Miracle-dz/UniFAR-FTUs huggingface dataset:
The Voice of Emotion across Species: How Do Human Listeners Recognize Animals' Affective States?

Voice-induced cross-taxa emotional recognition is the ability to understand the emotional state of another species based on its voice. In the past, induced affective states, experience-dependent higher cognitive processes or cross-taxa universal acoustic coding and processing mechanisms have been discussed to underlie this ability in humans. The present study sets out to distinguish the influence of familiarity and phylogeny on voice-induced cross-taxa emotional perception in humans. For the first time, two perspectives are taken into account: the self- (i.e. emotional valence induced in the listener) versus the others-perspective (i.e. correct recognition of the emotional valence of the recording context). Twenty-eight male participants listened to 192 vocalizations of four different species (human infant, dog, chimpanzee and tree shrew). Stimuli were recorded either in an agonistic (negative emotional valence) or affiliative (positive emotional valence) context. Participants rated the emotional valence of the stimuli adopting self- and others-perspective by using a 5-point version of the Self-Assessment Manikin (SAM). Familiarity was assessed based on subjective rating, objective labelling of the respective stimuli and interaction time with the respective species. Participants reliably recognized the emotional valence of human voices, whereas the results for animal voices were mixed. The correct classification of animal voices depended on the listener's familiarity with the species and the call type/recording context, whereas there was less influence of induced emotional states and phylogeny. Our results provide first evidence that explicit voice-induced cross-taxa emotional recognition in humans is shaped more by experience-dependent cognitive mechanisms than by induced affective states or cross-taxa universal acoustic coding and processing mechanisms.
Agrobacterium-Mediated Disruption of a Nonribosomal Peptide Synthetase Gene in the Invertebrate Pathogen Metarhizium anisopliae Reveals a Peptide Spore Factor

ABSTRACT Numerous secondary metabolites have been isolated from the insect pathogenic fungus Metarhizium anisopliae, but the roles of these compounds as virulence factors in disease development are poorly understood. We targeted for disruption by Agrobacterium tumefaciens-mediated transformation a putative nonribosomal peptide synthetase (NPS) gene, MaNPS1. Four of six gene disruption mutants identified were examined further. Chemical analyses showed the presence of serinocyclins, cyclic heptapeptides, in the extracts of conidia of control strains, whereas the compounds were undetectable in ΔManps1 mutants treated identically or in other developmental stages, suggesting that MaNPS1 encodes a serinocyclin synthetase. Production of the cyclic depsipeptide destruxins, M. anisopliae metabolites also predicted to be synthesized by an NPS, was similar in ΔManps1 mutant and control strains, indicating that MaNPS1 does not contribute to destruxin biosynthesis. Surprisingly, a MaNPS1 fragment detected DNA polymorphisms that correlated with relative destruxin levels produced in vitro, and MaNPS1 was expressed concurrently with in vitro destruxin production. ΔManps1 mutants exhibited in vitro development and responses to external stresses comparable to control strains. No detectable differences in pathogenicity of the ΔManps1 mutants were observed in bioassays against beet armyworm and Colorado potato beetle in comparison to control strains. This is the first report of targeted disruption of a secondary metabolite gene in M. anisopliae, which revealed a novel cyclic peptide spore factor.
Dynamic Sketching for Graph Optimization Problems with Applications to Cut-Preserving Sketches

In this paper, we introduce a new model for sublinear algorithms called \emph{dynamic sketching}. In this model, the underlying data is partitioned into a large \emph{static} part and a small \emph{dynamic} part and the goal is to compute a summary of the static part (i.e, a \emph{sketch}) such that given any \emph{update} for the dynamic part, one can combine it with the sketch to compute a given function. We say that a sketch is \emph{compact} if its size is bounded by a polynomial function of the length of the dynamic data, (essentially) independent of the size of the static part. A graph optimization problem $P$ in this model is defined as follows. The input is a graph $G(V,E)$ and a set $T \subseteq V$ of $k$ terminals; the edges between the terminals are the dynamic part and the other edges in $G$ are the static part. The goal is to summarize the graph $G$ into a compact sketch (of size poly$(k)$) such that given any set $Q$ of edges between the terminals, one can answer the problem $P$ for the graph obtained by inserting all edges in $Q$ to $G$, using only the sketch. We study the fundamental problem of computing a maximum matching and prove tight bounds on the sketch size. In particular, we show that there exists a (compact) dynamic sketch of size $O(k^2)$ for the matching problem and any such sketch has to be of size $\Omega(k^2)$. Our sketch for matchings can be further used to derive compact dynamic sketches for other fundamental graph problems involving cuts and connectivities. Interestingly, our sketch for matchings can also be used to give an elementary construction of a \emph{cut-preserving vertex sparsifier} with space $O(kC^2)$ for $k$-terminal graphs; here $C$ is the total capacity of the edges incident on the terminals. Additionally, we give an improved lower bound (in terms of $C$) of $\Omega(C/\log{C})$ on size of cut-preserving vertex sparsifiers.
The Inexact Newton Method with Semi Automatic Differentiation

