some applications of the TFPS to multichannel negative resistance, ballistic transistors, coupled channels transport, spintronics, superluminal, and optical antimatter effects are revisited. Two high‐resolution superlattice related experiments are reviewed: the tunneling time in photonic band‐gap and the optical response of blue‐emitting diodes. It is shown that the TFPS accurately predicts the experimental results.
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus

The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the ﬁrst experiments on this new resource. We compare in-domain and out-of-domain classiﬁcation, and perform a speciﬁc evaluation of nine linguistic phenomena. We also present the ﬁrst cross-lingual experiments, aimed at assessing whether multilingual transformer-based approaches can beneﬁt from using sentences in two languages during ﬁne-tuning.
A thiocoumarin-based colorimetric and ratiometric fluorescent probe for Hg2+ in aqueous solution and its application in live-cell imaging

A new intramolecular charge transfer (ICT) based colorimetric and ratiometric fluorescent chemodosimeter for the detection of Hg2+ has been rationally designed and developed. The probe operates via the specific mercury-promoted desulfurization reaction of thiocoumarin to coumarin and exhibits high selectivity and sensitivity in almost pure aqueous solution (containing only 1% DMSO) with a low detection limit of 1.85 ppb. Furthermore, the probe was successfully used for the fluorescence imaging of Hg2+ in live cells.
SICEM: A Generation Approach of Band Combination for Hyperspectral Imagery Reconstitution Based on Space and Information Analyses

A band selection algorithm named space and information comprehensive evaluation model (SICEM) is proposed in this paper, which reconstitutes the hyperspectral imagery by building an optimal subset to replace the original spectrum. SICEM reduces the dimensions while keeping the vital information of an image, and these are accomplished through two phases. Specifically, the improved fast density peaks clustering (I-FDPC) algorithm is employed to pick out the scattered bands in geometric space to generate a candidate set Uat first. Then, we conduct pruning in Uthrough iterative information analysis until the target set Ωis built. In this phase, we need to calculate comprehensive information score (CIS) for every member in Uafter assigning weights to the amount of information (AoI) and correlation. In each iteration, the band with highest score is selected into Ω, and the ones highly related to it will be removed out of Uvia a threshold. Compared with the four state-of-the-art unsupervised algorithms on real-world HSI datasets (IndianP and PaviaU), we find that SICEM has strong ability to form an optimal reduced-dimension combination with low correlation and rich information and it performs well in discrete band distribution, accuracy, consistency, and stability.
Níveis Altos de Tiol: Um Novo Marcador de Arritmias Ventriculares na Síndrome Coronária Aguda?

Bruno Ferraz de Oliveira Gomes1,2 Hospital Barra D’Or Rede D’Or São Luiz,1 Rio de Janeiro, RJ Brasil Universidade Federal do Rio de Janeiro Instituto de Cardiologia Edson Saad,2 Rio de Janeiro, RJ Brasil Minieditorial referente ao artigo: Associação entre Parâmetros Plasmático de Tiol e Níveis de Troponina em Pacientes com Síndrome Coronariana Aguda e Predição de Arritmia Ventricular Hospitalar
Novel Insights into the Role of Probiotics in Respiratory Infections, Allergies, Cancer, and Neurological Abnormalities

In recent years, probiotics have attracted public attention and transformed the social perception of microorganisms, convening a beneficial role/state on human health. With aging, the immune system, body physiology, and intestinal microbiota tend to change unfavorably, resulting in many chronic conditions. The immune-mediated disorders can be linked to intestinal dysbiosis, consequently leading to immune dysfunctions and a cluster of conditions such as asthma, autoimmune diseases, eczema, and various allergies. Probiotic bacteria such as Lactobacillus and Bifidobacterium species are considered probiotic species that have a great immunomodulatory and anti-allergic effect. Moreover, recent scientific and clinical data illustrate that probiotics can regulate the immune system, exert anti-viral and anti-tumoral activity, and shields the host against oxidative stress. Additionally, microbiota programming by probiotic bacteria can reduce and prevent the symptoms of respiratory infections and ameliorate the neurological status in humans. This review describes the most recent clinical findings, including safe probiotic therapies aiming to medicate respiratory infections, allergies, cancer, and neurological disorders due to their physiological interconnection. Subsequently, we will describe the major biological mechanism by which probiotic bacteriotherapy expresses its anti-viral, anti-allergic, anticancer, and neuro-stimulatory effects.
Immunosensing Based on Optical Fiber Technology: Recent Advances

