Miracle-dz/UniFAR-FTUs huggingface dataset:
Gain Without Pain: Recycling Reflected Energy From Wireless-Powered RIS-Aided Communications

In this article, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of Things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching- and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are, respectively, formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we, respectively, develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding suboptimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.
Intereaction between steady non-uniform two-dimensional currents and directional wind-generated gravity waves with applications for current measurements

Abstract : Interactions between steady non-uniform two-dimensional currents and directional gravity waves are studied for the case of a random wave field which is actively generated by the wind. Directional wave number spectrum in the form suggested by Phillips, Kitaigorodskii, Pierson-Moskowitz, and Longuet-Higgins-Cartwright-Smith is used as the basic spectral form for zero current conditions. Modified spectral functions under the influence of current are found by solving the kinematic wave conservation equation and the energy balance equation with the method of characteristics. By using this particular method, one can solve the problems of two-dimensional wave-current interactions by either specifying U and wave energy, E, or specifying wave number vector, k, and E, for every point space, x. The remote sensing techniques are suggested to be the best and the most convenient feasible way of measuring these parameters for inferring the current field. Thus the changes in wave characteristics, in the form of directional wave spectra, can readily be numerically calculated as the directional wind-generated gravity waves encounter the steady non-uniform two-dimensional currents. Using the remote sensing techniques which are capable of observing these changes in waves over a large area, one will be able to infer the magnitude and the direction of the current encountered. This particular method of inferring current field has great potential benefits in global applications.
Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic Study

Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, key elements underlying molecular property prediction remain largely unexplored, which impedes further advancements in this field. Herein, we conduct an extensive evaluation of representative models using various representations on the MoleculeNet datasets, a suite of opioids-related datasets and two additional activity datasets from the literature. To investigate the predictive power in low-data and high-data space, a series of descriptors datasets of varying sizes are also assembled to evaluate the models. In total, we have trained 62,820 models, including 50,220 models on fixed representations, 4,200 models on SMILES sequences and 8,400 models on molecular graphs. Based on extensive experimentation and rigorous comparison, we show that representation learning models exhibit limited performance in molecular property prediction in most datasets. Besides, multiple key elements underlying molecular property prediction can affect the evaluation results. Furthermore, we show that activity cliffs can significantly impact model prediction. Finally, we explore into potential causes why representation learning models can fail and show that dataset size is essential for representation learning models to excel.
Predicting Affective Vocal Bursts with Finetuned wav2vec 2.0

The studies of predicting affective states from human voices have relied heavily on speech. This study, indeed, explores the recognition of humans' affective state from their vocal burst, a short non-verbal vocalization. Borrowing the idea from the recent success of wav2vec 2.0, we evaluated finetuned wav2vec 2.0 models from different datasets to predict the affective state of the speaker from their vocal burst. The finetuned wav2vec 2.0 models are then trained on the vocal burst data. The results show that the finetuned wav2vec 2.0 models, particularly on an affective speech dataset, outperform the baseline model, which is handcrafted acoustic features. However, there is no large gap between the model finetuned on non-affective speech dataset and affective speech dataset.
The delay-dependent control problem for ﬁnite-horizon continuous-time decision process 1

This paper investigates the optimal control problem for the continuous-time decision processes with an enlarged class of control policies, i.e. delay-dependent control policies. We show that, under certain conditions, among the class of delay-dependent control policies, there is an optimal one which is Markovian. Such result is obtained by characterizing the value function to be a unique viscosity solution to certain di ﬀ erential-di ﬀ erence Hamilton-Jacobi-Bellman (HJB) equation in the setting of discrete space. In particular, the comparison principle is established for such HJB equations.
Transition from granular to Brownian suspension: An inclined plane experiment

We experimentally revisite the flow down an inclined plane of dense granular suspensions, with particles of sizes in the micron range, for which thermal fluctuations cannot be ignored. Using confocal microscopy on a miniaturized set-up, we observe that, in contrast with standard granular rheology, the flow profiles strongly depend on the particles size. Also, suspensions composed of small enough particles flow at infinitesimal inclinations. From the velocity measurements, an effective rheology is extracted in terms of a friction coefficient as a fonction of the dimensionless shear rate (the viscous number), and of the particle pressure normalized by the thermal pressure. Inspired by a previous work [1], a phenomenological model based on the sum of a thermal contribution describing the glass transition and an athermal contribution capturing the jamming transition is developed, which reproduces well the experimental observations. The model predicts the existence of a glassy friction angle lower than the granular athermal friction angle, a signature of the glass transition in the framework of a pressure imposed rheology.
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training

Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream applications. Some following works have targeted to improve data efficiency by adding self-supervision terms, but inter-domain (image-text) contrastive loss and intra-domain (image-image) contrastive loss are defined on individual spaces in those works, so many feasible combinations of supervision are overlooked. To overcome this issue, we propose UniCLIP, a Unified framework for Contrastive Language-Image Pre-training. UniCLIP integrates the contrastive loss of both inter-domain pairs and intra-domain pairs into a single universal space. The discrepancies that occur when integrating contrastive loss between different domains are resolved by the three key components of UniCLIP: (1) augmentation-aware feature embedding, (2) MP-NCE loss, and (3) domain dependent similarity measure. UniCLIP outperforms previous vision-language pre-training methods on various single- and multi-modality downstream tasks. In our experiments, we show that each component that comprises UniCLIP contributes well to the final performance.
MARS: a motif-based autoregressive model for retrosynthesis prediction

Abstract Motivation Retrosynthesis is a critical task in drug discovery, aimed at finding a viable pathway for synthesizing a given target molecule. Many existing approaches frame this task as a graph-generating problem. Specifically, these methods first identify the reaction center, and break a targeted molecule accordingly to generate the synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or by directly adding appropriate leaving groups. However, both of these strategies have limitations. Adding atoms results in a long prediction sequence that increases the complexity of generation, while adding leaving groups only considers those in the training set, which leads to poor generalization. Results In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Given that chemically meaningful motifs fall between the size of atoms and leaving groups, our model achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups. We evaluate our proposed model on a benchmark dataset and show that it significantly outperforms previous state-of-the-art models. Furthermore, we conduct ablation studies to investigate the contribution of each component of our proposed model to the overall performance on benchmark datasets. Experiment results demonstrate the effectiveness of our model in predicting retrosynthesis pathways and suggest its potential as a valuable tool in drug discovery. Availability and implementation All code and data are available at https://github.com/szu-ljh2020/MARS.
Fixed point results of enriched interpolative Kannan type operators with applications

The purpose of this paper is to introduce the class of enriched interpolative Kannan type operators on Banach space that contains theclasses of enriched Kannan operators, interpolative Kannan type contraction operators and some other classes of nonlinear operators. Some examples are presented to support the concepts introduced herein. A convergence theorem for the Krasnoselskij iteration method to approximate fixed point of the enriched interpolative Kannan type operators is proved. We study well-posedness, Ulam-Hyers stability and periodic point property of operators introduced herein. As an application of the main result, variational inequality problems is solved.
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e.g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, for scalability and quality. In total, we publicly release 272K manual semantic masks of 257 object classes, 9.9M interpolated dense masks, 67K hand-object relations, covering 36 hours of 179 untrimmed videos. Along with the annotations, we introduce three challenges in video object segmentation, interaction understanding and long-term reasoning. For data, code and leaderboards: http://epic-kitchens.github. io/VISOR
Solving the thoracic inverse problem in the fruit fly