Nonlinear optimization plays an important role in science computation and engineering analysis. Newton like method is popular for solving the nonlinear optimization problem. an inexact Newton algorithm is proposed recently, in which the preconditioned conjugate gradient method is applied to solve the Newton equations. Later, the algorithm is improved by efficiently using automatic differentiation. In practical application, large-scale systems of nonlinear equations typically exhibit either sparsity or other specialstructures in their Jacobian matrices. In this paper, we propose the structure inexact Newton algorithm (SINA), The algorithm utilized Semi-AD techniques can improve the algorithm efficiency by avoiding the unnecessary computation. Based on the efficiency coefficient defined by Brent, a theoretical efficiency ratio of SINA to the old algorithm is introduced. It has-been shown that SINA is much more efficient than the old one. Furthermore, this theoretical conclusion is supported by numerical experiments.
Nonlinearity analysis of R-2R ladder-based current-steering digital to analog converter

This paper presents a theoretical nonlinearity analysis of R-2R ladder-based current-steering digital to analog converter (DAC). Besides the current mismatches, this paper analyzes static nonlinearity caused by resistor mismatches in the R-2R ladder. An elliptic equation is then developed capable of deriving the required current and resistor matching of N-bits R-2R ladder-based current-steering DAC simultaneously to achieve the static nonlinearity specifications and required yield. Monte-Carlo simulation results agree well with the theoretical equation and confirm its accuracy.
Automatic Recognition Algorithm for Digitally Modulated Signals.

Since the end of the eighties, many researchers have been investigating the automatic identification and recognition of modulated communication signals. Recently, many algorithms have been proposed to automatically identify and recognize the digitally modulated signals. Such types of signals have been used in many applications such as: Electronic warfare, surveillance and threat analysis, control of communication quality etc. In this paper we outline the major problems, approaches and some algorithms to recognize automatically the type of the modulated signals. First of all, some algorithms to estimate some important features of the modulated signals, as the wave carrier frequency, have been discussed and simulated. At the second part, we propose a modified version of the well-known algorithm DMRA (Digital Modulation Recognition Algorithm) proposed by Azzouz and Nandi. Finally, many experiments and simulations have been conducted and presented.
Improving Sampling‐based Motion Control

We address several limitations of the sampling‐based motion control method of Liu et at. [ LYvdP* 10 ]. The key insight is to learn from the past control reconstruction trials through sample distribution adaptation. Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently construct open‐loop controls for long and challenging reference motions in good quality. Our ideas are intuitive and the implementations are simple. We compare the improved algorithm with the original algorithm both qualitatively and quantitatively, and demonstrate the effectiveness of the improved algorithm with a variety of motions ranging from stylized walking and dancing to gymnastic and Martial Arts routines.
Low area pipelined circuits by multi-clock cycle paths and clock scheduling

A new algorithm is proposed to reduce the number of intermediate registers of a pipelined circuit using a combination of multi-clock cycle paths and clock scheduling. The algorithm analyzes the pipelined circuit and determines the intermediate registers that can be removed. An efficient subsidiary algorithm is presented that computes the minimum feasible clock period of a circuit containing multi-clock cycle paths. Experiments with a pipelined adder and multiplier verify that the proposed algorithm can reduce the number of intermediate registers without degrading performance, even when delay variations exist
WASP-Bench: an MT lexicographers’ workstation supporting state-of-the-art lexical disambiguation

Most MT lexicography is devoted to developing rules of the kind, “in context C, translate source-language word S as target-language word T”. Very many such rules are required, producing them is laborious, and MT companies standardly spend large sums on it. We present the WASP-Bench, a lexicographer's workstation for the rapid and semi-automatic development of such rule-sets. The WASP-Bench makes use of a large source-language corpus and state-of-the-art techniques for Word Sense Disambiguation. We show that the WSD accuracy is on a par with the best results published to date, with the advantage that the WASP-Bench, unlike other high- performance systems, does not require a sense-disambiguated training corpus as input. The WASP-Bench is designed to fit readily with MT companies' working practices, as it may be used for as many or as few source language words as present disambiguation problems for a given target.
Design of LQR controller with big bang-big crunch optimization algorithm based on time domain criteria

