Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely assessed in pre-clinical drug development or during clinical trials, so this debilitating side-effect is often discovered as patients begin to report hearing loss. Furthermore, drug-induced ototoxicity in adults, and particularly in elderly patients, may go unrecognized due to hearing loss from a variety of etiologies because of a lack of baseline assessments immediately prior to novel therapeutic treatment. During the current pandemic, there is an intense effort to identify new drugs or repurpose FDA-approved drugs to treat COVID-19. Several potential COVID-19 therapeutics are known ototoxins, including chloroquine (CQ) and lopinavir-ritonavir, demonstrating the necessity to identify ototoxic potential in existing and novel medicines. Furthermore, several factors are emerging as potentiators of ototoxicity, such as inflammation (a hallmark of COVID-19), genetic polymorphisms, and ototoxic synergy with co-therapeutics, increasing the necessity to evaluate a drug's potential to induce ototoxicity under varying conditions. Here, we review the potential of COVID-19 therapies to induce ototoxicity and factors that may compound their ototoxic effects. We then discuss two models for rapidly detecting the potential for ototoxicity: mammalian auditory cell lines and the larval zebrafish lateral line. These models offer considerable value for pre-clinical drug development, including development of COVID-19 therapies. Finally, we show the validity of in silico screening for ototoxic potential using a computational model that compares structural similarity of compounds of interest with a database of known ototoxins and non-ototoxins. Preclinical screening at in silico, in vitro, and in vivo levels can provide an earlier indication of the potential for ototoxicity and identify the subset of candidate therapeutics for treating COVID-19 that need to be monitored for ototoxicity as for other widely-used clinical therapeutics, like aminoglycosides and cisplatin.
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models

We explore the black-box adversarial attack on video recognition models. Attacks are only performed on selected key regions and key frames to reduce the high computation cost of searching adversarial perturbations on a video due to its high dimensionality. To select key frames, one way is to use heuristic algorithms to evaluate the importance of each frame and choose the essential ones. However, it is time inefficient on sorting and searching. In order to speed up the attack process, we propose a reinforcement learning based frame selection strategy. Specifically, the agent explores the difference between the original class and the target class of videos to make selection decisions. It receives rewards from threat models which indicate the quality of the decisions. Besides, we also use saliency detection to select key regions and only estimate the sign of gradient instead of the gradient itself in zeroth order optimization to further boost the attack process. We can use the trained model directly in the untargeted attack or with little fine-tune in the targeted attack, which saves computation time. A range of empirical results on real datasets demonstrate the effectiveness and efficiency of the proposed method.
Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery

Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.
Winner Robustness via Swap- and Shift-Bribery: Parameterized Counting Complexity and Experiments

We study the parameterized complexity of counting variants of Swap- and Shift-Bribery, focusing on the parameterizations by the number of swaps and the number of voters. Facing several computational hardness results, using sampling we show experimentally that Swap-Bribery offers a new approach to the robustness analysis of elections.
Inconsistency Measurement for Paraconsistent Inference

One of the main aims of the methods developed for reasoning under inconsistency, in particular paraconsistent inference, is to derive informative conclusions from inconsistent bases. In this paper, we introduce an approach based on inconsistency measurement for defining non-monotonic paraconsistent consequence relations. The main idea consists in adapting properties of classical reasoning under consistency to inconsistent propositional bases by involving inconsistency measures (IM). We first exhibit interesting properties of our consequence relations. We then study situations where they bring about consequences that are always jointly consistent. In particular, we introduce a property of inconsistency measures that guarantees the consistency of the set of all entailed formulas. We also show that this property leads to several interesting properties of our IM-based consequence relations. Finally, we discuss relationships between our framework and well-known consequence relations that are based on maximal consistent subsets. In this setting, we establish direct connections between the latter and properties of inconsistency measures.
BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling

Different to traditional on-premise VDI , the virtual desktops in DaaS (Desktop as a Service) are hosted in public cloud where virtual machines are charged based on usage. Accordingly, an adaptive power management system which can turn off spare virtual machines without sacrificing end user experience is of significant customer value as it can greatly help reduce the running cost. Generally, logon behavior modeling for VDI users serves as the key enabling-technique to fulfill intelligent power management. Prior attempts work by modeling logon behavior in a user-dependent manner with tailored single-instance feature representation, where the strong relationships among pool-sharing VDI users are ignored in the modeling framework. In this paper, a novel formulation towards VDI user logon behavior modeling is proposed by employing the multi-instance multi-label (MIML) techniques. Specifically, each user is grouped with supporting users whose behaviors are jointly modeled in the feature space with multi-instance representation as well as in the output space with multi-label prediction. The resulting MIML formulation is optimized by adapting the popular MIML boosting procedure via balanced error-rate minimization. Experimental studies on real VDI customers' data clearly validate the effectiveness of the proposed MIML-based approach against state-of-the-art VDI user logon behavior modeling techniques.
An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.
Room-temperature ferromagnetism in Sr_(1-x)Y_xCoO_(3-delta) (0.2 < x < 0.25)