In many insect species, the thoracic exoskeletal structure plays a crucial role in enabling flight. In the dipteran indirect flight mechanism, thoracic cuticle acts as a transmission link between the flight muscles and the wings, and is thought to act as an elastic modulator: improving flight motor efficiency thorough linear or nonlinear resonance. But peering closely into the drivetrain of tiny insects is experimentally difficult, and the nature of this elastic modulation is unclear. Here, we present a new inverse-problem methodology to surmount this difficulty. In a data synthesis process, we integrate literature-reported rigid-wing aerodynamic and musculoskeletal data into a planar oscillator model for the fruit fly Drosophila melanogaster, and use this integrated data to identify several surprising properties of the fly’s thorax. We find that fruit flies likely have an energetic need for motor resonance: absolute power savings due to motor elasticity range from 0%–30% across literature-reported datasets, averaging 16%. However, in all cases, the intrinsic high effective stiffness of the active asynchronous flight muscles accounts for all elastic energy storage required by the wingbeat. The D. melanogaster flight motor should be considered as a system in which the wings are resonant with the elastic effects of the motor’s asynchronous musculature, and not with the elastic effects of the thoracic exoskeleton. We discover also that D. melanogaster wingbeat kinematics show subtle adaptions that ensure that wingbeat load requirements match muscular forcing. Together, these newly-identified properties suggest a novel conceptual model of the fruit fly’s flight motor: a structure that is resonant due to muscular elasticity, and is thereby intensely concerned with ensuring that the primary flight muscles are operating efficiently. Our inverse-problem methodology sheds new light on the complex behaviour of these tiny flight motors, and provides avenues for further studies in a range of other insect species.
Sparse Bayesian Inference on Gamma-Distributed Observations Using Shape-Scale Inverse-Gamma Mixtures

In various applications, we deal with high-dimensional positive-valued data that often exhibits sparsity. This paper develops a new class of continuous global-local shrinkage priors tailored to analyzing gamma-distributed observations where most of the underlying means are concentrated around a certain value. Unlike existing shrinkage priors, our new prior is a shape-scale mixture of inverse-gamma distributions, which has a desirable interpretation of the form of posterior mean and admits flexible shrinkage. We show that the proposed prior has two desirable theoretical properties; Kullback-Leibler super-efficiency under sparsity and robust shrinkage rules for large observations. We propose an efficient sampling algorithm for posterior inference. The performance of the proposed method is illustrated through simulation and two real data examples, the average length of hospital stay for COVID-19 in South Korea and adaptive variance estimation of gene expression data.
Dissipation in relativistic superfluid neutron stars

We analyze damping of oscillations of general relativistic superfluid neutron stars. To this aim we extend the method of decoupling of superfluid and normal oscillation modes first suggested in [Gusakov & Kantor PRD 83, 081304(R) (2011)]. All calculations are made self-consistently within the finite temperature superfluid hydrodynamics. The general analytic formulas are derived for damping times due to the shear and bulk viscosities. These formulas describe both normal and superfluid neutron stars and are valid for oscillation modes of arbitrary multipolarity. We show that: (i) use of the ordinary one-fluid hydrodynamics is a good approximation, for most of the stellar temperatures, if one is interested in calculation of the damping times of normal f-modes; (ii) for radial and p-modes such an approximation is poor; (iii) the temperature dependence of damping times undergoes a set of rapid changes associated with resonance coupling of neighboring oscillation modes. The latter effect can substantially accelerate viscous damping of normal modes in certain stages of neutron-star thermal evolution.
Evidence for High-Temperature Superconductivity in Doped Laser-Processed Sr-Ru-O

We have discovered that samples of a new material produced by special processing of crystals of Sr2RuO4 (which is known to be a triplet superconductor with Tc values ~1.0-1.5K) exhibit signatures of superconductivity (zero DC resistance and expulsion of magnetic flux) at temperatures exceeding 200K. The special processing includes deposition of a silver coating and laser micromachining; Ag doping and enhanced oxygen are observed in the resultant surface layer. The transition, whether measured resistively or by magnetic field expulsion, is broad. When the transition is registered by resistive methods, the critical temperature is markedly reduced when the measuring current is increased. The resistance disappears by about 190K. The highest value of Tc registered by magneto-optical visualization is about 220K and even higher values (up to 250K) are indicated from the SQUID-magnetometer measurements.
Temperature-dependent interfacial chemical bonding states and band alignment of HfOxNy∕SiO2∕Si gate stacks