The evolution of optical fiber technology has revolutionized a variety of fields, from optical transmission to environmental monitoring and biomedicine, given their unique properties and versatility. For biosensing purposes, the light guided in the fiber core is exposed to the surrounding media where the analytes of interest are detected by different techniques, according to the optical fiber configuration and biofunctionalization strategy employed. These configurations differ in manufacturing complexity, cost and overall performance. The biofunctionalization strategies can be carried out directly on bare fibers or on coated fibers. The former relies on interactions between the evanescent wave (EW) of the fiber and the analyte of interest, whereas the latter can comprise plasmonic methods such as surface plasmon resonance (SPR) and localized SPR (LSPR), both originating from the interaction between light and metal surface electrons. This review presents the basics of optical fiber immunosensors for a broad audience as well as the more recent research trends on the topic. Several optical fiber configurations used for biosensing applications are highlighted, namely uncladded, U-shape, D-shape, tapered, end-face reflected, fiber gratings and special optical fibers, alongside practical application examples. Furthermore, EW, SPR, LSPR and biofunctionalization strategies, as well as the most recent advances and applications of immunosensors, are also covered. Finally, the main challenges and an outlook over the future direction of the field is presented.
Characterization of OmcA Mutants from Shewanella oneidensis MR‐1 to Investigate the Molecular Mechanisms Underpinning Electron Transfer Across the Microbe‐Electrode Interface

Electricity production in microbial fuel cells (MFCs) is an emerging green alternative to the use of fossil fuels. Shewanella oneidensis MR‐1 (SOMR‐1) is a Gram‐negative bacterium, adapted to MFCs due to its ability to link its bioenergetic metabolism through the periplasm to reduce extracellular electron acceptors. OmcA is a highly abundant outer‐membrane cytochrome of SOMR‐1 cells and is involved in the extracellular electron transfer to solid acceptors and electron shuttles. To investigate electron transfer performed by OmcA towards final acceptors, site directed mutagenesis was used to disturb the axial coordination of hemes. Interactions between OmcA and redox partners such as iron and graphene oxides, and electron shuttles were characterized using nuclear magnetic resonance and stopped‐flow experiments. Results showed that solid electron acceptors do not come into close proximity to the hemes, in agreement with experimentally observed slow electron transfer. In contrast, mutation of the distal axial ligand of heme VII changes the driving force of OmcA towards electron shuttles and reduces the affinity of the FMN:OmcA complex. Overall, these results reveal a functional specificity of particular hemes of OmcA and provide guidance for the rational design of mutated SOMR‐1 strains optimized for operating in different microbial electrochemical devices.
The Mini-Cog, Clock Drawing Test, and Three-Item Recall Test: Rapid Cognitive Screening Tools with Comparable Performance in Detecting Mild NCD in Older Patients