We have measured magnetic susceptibility and resistivity of Sr$_{1-x}$Y$_x$CoO$_{3-\delta}$ ($x=$ 0.1, 0.15, 0.2, 0.215, 0.225, 0.25, 0.3, and 0.4), and have found that Sr$_{1-x}$Y$_x$CoO$_{3-\delta}$ is a room temperature ferromagnet with a Curie temperature of 335 K in a narrow compositional range of 0.2 $\leq x\leq$ 0.25. This is the highest transition temperature among perovskite Co oxides. The saturation magnetization for $x=$ 0.225 is 0.25 $\mu_B$/Co at 10 K, which implies that the observed ferromagnetism is a bulk effect. We attribute this ferromagnetism to a peculiar Sr/Y ordering.
About one discrete model of splitting by the physical processes of a piezoconductive medium with gas hydrate inclusions

The thermodynamically equilibrium model for splitting by the physical processes of a two-component three-phase filtration fluid dynamics with gas hydrate inclusions is considered in the paper, for which a family of two-layer completely conservative difference schemes of the support operators method with time weights profiled in space is constructed. On the irregular grids of the theory of the support-operators method applied to the specifics of the processes of transfer of saturations and internal energies of water and gas in a medium with gas hydrate inclusions, methods of directwind approximation of these processes are considered. These approximations preserve the continual properties of divergence–gradient operations in their difference form and are related to the velocity field providing saturations transfer and internal energies of fluids. Fluid dynamics with gas hydrate inclusions are also calculated on the basis of the proposed approach, in particular, in areas of severe pressure depression in the collector space.
TEC: A Time Evolving Contextual Graph Model for Speaker State Analysis in Political Debates

Political discourses provide a forum for representatives to express their opinions and contribute towards policy making. Analyzing these discussions is crucial for recognizing possible delegates and making better voting choices in an independent nation. A politician's vote on a proposition is usually associated with their past discourses and impacted by cohesion forces in political parties. We focus on predicting a speaker's vote on a bill by augmenting linguistic models with temporal and cohesion contexts. We propose TEC, a time evolving graph based model that jointly employs links between motions, speakers, and temporal politician states. TEC outperforms competitive models, illustrating the benefit of temporal and contextual signals for predicting a politician's stance.
Multi-Agent Belief Base Revision

We present a generalization of belief base revision to the multi-agent case. In our approach agents have belief bases containing both propositional beliefs and higher-order beliefs about their own beliefs and other agents’ beliefs. Moreover, their belief bases are split in two parts: the mutable part, whose elements may change under belief revision, and the core part, whose elements do not change. We study a belief revision operator inspired by the notion of screened revision. We provide complexity results of model checking for our approach as well as an optimal model checking algorithm. Moreover, we study complexity of epistemic planning formulated in the context of our framework.
PSO-Based UAV Deployment and Dynamic Power Allocation for UAV-Enabled Uplink NOMA Network

Recently, unmanned aerial vehicles (UAVs) have been used as flying base stations (BSs) to take advantage of line-of-sight (LOS) connectivity and efficiently enable fifth-generation (5G) and cellular network coverage and data rates. On the other hand, nonorthogonal multiple access (NOMA) is a promising technique to help achieve unprecedented requirements by simultaneously allowing multiple users to send data over the same resource block. In this paper, we study a UAV-enabled uplink NOMA network, where the UAV collects data from ground users while flying at a certain altitude. Unlike all existing work on this topic, this study consists of two stages. In the first stage, we use the well-known Particle Swarm Optimization (PSO) algorithm, which is a metaheuristic algorithm, to deploy the UAV in 3D space, so that the users’ sum pathlosses are minimized. In the second stage, we investigate the user pairing problem and propose a dynamic power allocation technique for determining the user’s power allocation coefficients, as well as a closed-form equation for the ergodic sum-rate. Results show our PSO-based algorithm prevailing over the Genetic Algorithm (GA) and random deployment methods. The proposed dynamic power allocation strategy maximizes the network’s ergodic sum-rate and outperforms the fixed power allocation strategy. Additionally, the results reveal that the best pairing scheme is the one that keeps uniform channel gain difference in the same pair.
SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables

Error detection is one of the most important steps in data cleaning and usually requires extensive human interaction to ensure quality. Existing supervised methods in error detection require a significant amount of training data while unsupervised methods rely on fixed inductive biases, which are usually hard to generalize, to solve the problem. In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. SPADE introduces a novel probabilistic active learning model, where the system suggests examples to be labeled based on the agreements between user labels and indicative signals, which are designed to capture potential errors. SPADE uses a two-phase data augmentation process to enrich a dataset before training a deep learning classifier to detect unlabeled errors. In our evaluation, SPADE achieves an average F1-score of 0.91 over five datasets and yields a 10% improvement compared with the state-of-the-art systems.
An insight of spondias mangifera willd: an underutilized medicinal plant with immense nutraceutical and therapeutic potentials