Temperature-dependent interfacial chemical bonding states and band alignment of HfOxNy∕SiO2∕Si gate stacks have been investigated by X-ray photoemission spectroscopy (XPS) and spectroscopic ellipsometry (SE). By means of the chemical shifts of Hf 4f, Si 2p, O 1s, and N 1s core-level spectra, it has been found that the chemical stability of the HfOxNy∕SiO2∕Si stacks strongly depends on the annealing temperature. Analysis of temperature-dependent band alignment of HfOxNy∕SiO2∕Si stacks suggests that the valence band offset ΔEv increases slowly from 1.82eV for as-grown film to 2.55eV for annealed film at 700°C; however, the values of conduction band offset ΔEc only demonstrates a slight change in the vicinity of 1.50eV. From the band offset viewpoint, HfOxNy∕SiO2∕Si gate stack could be a promising candidate for high-k gate dielectrics.
Regional innovation and the retention of foreign direct investment: a place-based approach

ABSTRACT The relationship between regional innovation and foreign direct investment (FDI) has been extensively examined, but the impact of regional innovation on FDI retention has received scant attention. Drawing on the logic of place-based approaches, the paper investigates this impact by conducting survival analyses on data collected across 31 provinces of China. We find that regional technician supply (RTS) and regional intellectual property flexibility (RIPF) help regions retain foreign ventures. However, RTS is less likely to retain the foreign investments of multinational enterprises with large research and development expenditure, while RIPF is more likely to retain them. Theoretical and policy implications are discussed.
Prefrontal-Parietal-cTBS Effects on Metacognitive Awareness: Real or Illusory?

Background: Neuroimaging and lesion studies suggest that the dorsolateral prefrontal and posterior parietal cortices mediate visual metacognitive awareness. The causal evidence provided by non-invasive brain stimulation, however, is inconsistent. Objective/Hypothesis: Here we revisit major experiments with an improved methodology and a new Kanizsa figure task trying to determine whether bilateral continuous theta-burst transcranial magnetic stimulation (cTBS) over these regions affects perceptual metacognition. Specifically, we tested whether subjective visibility ratings and/or metacognitive efficiency are lower when cTBS is applied to these two regions in comparison to an active control region. Methods: A within-subjects design including three sessions, at one-week intervals, was implemented. In each session, every participant was administered bilateral cTBS to either prefrontal, control or parietal cortices. Two concurrent tasks were performed: real and illusory figure, using a staircase procedure to stabilize objective performance. Results: We found strong null findings for the replicated task, suggesting that cTBS affected neither visibility ratings nor visual metacognition. Nevertheless, we also observed that the implementation of illusory Kanizsa figures disrupted metacognitive ability, specifically when cTBS was applied to prefrontal but not parietal areas. Conclusion(s): This study adds new evidence to the debate regarding the role of the PPN in metacognition: specifically, using rigorous criteria, evidence is adduced to show that the DLPFC cTBS selectively interferes with metacognitive awareness. What is more, a novel method for investigating the role of the PPN in metacognition is presented.
High Latitude Coral Larval Recruitment: Spatiotemporal Process Variability in the Gulf of California1

Abstract: The aim of this study is to explore if sexual reproduction is present in scleractinian corals at the northern limit of their distribution (28.980° N, 113.470° W) in a zone subject to upwelling and seasonal variations in sea surface temperature, and sampling was performed from August 2018 to October 2019. We placed 42 terracotta recruitment tiles in Bahía de los Ángeles, Baja California. Coral cover was estimated, and the height, major diameter, and minor diameter of coral colonies were measured. Astrangia haimei and Porites panamensis recruits were identified on the recruitment tiles, constituting the first quantitative record for the northeastern tropical Pacific. Recruitment of P. panamensis was higher (Llave: 63.09 ± 114 ind m–2 y–1, Rasito: 3.21 ± 7 ind m–2 y–1), while A. haimei recruitment at the same sites was 3.85 ± 8 and 1.93 ± 6 ind m–2 y–1, respectively. Recruitment differences between locations were attributed to coral cover (the abundance of Porites panamensis is 15-fold greater in Llave than in Rasito) and colony size (P. panamensis Llave: 10.53 ± 5.93 cm, Rasito: 4 ± 0.63 cm). Both SST and Chl-a concentrations were also highly correlated with coral recruitment, with higher recruitment rates observed in the warmer seasons when high nutrient concentrations were also present. In contrast to other sites in the eastern tropical Pacific, the highest rate of recruitment was reported at this study (high-latitude coral community), which, according with the positive high latitude trend reported by other studies, is likely due to climate change.