Miracle-dz/UniFAR-FTUs huggingface dataset:
Physico-Chemical Indices, Iso-alpha-Acid, Phenolic Contents and Antioxidant Activity of Commercial Beers

The effects of brewing style on physicochemical indices, iso-a-acids and antioxidant contents of commercial beers were investigated. A great variability was highlighted for all the considered parameters (sugar and alcohol contents, density, pH, titratable acidity, iso-a-acids, phenolic content, antioxidant activity). The beers having the highest iso-a-acids contents were Guinness Special Export Stout (which also showed the highest phenolic content), Chimay Peres Trappistes Triple, and Greene King IPA whereas the lowest values were detected in Cuvee De Ranke, Heineken Premium Quality Lager, and Riedenburger Brauhaus gluten free. The latter also had the lowest phenolic concentration. The antioxidant activity values strictly correlated with the total phenolic content but not with the iso-? -acid amount. The Principal Component Analysis failed in distinguishing beers based on their declared type/styles. This was a predictable result since the beer styles don’t represent a compositional classification but they are rather used to indicate commercial types.
Do facial expressions signal specific emotions? Judging emotion from the face in context

Certain facial expressions have been theorized to be easily recognizable signals of specific emotions. If so, these expressions should override situationally based expectations used by a person in attributing an emotion to another. An alternative account is offered in which the face provides information relevant to emotion but does not signal a specific emotion. Therefore, in specified circumstances, situational rather than facial information was predicted to determine the judged emotion. This prediction was supported in 3 studies--indeed, in each of the 22 cases examined (e.g., a person in a frightening situation but displaying a reported "facial expression of anger" was judged as afraid). Situational information was especially influential when it suggested a nonbasic emotion (e.g., a person in a painful situation but displaying a "facial expression of fear" was judged as in pain).
Well-defined carbon polyhedrons prepared from nano metal–organic frameworks for oxygen reduction

We report a nanostructured electrocatalyst engineered from molecular design and morphology evolution. Using a typical Co-based metal–organic framework, ZIF-67, nanocrystals were precisely synthesized with tunable size and morphology. Nitrogen-doped carbon nano polyhedrons decorated with cobalt nanoparticles were fabricated via pyrolysis of ZIF-67 and the carbonized products inherited the nano-size and shape of the MOF precursor. The intense effect of size on the electrocatalytic activity and transport properties was systematically investigated. The catalyst derived from the smallest MOF (300 nm), exhibited superior performance towards oxygen reduction with an onset potential of 0.86 V and a half-wave potential of 0.71 V in acidic solution, which are comparable to the best carbon-based oxygen reduction reaction (ORR) catalysts. This work will pave the way for the development of MOF-derived energy materials in various fields such as fuel cells and Li-air batteries, and opens new avenues for the design of MOFs in electrochemical applications.
Antiviral therapy for human immunodeficiency virus infections.

Depending on the stage of their intervention with the viral replicative cycle, human immunodeficiency virus inhibitors could be divided into the following groups: (i) adsorption inhibitors (i.e., CD4 constructs, polysulfates, polysulfonates, polycarboxylates, and polyoxometalates), (ii) fusion inhibitors (i.e., plant lectins, succinylated or aconitylated albumins, and betulinic acid derivatives), (iii) uncoating inhibitors (i.e., bicyclams), (iv) reverse transcription inhibitors acting either competitively with the substrate binding site (i.e., dideoxynucleoside analogs and acyclic nucleoside phosphonates) or allosterically with a nonsubstrate binding site (i.e., non-nucleoside reverse transcriptase inhibitors), (v) integration inhibitors, (vi) DNA replication inhibitors, (vii) transcription inhibitors (i.e., antisense oligodeoxynucleotides and Tat antagonists), (viii) translation inhibitors (i.e., antisense oligodeoxynucleotides and ribozymes), (ix) maturation inhibitors (i.e., protease inhibitors, myristoylation inhibitors, and glycosylation inhibitors), and finally, (x) budding (assembly/release) inhibitors. Current knowledge, including the therapeutic potential, of these various inhibitors is discussed. In view of their potential clinical the utility, the problem of virus-drug resistance and possible strategies to circumvent this problem are also addressed.
Consistent Plug-in Classifiers for Complex Objectives and Constraints