Spondias mangifera, is best known for its fruits in Indo-Malaysia region. The fruit contains about 25 percent juicy and edible pulp. The pulp is used as a common ingredient in culinary preparations like condiments, curries, jams, sherbet, beverages in the countries where the plant grows naturally. Green fruits from the plant are pickled in brine, which is the rich source of phenolic compounds, acids, minerals, flavonoids, sugars, vitamins and peptides. The ripe fruit is full of acids and micro- and macro- nutrients, which combine well with sugars, and used for the preparation of a variety of traditional highly energetic and refreshing drinks. It is considered nowadays as a valuable source for the development of several industrial products and of several unique products of the medicines against several diseases. Amara is relatively a new item to be explored for its full potential and it needs publicity and promotion in the international market. This review includes comprehensive information on traditional uses, chemical constituents, pharmacological actions and nutraceutical values of raw materials and processed products.
Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.
The Parameterized Complexity of Connected Fair Division

We study the Connected Fair Division problem (CFD), which generalizes the fundamental problem of fairly allocating resources to agents by requiring that the items allocated to each agent form a connected subgraph in a provided item graph G. We expand on previous results by providing a comprehensive complexity-theoretic understanding of CFD based on several new algorithms and lower bounds while taking into account several well-established notions of fairness: proportionality, envy-freeness, EF1 and EFX. In particular, we show that to achieve tractability, one needs to restrict both the agents and the item graph in a meaningful way. We design (XP)-algorithms for the problem parameterized by (1) clique-width of G plus the number of agents and (2) treewidth of G plus the number of agent types, along with corresponding lower bounds. Finally, we show that to achieve fixed-parameter tractability, one needs to not only use a more restrictive parameterization of G, but also include the maximum item valuation as an additional parameter.
Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.
Transport properties of Co in Cu(100) from density functional theory calculations

The electronic transport properties of a point-contact system formed by a single Co atom adsorbed on Cu (100) and contacted by a copper tip is evaluated in the presence of intra-atomic Coulomb interactions and spin–orbit coupling. The calculations are performed using equilibrium Green’s functions evaluated within density functional theory completed with a Hubbard U term and spin–orbit interaction, as implemented in the Gollum package. We show that the contribution to the transmission between electrodes of spin-flip components is negative and scaling as λ2/Γ2 where λ is the SOC and Γ the Co atom-electrode coupling. Hence, due to this unfavorable ratio, SOC effects in transport in this system are small. However, we show that the spin-flip transmission component can increase by 2 orders of magnitude depending on the value of the Hubbard U term. These effects are particularly important in the contact regime because of the prevalence of d-electron transport, while in the tunneling regime, transport is controll.
Infiltrated mesoporous oxygen electrodes for high temperature co-electrolysis of H2O and CO2 in solid oxide electrolysis cells

In the last few years, high temperature solid oxide electrolysis cells (SOECs) have emerged as a promising solution for energy conversion and storage. However, state-of-the-art systems suffer from technological limitations, which prevent their widespread use and market penetration. Particularly, the electrode–electrolyte interface represents a critical element due to the high oxygen potential located in this area, which in turn determines interface delamination. In this context, the use of mesoporous materials, whose architecture is characterized by concatenated nanometric-size pores and high specific surface area, represents a powerful strategy toward the achievement of long-term stability of the electrode. Such structures have been recently proposed as ionically conducting electrode scaffolds for solid oxide fuel cells (SOFCs), exhibiting good performances and low degradation rates. In particular, it has been shown that a highly increased triple phase boundary (TPB)-active points distribution along the electrode may be achieved upon infiltration with a catalytically active material. In this study, infiltrated mesoporous cerium was used as a functional oxygen electrode layer in a fuel electrode supported SOEC system. The results indicate that an enhancement in both long-term stability and electrolysis cell performance are achieved. This is attributed to the decrease in high current density paths and areas of high oxygen potential and to the superior thermal stability of such a nanostructured composite, which allows better current distribution. First, the morphological characterization of the as-synthesized mesoporous Ce0.8Gd0. 2O1.9 (CGO), which was carried out by TEM microscopy and low-angle X-ray diffraction (LA-XRD), is presented. A structural and functional investigation of the fuel electrode supported cells, in which the oxygen electrode is composed of catalytically active La0.6Sr0. 4Co0.2Fe0. 8O3 (LSCF) infiltrating a CGO scaffold, was studied by means of XRD, scanning electron microscopy and spectroscopy, and electrochemical measurements. Characterization under co-electrolysis mode (45% H2O, 45% CO2 and 10% H2) shows an injected current density higher than 1.2 A cm−2 at 1.4 V at 750 °C. Electrochemical impedance spectroscopy was carried out at regular time intervals during galvanostatic (0.5 and 0.75 A cm−2) long-term operation for 1400 h, exhibiting a decrease in polarization resistance and only slight increase in serial resistance during operation. Total degradation rates lower than 2% kh−1 at 0.5 A cm−2 and 1% kh−1 at 0.75 A cm−2 were obtained. Finally, a mechanism for cell degradation based on evolution of the fuel electrode is proposed.