Abstracts Background: Early mild neurocognitive disorder (mild NCD) detection can allow for appropriate planning and delay disease progression. There have been few studies examining validated mild NCD detection tools. One such tool that may be of use is the Mini-Cog, which consists of the clock drawing test (CDT) and three-item recall. Methods: This study aimed to compare the diagnostic properties of the Mini-Cog, the CDT alone, and the three-item recall test alone in mild NCD detection according to DSM-5 criteria. The participants were older patients attending the medicine outpatient clinic. Area under receiver operating characteristic (ROC) curve (AUC) analysis was used to compare the tools’ accuracy. Results: A total of 150 patients were enrolled, 42 of whom were diagnosed as having mild NCD. The AUCs of ROC curves of the three-item recall, CDT, Mini-Cog1, and Mini-Cog2 were 0.71, 0.67, 0.73, and 0.71, respectively (p = 0.36). The sensitivity of the tools was 85.7%, 66.7%, 57.4%, and 69% respectively. The tests performed similarly in participants with ≤6 years of education (p = 0.27) and those with >6 years of education (p = 0.49). Conclusions: All tools exhibited similar acceptable performance in detecting mild NCD and were not affected by education. These convenient tools might be suitable for use in clinical practice.
Ovarian Cell Encapsulation in an Enzymatically Crosslinked Silk-Based Hydrogel with Tunable Mechanical Properties

An artificial ovary is a promising approach for preserving fertility in prepubertal girls and women who cannot undergo current cryopreservation strategies. However, this approach is in its infancy, due to the possible challenges of creating a suitable 3D matrix for encapsulating ovarian follicles and stromal cells. To maintain the ovarian stromal cell viability and proliferation, as a first step towards developing an artificial ovary, in this study, a double network hydrogel with a high water swelling capacity (swelling index 15–19) was developed, based on phenol conjugated chitosan (Cs-Ph) and silk fibroin (SF) through an enzymatic crosslinking method using horseradish peroxidase. The addition of SF (1%) to Cs (1%) decreased the storage modulus (G’) from 3500 Pa (Cs1) to 1600 Pa (Cs-SF1), and the hydrogels with a rapid gelation kinetic produced a spatially homogeneous distribution of ovarian cells that demonstrated 167% proliferation after 7 days. This new Cs-SF hydrogel benefits from the toughness and flexibility of SF, and phenolic chemistry could provide the potential microstructure for encapsulating human ovarian stromal cells.
Active Ingredients from Euodia ruticarpa Steam Distilled Essential Oil Inhibit PC-3 Prostate Cancer Cell Growth via Direct Action and Indirect Immune Cells Conditioned Media In Vitro

Active constituents isolated from Euodia ruticarpa (ER) steam distilled essential oil (SDEO) against PC-3 prostate cancer cell growth remain unclear. To clarify the puzzle, ER SDEO was extracted and further resolved into six isolated fractions ERF1–F6 with Sephadex LH-20 gel filtration chromatography to analyze their biological activities. Active ingredients in the isolated fractions were analyzed with GC-MS. Potential isolated fractions were selected to treat PC-3 cells with direct action and indirect treatment by mouse splenocyte- (SCM) and macrophage-conditioned media (MCM). The relationship between PC-3 cell viabilities and corresponding total polyphenols, flavonoid contents as well as Th1/Th2 cytokine profiles in SCM was analyzed using the Pearson product–moment correlation coefficient (r). As a result, ERF1–F3 was abundant in total polyphenols and flavonoids contents with diverse active ingredients. Treatments with ERF1–F3 at appropriate concentrations more or less inhibit PC-3 cell growth in a direct action manner. Only SCM, respectively, cultured with ER SDEO and ERF1–F3 markedly enhanced the effects to inhibit PC-3 cell growth, suggesting that secretions by splenocytes might involve anti-PC-3 effects. There are significantly negative correlations between PC-3 cell viabilities and IL-2, IL-10 as well as IL-10/IL-2 ratios in the corresponding SCM. Total polyphenol and flavonoid contents in the media cultured with ER SDEO isolated fractions positively correlated with IL-10 (Th2) and IL-10/IL-2 (Th2/Th1) cytokine secretion ratios by splenocytes, indicating that polyphenol and flavonoid components in ER SDEO isolated fractions promote Th2-polarized and anti-inflammatory characteristics. These new findings concluded that the inhibitory effects against PC-3 prostate cancer cell growth are attributed to active anti-inflammatory ingredients in ER SDEO and its active ERF1–F3 fractions through direct action and indirect treatment by modulating splenocytes’ cytokine secretion profiles.