Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots

We present the conceptual formulation, design, fabrication, control, and commercial translation of an Internet of Things (IoT)-enabled social robot as mapped through validation of human emotional response to its affective interactions. The robot design centers on a humanoid hybrid face that integrates a rigid faceplate with a digital display to simplify conveyance of complex facial movements while providing the impression of 3-D depth. We map the emotions of the robot to specific facial feature parameters, characterize recognisability of archetypical facial expressions, and introduce pupil dilation as an additional degree of freedom for emotion conveyance. Human interaction experiments demonstrate the ability to effectively convey emotion from the hybrid-robot face to humans. Conveyance is quantified by studying neurophysiological electroencephalography (EEG) response to perceived emotional information as well as through qualitative interviews. The results demonstrate core hybrid-face robotic expressions can be discriminated by humans (80%+recognition) and invoke face-sensitive neurophysiological event-related potentials, such as N170 and vertex positive potentials in EEG. The hybrid-face robot concept has been modified, implemented, and released in the commercial IoT robotic platform Miko (“My Companion”), an affective robot currently in use for human–robot interaction with children. We demonstrate that human EEG responses to Miko emotions are comparative to that of the hybrid-face robot validating design modifications implemented for large-scale distribution. Finally, interviews show above 90% expression recognition rates in our commercial robot. We conclude that simplified hybrid-face abstraction conveys emotions effectively and enhances human–robot interaction.
Fragmentation in trader preferences among multiple markets: market coexistence versus single market dominance

Technological advancement has led to an increase in the number and type of trading venues and a diversification of goods traded. These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets? In this paper, we address these questions in a stylized model of zero-intelligence traders who make repeated decisions at which of three available markets to trade. We analyse the model numerically and analytically and find that the traders’ decision parameters—memory length and how strongly decisions are based on past success—make the key difference between consolidated and fragmented steady states of the population of traders. All three markets coexist with equal shares of traders only when either learning is too weak and traders choose randomly, or when markets are identical. In the latter case, the population of traders fragments across the markets. With different markets, we note that market dominance is the more typical scenario. Overall we show that, contrary to previous research emphasizing the role of traders’ heterogeneity, market coexistence can emerge simply as a consequence of co-adaptation of an initially homogeneous population of traders.
Algorithms for finding k in k-means

$k-$means Clustering requires as input the exact value of $k$, the number of clusters. Two challenges are open: (i) Is there a data-determined definition of $k$ which is provably correct and (ii) Is there a polynomial time algorithm to find $k$ from data ? This paper provides the first affirmative answers to both these questions. As common in the literature, we assume that the data admits an unknown Ground Truth (GT) clustering with cluster centers separated. This assumption alone is not sufficient to answer Yes to (i). We assume a novel, but natural second constraint called no tight sub-cluster (NTSC) which stipulates that no substantially large subset of a GT cluster can be "tighter" (in a sense we define) than the cluster. Our yes answer to (i) and (ii) are under these two deterministic assumptions. We also give polynomial time algorithm to identify $k$. Our algorithm relies on NTSC to peel off one cluster at a time by identifying points which are tightly packed. We are also able to show that our algorithm(s) apply to data generated by mixtures of Gaussians and more generally to mixtures of sub-Gaussian pdf's and hence are able to find the number of components of the mixture from data. To our knowledge, previous results for these specialized settings as well, assume generally that $k$ is given besides the data.
Variational quantum simulations of stochastic differential equations

Stochastic differential equations (SDE), which models uncertain phenomena as the time evolution of random variables, are exploited in various fields of natural and social sciences such as finance. Since SDEs rarely admit analytical solutions and must usually be solved numerically with huge classical-computational resources in practical applications, there is strong motivation to use quantum computation to accelerate the calculation. Here, we propose a quantum-classical hybrid algorithm that solves SDEs based on variational quantum simulation (VQS). We first approximate the target SDE by a trinomial tree structure with discretization and then formulate it as the time-evolution of a quantum state embedding the probability distributions of the SDE variables. We embed the probability distribution directly in the amplitudes of the quantum state while the previous studies did the square-root of the probability distribution in the amplitudes. Our embedding enables us to construct simple quantum circuits that simulate the time-evolution of the state for general SDEs. We also develop a scheme to compute the expectation values of the SDE variables and discuss whether our scheme can achieve quantum speed-up for the expectation-value evaluations of the SDE variables. Finally, we numerically validate our algorithm by simulating several types of stochastic processes. Our proposal provides a new direction for simulating SDEs on quantum computers.
FUSION PAR CONFINEMENT INERTIEL ÉTAT TECHNIQUE ACTUEL ET PERSPECTIVES