We present a consistent algorithm for constrained classiﬁcation problems where the objective (e.g. F-measure, G-mean) and the constraints (e.g. demographic parity fairness, coverage) are deﬁned by general functions of the confusion matrix. Our approach reduces the problem into a sequence of plug-in classiﬁer learning tasks. The reduction is achieved by posing the learning problem as an optimization over the intersection of two sets: the set of confusion matrices that are achievable and those that are feasible. This decoupling of the constraint space then allows us to solve the problem by applying Frank-Wolfe style optimization over the individual sets. For objective and constraints that are convex functions of the confusion matrix, our algorithm requires O (1 / ✏ 2 ) calls to the plug-in subroutine, which improves on the O (1 / ✏ 3 ) calls needed by the reduction-based algorithm of Narasimhan (2018) [29]. We show empirically that our algorithm is competitive with prior methods, while being more robust to choices of hyper-parameters.
Efficiently Computing All Delaunay Triangles Occurring over All Contiguous Subsequences

Given an ordered sequence of points P = {p1, p2, . , pn}, we are interested in computing T , the set of distinct triangles occurring over all Delaunay triangulations of contiguous subsequences within P . We present a deterministic algorithm for this purpose with near-optimal time complexity O(|T | logn). Additionally, we prove that for an arbitrary point set in random order, the expected number of Delaunay triangles occurring over all contiguous subsequences is Θ(n logn). 2012 ACM Subject Classification Theory of computation → Computational geometry
Estimating weighted areas under the ROC curve

Exponential bounds on the estimation error are given for the plug-in estimator of weighted areas under the ROC curve. The bounds hold for single score functions and uniformly over classes of functions, whose complexity can be controlled by Gaussian or Rademacher averages. The results justify learning algorithms which select score functions to maximize the empirical partial area under the curve (pAUC). They also illustrate the use of some recent advances in the theory of nonlinear empirical processes
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network

To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability. To better utilize the document network, we ﬁrst propose graph Poisson factor analysis (GPFA) that constructs a probabilistic model for interconnected documents and also provides closed-form Gibbs sampling up-date equations, moving beyond sophisticated approximate assumptions of existing RTMs. Extending GPFA, we develop a novel hierarchical RTM named graph Poisson gamma belief network (GPGBN), and further introduce two different Weibull distribution based variational graph auto-encoders for efﬁcient model inference and effective network information aggregation. Experimental results demonstrate that our models extract high-quality hierarchical latent document representations, leading to improved performance over baselines on various graph analytic tasks.
Cooperative Multi-player Bandit Optimization

Consider a team of cooperative players that take actions in a networked-environment. At each turn, each player chooses an action and receives a reward that is an unknown function of all the players’ actions. The goal of the team of players is to learn to play together the action proﬁle that maximizes the sum of their rewards. However, players cannot observe the actions or rewards of other players, and can only get this information by communicating with their neighbors. We design a distributed learning algorithm that overcomes the informational bias players have towards maximizing the rewards of nearby players they got more information about. We assume twice continuously differentiable reward functions and constrained convex and compact action sets. Our communication graph is a random time-varying graph that follows an ergodic Markov chain. We prove that even if at every turn players take actions based only on the small random subset of the players’ rewards that they know, our algorithm converges with probability 1 to the set of stationary points of (projected) gradient ascent on the sum of rewards function. Hence, if the sum of rewards is concave, then the algorithm converges with probability 1 to an optimal action proﬁle.
Whole scene reconstruction of medical service robots fused with key frame focus stack images

Facing the precise service requirements of medical service robots in irregular environments, to solve the problem of scene depth estimation and medical service scene reconstruction of monocular robots, an algorithm framework for whole scene three-dimensional (3D) point cloud reconstruction based on scene focus stack images fusion with monocular SLAM sparse key frames is proposed. The algorithm first collects the focus stack image under the initial viewing angle through the medical service robot mobile terminal of the camera. On the server side, multi-frame focused images are iteratively reconstructed by light field imaging technology to reconstruct the scene depth image. Further generate a high-precision 3D point cloud reconstruction image of one single scene. Finally combine the sparse key frame information of the monocular SLAM on the mobile terminal and the camera movement pose information. The 3D point cloud reconstruction within the scope of the whole medical service scene is realized through multi-perspective point cloud matching. Provide scene point cloud reconstruction information for the target recognition and navigation of medical service robots.
Mining Personality Traits from Social Text Messages

Recently, approaches of applying personality on practical applications have attracted attention from multidisciplinary areas beyond psychology. An example is the personality-based recommender systems that have been successfully applied to music, movie, and game recommendation tasks. However, finding the personality traits of a person is not a trivial task. In this work, we will propose an automatic scheme for identifying the Big Five personality traits using text mining techniques. We analyzed the social messages posted by Facebook users to find the correlations among keywords and personality traits. The personality traits of a person can then be identified by examining his social messages according to such correlations. We performed the experiments using the myPersonality dataset and obtained promising results.
Biodegradation of 2-Chlorobenzoic Acid by Enterobacter cloacae: Growth Kinetics and Effect of Growth Conditions