LQR controller is the most popular technique that provides an optimal control law for linear systems among the state space feedback control strategies. However, the conventional LQR controller synthesis is unfortunately an iterative process due to the trial and error approach involved in determining the parameters values of the weighing matrices Q and R. Here, the Big Bang-Big Crunch (BB-BC) optimization algorithm is used that optimizes a time domain fitness function in the design of the state feedback optimal control law and thus avoiding the repeated adjustment process of LQR parameters. In this study, a special performance fitness function that is inversely proportional to the certain time domain step response criteria of a dynamical system is proposed for the optimization procedure. In order to test the performance of the proposed method, firstly a simulation study is done within the MATLAB to stabilize an inverted pendulum on cart. Then, the proposed controller is used in a real time implementation to stabilize a DC-DC boost converter benchmark in the lab. Both MATLAB simulations and laboratory experiments demonstrate the effectiveness of the proposed controller.
Reciprocal attentive communication in remote meeting with a humanoid robot

In this paper, we investigate the reciprocal attention modality in remotecommunication. A remote meeting system with a humanoid robot avatar is proposedto overcome the invisible wall for a video conferencing system. Ourexperimental result shows that a tangible robot avatar provides more effectivereciprocal attention against video communication. The subjects in the experimentare asked to determine whether a remote participant with the avatar is activelylistening or not to the local presenter's talk. In this system, the head motionof a remote participant is transferred and expressed by the head motion of ahumanoid robot. While the presenter has difficulty in determining the extentof a remote participant's attention with a video conferencing system, she/he hasbetter sensing of remote attentive states with the robot. Based on theevaluation result, we propose a vision system for the remote user thatintegrates omni-directional camera and robot-eye camera images to provide a wideview with a delay compensation feature.
Comment on "Ensemble Projection for Semi-supervised Image Classification"

In a series of papers by Dai and colleagues [1,2], a feature map (or kernel) was introduced for semi- and unsupervised learning. This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels. In this critique, we analyze the latest version of this series of papers, which is called Ensemble Projections [2]. We show that the results reported in [2] were not well conducted, and that Ensemble Projections performs poorly for semi-supervised learning.
The Jrpm system for dynamically parallelizing Java programs

We describe the Java runtime parallelizing machine (Jrpm), a complete system for parallelizing sequential programs automatically. Jrpm is based on a chip multiprocessor (CMP) with thread-level speculation (TLS) support. CMPs have low sharing and communication costs relative to traditional multiprocessors, and thread-level speculation (TLS) simplifies program parallelization by allowing us to parallelize optimistically without violating correct sequential program behavior. Using a Java virtual machine with dynamic compilation support coupled with a hardware profiler, speculative buffer requirements and inter-thread dependencies of prospective speculative thread loops (STLs) are analyzed in real-time to identify the best loops to parallelize. Once sufficient data has been collected to make a reasonable decision, selected loops are dynamically recompiled to run in parallel. Experimental results demonstrate that Jrpm can exploit thread-level parallelism with minimal effort from the programmer. On four processors, we achieved speedups of 3 to 4 for floating point applications, 2 to 3 on multimedia applications, and between 1.5 and 2.5 on integer applications. Performance was achieved by automatic selection of thread decompositions by the hardware profiler, intra-procedural optimizations on code compiled dynamically into speculative threads, and some minor programmer transformations for exposing parallelism that cannot be performed automatically.
Applications of pixel textures in visualization and realistic image synthesis

With fast3D graphicsbecomingmoreandmoreavailableevenon low endplatforms,thefocusin developingnew graphicshardware is beginning to shift towardshigher quality renderingand additional functionality insteadof simply higher performanceimplementationsof the traditionalgraphicspipeline. On this searchfor improved quality it is importantto identify a powerful set of orthogonalfeaturesto be implementedin hardware,which canthen beflexibly combinedto form new algorithms. Pixel texturesareanOpenGLextensionby SiliconGraphicsthat fits into this category. In this paper , we demonstratethebenefitsof thisextensionby presentingseveraldifferentalgorithmsexploiting its functionalityto achievehighquality, highperformancesolutions for a variety of differentapplicationsfrom scientificvisualization andrealisticimagesynthesis.Weconcludethatpixel texturesarea valuable,powerful featurethatshouldbecomea standardin future graphicssystems. CR Categories: I.3. 3 [Computer Graphics]: Picture/Image Generation—Bitmapand framebuffer operationsI.3. 3 [Computer Graphics]: Picture/ImageGeneration—Displayalgorithms I.3. 6 [ComputerGraphics]: Methodologyand Techniques—Standards I.3. 7 [Computer Graphics]: Three-DimensionalGraphics and Realism—Color , Shading,Shadowing andTexture
A Robust Interpolation Algorithm for Spectral Analysis