On rappelle les conditions a realiser dans le cas de la fusion nucleaire pour aboutir a un bilan positif d'energie et que deux grandes voies sont possibles : le confinement magnetique et le confinement inertiel. On aborde ensuite, en se limitant a la fusion inertielle, les conditions a realiser au moyen de lasers de grande puissance, de faisceaux d'electrons ou d'ions, compte tenu des mecanismes physiques d'interaction avec la matiere. On decrit l'etat actuel de developpement de ces outils et on examine les limitations techniques rencontrees ainsi que les perspectives d'amelioration. On montre que les equipements necessaires pour concevoir un reacteur restent encore tres loin des possibilites, mais que les importants travaux realises dans le monde a l'heure actuelle doivent permettre de beaucoup mieux cerner les conditions a remplir et d'en deduire les meilleures approches.
The Role Played by Water in Spin State Variations among Nickel(II) Halide Complexes Containing (7R,14S)-5,5,7,12,12,14-Hexamethyl-1,4,8,11-tetraazacyclotetradecane

A complex dihydrate formed with nickel(II) bromide and the title macrocyclic ligand (memo-Me6[14]aneN4) crystallizes in triclinic space group P\bar1 and in orthorhombic space group Pcab. The crystal and molecular structures of the two crystal modifications have been determined by single crystal X-ray diffraction. In each compound, the nickel(II) is surrounded by a square-planar array of nitrogen atoms, giving low-spin complexes. In both compounds, water of crystallization and bromide ions are located above the hydrogen atoms of N–H groups and form hydrogen-bonded chelate rings of the type Ni–N–H···Br−···H–O···H–N. The N–H groups adjacent to the single methyl carbon and the geminal dimethyl carbon interact with bromide ion and water, respectively, in the P\bar1 form, whereas vice versa in the Pcab form. Water molecules in the present crystals and those in the previously reported crystal structure of [Ni(meso-Me6[14]aneN4)]Cl2·2H2O are involved in the same type of specific hydrogen-bonds. Because of the spe.
Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition

Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor generalization performance when using limited training data. In order to address this issue, this paper presents a full Bayesian framework to account for model uncertainty in sequence discriminative training of factored TDNN acoustic models. Several Bayesian learning based TDNN variant systems are proposed to model the uncertainty over weight parameters and choices of hidden activation functions, or the hidden layer outputs. Efficient variational inference approaches using as few as one single parameter sample ensure their computational cost in both training and evaluation time comparable to that of the baseline TDNN systems. Statistically significant word error rate (WER) reductions of 0.4%–1.8% absolute (5%-11% relative) were obtained over a state-of-the-art 900 h speed perturbed Switchboard corpus trained baseline LF-MMI factored TDNN system using multiple regularization methods including F-smoothing, L2 norm penalty, natural gradient, model averaging and dropout, in addition to i-Vector plus learning hidden unit contribution (LHUC) based speaker adaptation and RNNLM rescoring. The efficacy of the proposed Bayesian techniques is further demonstrated in a comparison against the state-of-the-art performance obtained on the same task using the most recent hybrid and end-to-end systems reported in the literature. Consistent performance improvements were also obtained on a 450-h HKUST conversational Mandarin telephone speech recognition task. On a third cross domain adaptation task requiring rapidly porting a 1000-h LibriSpeech data trained system to a small DementiaBank elderly speech corpus, the proposed Bayesian TDNN LF-MMI systems outperformed the baseline system using direct weight fine-tuning by up to 2.5% absolute WER reduction.
Active Visual Localization in Partially Calibrated Environments

Humans can robustly localize themselves without a map after they get lost following prominent visual cues or landmarks. In this work, we aim at endowing autonomous agents the same ability. Such ability is important in robotics applications yet very challenging when an agent is exposed to partially calibrated environments, where camera images with accurate 6 Degree-of-Freedom pose labels only cover part of the scene. To address the above challenge, we explore using Reinforcement Learning to search for a policy to generate intelligent motions so as to actively localize the agent given visual information in partially calibrated environments. Our core contribution is to formulate the active visual localization problem as a Partially Observable Markov Decision Process and propose an algorithmic framework based on Deep Reinforcement Learning to solve it. We further propose an indoor scene dataset ACR-6, which consists of both synthetic and real data and simulates challenging scenarios for active visual localization. We benchmark our algorithm against handcrafted baselines for localization and demonstrate that our approach significantly outperforms them on localization success rate.
Environmental Effects of Chemical Additives Used in Poultry Litter and Swine Manure