ABSTRACT Enterobacter cloacae was originally isolated from soil irrigated with wastewater on the basis of its ability to grow with linear alkylbenzene sulfonate (LAS) as the sole source for carbon and energy. The isolated bacterium was grown in batch cultures using a 2-chlorobenzoic acid (2-CBA)-containing minimal salt medium (MSM). 2-CBA was found to be the sole source for carbon and energy. 2-CBA inhibited the growth rate with a maximum concentration of 10 mM, after which no growth occurred. The Haldane model was used to predict the specific growth rate concentration data. 2-CBA degradation by starved E. cloaca cells was faster than that of nonstarved cells. The maximum growth rates on 2-CBA (2 mM) for starved and nonstarved cells reached only 0.34 and 0.28 h−1, respectively. Glucose, lactose, sucrose, maltose, succinic acid, and mannitol as additional carbon sources at a fixed concentration (0.2%) caused the degradation rate of 2-CBA to proceed faster at ranges between 1.08- and 1.5-fold higher than that of the control. In contrast, using only fructose and sorbitol as the carbon sources showed catabolic repression of the degradation activity of 2-CBA by E. cloaca cells, although their cell mass was improved. All nitrogen sources supplied caused an increase in cell mass, whereas only lysine, alanine, glutamine, casein, and yeast extract caused a decrease in the degradation rate of 2-CBA, with a range between 12% and 28%. The activity of C120 could be detected in a crude extract of E. cloacae cells, indicating that the chloroaromatic ring fission occurs through the ortho pathways, not through the meta pathways. The data showed that different initial cell (inocula) densities did not affect the induction time for 2-CBA degradation. However, doubling the initial cell densities reduced the time required for reaching the complete degradation. 2-CBA degradation was optimally achieved at a 37°C incubation temperature and a pH of 7.5.
Robust Filtering for Discrete-Time Linear Parameter-Varying Descriptor Systems

This paper deals with robust state estimation for discrete-time, linear parameter varying (LPV) descriptor systems. It is assumed that all the system state-space matrices are affine functions of the uncertain parameters and both the parameters and their variations are bounded functions of time with known minimum and maximum values. First, necessary and sufficient conditions are proposed for admissibility and bounded realness for discrete linear time-varying (DLTV) descriptor systems. Next, two convex optimisation based methods are proposed for designing admissible stationary linear descriptor filters for LPV descriptor systems which ensure a prescribed upper bound on the ℓ2-induced gain from the noise signal to the estimation error regardless of model uncertainties. The proposed filter design results were based on parameter-dependent generalised Lyapunov functions, and full-order, augmented-order and reduced-order filters were considered. Numerical examples are presented to show the effectiveness of the proposed filtering scheme. In particular, the proposed approach was used to estimate the state variables of a controlled horizontal 2-DOF robotic manipulator based on noisy measurements.
A Unified Framework of FPT Approximation Algorithms for Clustering Problems

In this paper, we present a framework for designing FPT approximation algorithms for many k -clustering problems. Our results are based on a new technique for reducing search spaces. A reduced search space is a small subset of the input data that has the guarantee of containing k clients close to the facilities opened in an optimal solution for any clustering problem we consider. We show, somewhat surprisingly, that greedily sampling O ( k ) clients yields the desired reduced search space, based on which we obtain FPT( k )-time algorithms with improved approximation guarantees for problems such as capacitated clustering, lower-bounded clustering, clustering with service installation costs, fault tolerant clustering, and priority clustering
Online Convex Optimization Over Erdos-Renyi Random Networks

The work studies how node-to-node communications over an Erd ˝ os-Rényi random network inﬂuence distributed online convex optimization, which is vital in solving large-scale machine learning in antagonistic or changing environments. At per step, each node (computing unit) makes a local decision, experiences a loss evaluated with a convex function, and communicates the decision with other nodes over a network. The node-to-node communications are described by the Erd ˝ os-Rényi rule, where independently each link takes place with a probability p over a prescribed connected graph. The objective is to minimize the system-wide loss accumulated over a ﬁnite time horizon. We consider standard distributed gradient descents with full gradients, one-point bandits and two-points bandits for convex and strongly convex losses, respectively. We establish how the regret bounds scale with respect to time horizon T , network size N , decision dimension d , and an algebraic network connectivity. The regret bounds scaling with respect to T match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e.g., O ( √ T ) and O (ln( T )) regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively. For classical Erd ˝ os-Rényi networks over all-to-all possible node communications, the regret scalings with respect to the probability p are analytically established, based on which the tradeoff between the communication overhead and computation accuracy is clearly demonstrated. Numerical studies have validated the theoretical ﬁndings.
Bayesian Pseudocoresets

Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data. Recent work has found that a small, weighted subset of data (a coreset ) may be used in place of the full dataset during inference, taking advantage of data redundancy to reduce computational cost. However, this approach has limitations in the increasingly common setting of sensitive, high-dimensional data. Indeed, we prove that there are situations in which the Kullback-Leibler (KL) divergence between the optimal coreset and the true posterior grows with data dimension; and as coresets include a subset of the original data, they cannot be constructed in a manner that preserves individual privacy. We address both of these issues with a single uniﬁed solution, Bayesian pseudocoresets —a small weighted collection of synthetic “pseudodata”—along with a variational optimization method to select both pseudodata and weights. The use of pseudodata (as opposed to the original datapoints) enables both the summarization of high-dimensional data and the differentially private summarization of sensitive data. Real and synthetic experiments on high-dimensional data demonstrate that Bayesian pseudocoresets achieve signiﬁcant improvements in posterior approximation error compared to traditional coresets, and that pseudocoresets provide privacy without a signiﬁcant loss in approximation quality.
Fast and Accurate $k$-means++ via Rejection Sampling

$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance. Despite its wide adoption, $k$-means++ sometimes suffers from being slow on large data-sets so a natural question has been to obtain more efficient algorithms with similar guarantees. In this paper, we present a near linear time algorithm for $k$-means++ seeding. Interestingly our algorithm obtains the same theoretical guarantees as $k$-means++ and significantly improves earlier results on fast $k$-means++ seeding. Moreover, we show empirically that our algorithm is significantly faster than $k$-means++ and obtains solutions of equivalent quality.
Robust illumination invariant depth recovery by combining local phase and intensity information of images

Abstract. Both intensity and phase information of images have been the most important similarity measures in solving the general stereo matching problem. Intensity contains most of the imaging information of the scene/object, yet the phase information could reflect the local structure of images, which is more robust than the grayscale value. Plenty of work has been done in intensity-based or phase-based stereo matching methods. However, neither of them could work well enough when process images were taken under varied illuminations. A robust depth recovery method by making use of both intensity and phase information of stereo images properly is proposed. Firstly, 2D signal analysis is conducted by using the multiscale monogenic wavelet transform, from which local phase and intensity amplitude information are extracted into different scales. Secondly, disparity maps are estimated in different scales based on the intensity information. Thirdly, the optimal disparity is obtained by weighted-combining the disparity maps in different scales. The weighted coefficients are computed by making use of the phase information. Extensive experimental evaluation demonstrates the benefits of the proposed method.
HIP-20: Integration of Vehicle-HSM-Generated Credentials into Plug-and-Charge Infrastructure

Plug-and-Charge (PnC) standards such as ISO 15118-20 enable the charging of Electric Vehicles (EVs) with (nearly) no user intervention by storing authentication credentials directly in the vehicle. However, these credentials are generated in backend systems of vehicle manufacturers and charging service providers (called Mobility Operators (MOs)) making them vulnerable to attacks on corporate networks. In previous work, we proposed HIP, an ISO 15118 extension where credentials are generated in a Hardware Security Module (HSM) within the EV and the private parts of the credentials never leave the Hardware Security Module (HSM). In this paper, we propose HIP-20, which improves HIP by enabling easy integration into existing backend infrastructures and processes. HIP-20 enables support of existing Certificate Authoritiess (CAs) and established processes such as using Certificate Signing Requests (CSRs), enables credential provisioning via channels outside of ISO 15118, and makes necessary adaptations required due to changes in the new edition ISO 15118-20. Our evaluation shows that HIP-20 significantly increases security and interoperability with existing processes and infrastructures.
Comparison of Audio and Visual Cues to Support Remote Guidance in Immersive Environments

Collaborative virtual environments provide the ability for collocated and remote participants to communicate and share information with each other. For example, immersive technologies can be used to facilitate collaborative guidance during navigation of an unfamiliar environment. However, the design space of 3D user interfaces for supporting collaborative guidance tasks, along with the advantages and disadvantages of different immersive communication modalities to support these tasks, are not well understood. In this paper, we investigate three different methods for providing assistance (visual-only, audio-only, and combined audio/visual cues) using an asymmetric collaborative guidance task. We developed a novel experimental design and virtual reality scenario to evaluate task performance during navigation of a complex and dynamic environment while simultaneously avoiding observation by patrolling sentries. Two experiments were conducted: a dyadic study conducted at a large public event and a controlled lab study using a confederate. Combined audio/visual guidance cues were rated easier to use and more effectively facilitated the avoidance of sentries compared with the audio-only condition. The presented work has the potential to inform the design of future experiments and applications that involve communication modalities to support collaborative guidance tasks with immersive technologies.