We propose a robust interpolation algorithm for model-based spectral analysis. Instead of estimating the spectral model directly, the so-called half spectrum, which has a one-to-one relationship with the spectrum through standard spectral decomposition, is estimated using an interpolation approach. The interpolation data consists of the values and the derivatives of the half spectrum function at a set of user-specified points, and can be easily estimated using an input-to-state filter. Our algorithm allows a large number of noisy interpolation data to be used to optimally fit a half spectrum function of a fixed order. The capability of handling large number of interpolation data makes our algorithm robust to the inherent finite sample noise in the interpolation data. The algorithm involves solving some least-squares problems and semidefinite programming problems, and is thus numerically efficient. Numerical tests show that our algorithm gives very reliable spectral estimates.
The fusion kinetics of influenza hemagglutinin expressing cells to planar bilayer membranes is affected by HA density and host cell surface

Time-resolved admittance measurements were used to follow formation of individual fusion pores connecting influenza virus hemagglutinin (HA)- expressing cells to planar bilayer membranes. By measuring in-phase, out-of-phase, and dc components of currents, pore conductances were resolved with millisecond time resolution. Fusion pores developed in stages, from small pores flickering open and closed, to small successful pores that remained open until enlarging their lumens to sizes greater than those of viral nucleocapsids. The kinetics of fusion and the properties of fusion pores were studied as functions of density of the fusion protein HA. The consequences of treating cell surfaces with proteases that do not affect HA were also investigated. Fusion kinetics were described by waiting time distributions from triggering fusion, by lowering pH, to the moment of pore formation. The kinetics of pore formation became faster as the density of active HA was made greater or when cell surface proteins were extensively cleaved with proteases. In accord with this faster kinetics, the intervals between transient pore openings within the flickering stage were shorter for higher HA density and more extensive cell surface treatment. Whereas the kinetics of fusion depended on HA density, the lifetimes of open fusion pores were independent of HA density. However, the lifetimes of open pores were affected by the proteolytic treatment of the cells. Faster fusion kinetics correlated with shorter pore openings. We conclude that the density of fusion protein strongly affects the kinetics of fusion pore formation, but that once formed, pore evolution is not under control of fusion proteins but rather under the influence of mechanical forces, such as membrane bending and tension.
Local search approach for the pairwise constrained clustering problem

The pairwise constrained clustering is the problem of partitioning a set of data points into clusters when we know in advance that some pairs of points should be in the same cluster and some pairs should not. Previous studies on this problem can be divided into three categories: modifying a traditional clustering algorithm to incorporate constraints, learning a distance measure or combining both approaches. Local search is a heuristic method for finding high-quality solutions for hard optimization problems in a reasonable computation time. It has been applied to the traditional clustering problem in many studies. However, it has never been used for the pairwise constrained clustering problem. Therefore, this paper proposes Tabu search algorithms for this problem, which were tested on several datasets. The experimental results show that these algorithms are very interesting in comparison with some state-of-the-art algorithms, COP K-means, MPC K-means and LCVQE.
Hyperspectral Image Classification Based on Fusion of Guided Filter and Domain Transform Interpolated Convolution Filter

Abstract In recent years, the spatial texture features obtained by filtering have become a hot research topic to improve hyperspectral image classification, but spatial correlation information is often lost in spatial texture information extraction. To solve this problem, a spectral-spatial classification method based on guided filtering and by the algorithm Large Margin Distribution Machine (LDM) is proposed. More specifically, the spatial texture features can be extracted by a Guided filter (GDF) from hyperspectral images whose dimensionality is reduced by a Principal Component Analysis (PCA). Spatial correlation features of the hyperspectral image are then obtained using a Domain Transform Interpolated Convolution Filter. The last step is to fuse spatial texture features and correlation features for classification by LDM. The experimental results using the actual hyperspectral image indicate that the proposed GDFDTICF-LDM method is superior to other classification methods, such as the original Support Vector Machine (SVM) with raw spectral features, dimensionality reduction features and spatial-spectral information, methods of edge-preserving filter and recursive filter, and LDM-based methods.
Generalized Epidemic Mean-Field Model for Spreading Processes Over Multilayer Complex Networks

Mean-field deterministic epidemic models have been successful in uncovering several important dynamic properties of stochastic epidemic spreading processes over complex networks. In particular, individual-based epidemic models isolate the impact of the network topology on spreading dynamics. In this paper, the existing models are generalized to develop a class of models that includes the spreading process in multilayer complex networks. We provide a detailed description of the stochastic process at the agent level where the agents interact through different layers, each represented by a graph. The set of differential equations that describes the time evolution of the state occupancy probabilities has an exponentially growing state-space size in terms of the number of the agents. Based on a mean-field type approximation, we developed a set of nonlinear differential equations that has linearly growing state-space size. We find that the latter system, referred to as the generalized epidemic mean-field (GEMF) model, has a simple structure characterized by the elements of the adjacency matrices of the network layers and the Laplacian matrices of the transition rate graphs. Finally, we present several examples of epidemic models, including spreading of virus and information in computer networks and spreading of multiple pathogens in a host population .