Research into the use of chemical additives for litter or manure additives to reduce environmental pollution has focused on aluminum; calcium; and iron-containing compounds. Aluminum sulfate [Al(SO4)3·14H2O or Al(SO4)3· 18H2O, alum) has been studied extensively in the poultry industries and is currently being used by the U.S. Environmental Protection Agency (U.S. EPA) as a litter additive. Recent studies have been very promising for the use of aluminum chloride (AlCl3) in swine manure to reduce ammonia volatilization and odor. Use of phytase with aluminum chloride reduces levels of soluble P and protects drinking water from P contamination. Basic research into the use of Fe(II) and Fe(III) compounds in manure has shown possibilities for their use in odor and ammonia volatilization reduction. Calcium-containing compounds such as limestone may be attractive for utilization in the future due to their low cost. Besides these compounds, research into the use of other chemical compounds such as zeolite, antibiotics, potassium permanganate, hydrogen peroxide, sulfuric acid, chlorine, phosphoric acid, formaldehyde, yucca saponin, ZnSO4, and organic acids is continuing and there may be promise for the use of these compounds in the future. Long-term studies are needed to determine the effects of these compounds when used as fertilizer on plant absorption and soil quality.
RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System

Federated Learning (FL) is an emerging decentralized artificial intelligence paradigm, which promises to train a shared global model in high-quality while protecting user data privacy. However, the current systems rely heavily on a strong assumption: all clients have a wealth of ground truth labeled data, which may not be always feasible in the real life. In this paper, we present a practical Robust, and Communication-efficient Semi-supervised FL (RC-SSFL) system design that can enable the clients to jointly learn a high-quality model that is comparable to typical FL's performance. In this setting, we assume that the client has only unlabeled data and the server has a limited amount of labeled data. Besides, we consider malicious clients can launch poisoning attacks to harm the performance of the global model. To solve this issue, RC-SSFL employs a minimax optimization-based client selection strategy to select the clients who hold high-quality updates and uses geometric median aggregation to robustly aggregate model updates. Furthermore, RC-SSFL implements a novel symmetric quantization method to greatly improve communication efficiency. Extensive case studies on two real-world datasets demonstrate that RC-SSFL can maintain the performance comparable to typical FL in the presence of poisoning attacks and reduce communication overhead by $2 \times \sim 4 \times $.
Chemical and Isotopic Composition of Humic-Like Substances (HULIS) in Ambient Aerosols in Guangzhou, South China

Humic-like substances (HULIS) constitute a class of organic compounds identified in atmospheric samples that influence many properties of aerosols in the atmosphere. In this study, 6 HULIS samples were isolated from atmospheric total suspended particle (TSP) samples collected at 3 locations in Guangzhou of China, 1 each in summer and winter. On the basis of analyses by elemental analyzer, more detailed chemical species of elements were explored by proton nuclear magnetic resonance (1H NMR) spectroscopy for hydrogen and X-ray photoelectron spectroscopy (XPS) for carbon, oxygen, nitrogen, and sulfur. The sources of HULIS were identified by carbon isotopic techniques. The results show that HULIS made up an important component of water-soluble organic carbon (WSOC). Carbon and oxygen were the predominant components in the HULIS. The 1H NMR spectra indicated that H functional groups consisted of aliphatic C‒H, aromatic C‒H, H‒C‒C˭, and H‒C‒O groups. They were all characterized by the highest contents of aliphatic protons and the lowest contents of aromatic protons. The XPS analysis of the HULIS fraction isolated from winter samples at the urban site Wushan (WS) showed that C, O, N, and S have various chemical states. The oxidized carbon groups accounted for 30.5% of total carbon. The nitrogen species contained amide, quaternary, and nitrate groups, and the sulfur species consisted of thiophenes, thioesters, mercaptanes, sulfones, and sulfates. The δ13C values of HULIS showed no obvious differences among the 6 HULIS samples. The δ15N values of summer HULIS samples in Maofengshan Mountain Forest Park (MFS) and Universities Town (UT) were significantly higher than those of winter samples. It is noteworthy that 14C data clearly indicate that even in urban areas biomass-derived carbon was the major component in the atmospheric HULIS samples. Copyright 2012 American Association for Aerosol Research
Semantic Image Synthesis via Efficient Class-Adaptive Normalization

Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away. Despite its impressive performance, a more thorough understanding of the advantages inside the box is still highly demanded to help reduce the significant computation and parameter overhead introduced by this novel structure. In this paper, from a return-on-investment point of view, we conduct an in-depth analysis of the effectiveness of this spatially-adaptive normalization and observe that its modulation parameters benefit more from semantic-awareness rather than spatial-adaptiveness, especially for high-resolution input masks. Inspired by this observation, we propose class-adaptive normalization (CLADE), a lightweight but equally-effective variant that is only adaptive to semantic class. In order to further improve spatial-adaptiveness, we introduce intra-class positional map encoding calculated from semantic layouts to modulate the normalization parameters of CLADE and propose a truly spatially-adaptive variant of CLADE, namely CLADE-ICPE. %Benefiting from this design, CLADE greatly reduces the computation cost while being able to preserve the semantic information in the generation. Through extensive experiments on multiple challenging datasets, we demonstrate that the proposed CLADE can be generalized to different SPADE-based methods while achieving comparable generation quality compared to SPADE, but it is much more efficient with fewer extra parameters and lower computational cost. The code is available at https://github.com/tzt101/CLADE.git
On Epitaxy of Ultrathin Ni[sub 1−x]Pt[sub x] Silicide Films on Si(001)

Epitaxial Ni(Pt)Si2-y (y < 1) films readily grow upon thermal treatment of 2 nm thick Ni and Ni0.96Pt0. 04 films deposited on Si(001). For annealing at 500 degrees C, the films are 5.4-5.6 nm thick with 61-70 mu cm in resistivity. At 750 degrees C, the epitaxial Ni(Pt)Si2-y films become 6.1-6.2 nm thick with a resistivity of 42-44 mu cm. Structural analysis reveals twins, facet wedges, and thickness inhomogeneities in the films grown at 500 degrees C. For higher temperature, an almost defect-free NiSi2-y film with a flat and sharp interface is formed. The presence of Pt makes the aforementioned imperfections more persistent.
Surface film formation on a graphite electrode in Li‐ion batteries: AFM and XPS study

The formation of a passivation film (solid electrolyte interphase, SEI) at the surface of the negative electrode of full LiCoO2/graphite lithium‐ion cells using LiPF6 (1M) in carbonate solvents as electrolyte was investigated by means of x‐ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). The analyses were carried out at different potentials of the first and the fifth cycles, showing the potential‐dependent character of the surface‐film species formation. These species were mainly identified as Li2CO3 up to 3.8 V and LiF up to 4.2 V. This study shows the formation of the SEI during charging and its partial dissolution during discharge. Copyright © 2005 John Wiley & Sons, Ltd.
Investigation of gellan networks and gels by atomic force microscopy

Methods previously developed for the atomic force microscopy (AFM) imaging of individual polysaccharides (Kirby, A. R.; Gunning, A. P.; Morris, V. J. Biopolymers 1996, 38, 355−366) have been extended in order to image networks and gels formed by the bacterial polysaccharide gellan gum. Networks were formed by air-drying solutions of K+ gellan onto freshly cleaved mica. AFM images were obtained in the direct current contact mode at constant force under butanol. Network formation can be inhibited by diluting the stock gellan solution or by converting K+ gellan into the tetramethylammonium salt form. Inhibition of network formation led to AFM images of heterogeneous populations of gellan aggregates (gel precursors). Attempts have been made to image the surface of bulk aqueous gellan gels under butanol. The quality of the images obtained improved with increasing gel modulus. For rigid acid-set gellan gels, molecular resolution was achieved, revealing a bifurcated branched fibrous network.
Concise route to functionalized carbon nanotubes

A concise route was developed to functionalize carbon nanotubes via the esterification reaction of the carboxylate salt of carbon nanotubes and alkyl halides in the presence of a phase-transfer reagent in water. The series of functionalized CNTs obtained were characterized and analyzed by FT-IR, 1H NMR, TEM, and TGA techniques.
Water transport and clustering in poly[vinyl cloride], poly[oxymethylene], and other polymers

Abstract The diffusion constants for water vapor in poly (vinyl chloride) and poly(oxymethylene) have been determined by the time-lag method. In addition the sorption isotherms have been measured by direct weighing. The values of the solubility coefficient obtained gravimetrically have been compared with those calculated from the quotient of the permeability and diffusion constants obtained from the time lags. Quite different patterns of behavior were exhibited by the two polymers. In the case of poly(oxvmethylene) the two sets of values of the solubility coefficients were quite similar and fitted a normal Brunauer—Emmett-Teller (BET) type-III sorption isotherm. With poly(vinyl chloride), however, the solubilities calculated from P/D obeyed Henry's law and were considerably less than those obtained by direct weighing (except at low activities) which again were of the BET type III. These results were compared to those found with other polymers and have been interpreted in terms of the different degrees of .