Miracle-dz/UniFAR-FTUs huggingface dataset:
f¨ur Mathematik in den Naturwissenschaften Leipzig Learning Binaural Spectrogram Features for Azimuthal Speaker Localization

Spatial localization of speech and other natural sounds with rich spectro-temporal structure is a computationally challenging task. It requires extraction of features which are informative about speaker’s position and yet invariant to sound level and spectral modulation present in the signal. This paper demonstrates that this can be achieved with Independent Component Analysis (ICA) applied to binaural speech spectrograms. A small subset of learned Independent Components (ICs) captures signal structure imposed by outer ears. A Gaussian Classiﬁer trained on those features, performs accurate localization on the azimuthal plane. The remaining majority of ICs have position invariant distributions, and can be used to reconstruct the spectrogram of the original sound source.
Hyperparameter estimation in Dirichlet process mixture models

In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparameter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a result, inference about this key quantity may be developed in tandem with the existing, routine Gibbs sampling algorithms for fitting such mixture models. The concept of data augmentation is important, as ever, in developing this extension of the existing algorithm. A final section notes an simple asymptotic approximation to the posterior.
Comparing isotropic and anisotropic smoothing for voxel‐based DTI analyses: A simulation study

Voxel‐based analysis (VBA) methods are increasingly being used to compare diffusion tensor image (DTI) properties across different populations of subjects. Although VBA has many advantages, its results are highly dependent on several parameter settings, such as those from the coregistration technique applied to align the data, the smoothing kernel, the statistics, and the post‐hoc analyses. In particular, to increase the signal‐to‐noise ratio and to mitigate the adverse effect of residual image misalignments, DTI data are often smoothed before VBA with an isotropic Gaussian kernel with a full width half maximum up to 16 × 16 × 16 mm3. However, using isotropic smoothing kernels can significantly partial volume or voxel averaging artifacts, adversely affecting the true diffusion properties of the underlying fiber tissue. In this work, we compared VBA results between the isotropic and an anisotropic Gaussian filtering method using a simulated framework. Our results clearly demonstrate an increased sensitivity and specificity of detecting a predefined simulated pathology when the anisotropic smoothing kernel was used. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.
Perspectives and future directions.

BACKGROUND Mechanisms of behavior change (MOBC) in treating drinking problems is a topic of growing importance. METHODS This article identifies salient themes presented in talks during the first Annual RSA Satellite on Mechanisms of Behavior Change in Alcoholism Treatment. RESULTS Themes focused on the need to move beyond a passive approach to identifying MOBC in alcoholism treatment and a number of conceptual and methodological advances were proposed. CONCLUSIONS While a consensus has emerged that more attention should be paid to MOBC in alcoholism treatment research, a subgroup of experts are calling for MOBC to be the primary focus in future efforts.
LU Factorization with Partial Pivoting for a Multi-CPU, Multi-GPU Shared Memory System

LU factorization with partial pivoting is a canonical numerical procedure and the main component of the High Performance Linpack benchmark. This article presents an implementation of the algorithm for a hybrid, shared memory, system with standard CPU cores and GPU accelerators. The optimizations include lookahead, dynamic task scheduling, fine grain parallelism for memory-bound operations, autotuning, and data layout geared towards complex memory hierarchies. Performance in excess of one Tera flop/s is achieved using four AMD Magny Cours CPUs and four NVIDIA Fermi GPUs.
Area Based Image Matching Methods – A Survey

— This paper aims to present a review of recent as well as classic area based image matching methods. Determination of three dimensional data from images is very important task in the machine vision field. Stereo vision has wide range of potential application areas including three dimensional reconstruction of a view. Matching algorithms play a key role in deciding correspondences between two image scenes. Mainly, the matching algorithms are distinguished as area based matching and feature based matching. Area based methods sometimes called correlation like methods or template matching, merge the feature detection step with the matching part. These methods deal with the images without attempting to detect salient objects. Windows of predefined size is used for the estimation of correspondence. The major goal of this paper is to provide comprehensive reference for the researchers involved in, area based image matching. Main contribution, advantages and drawbacks of the area based methods are mentioned in this paper.
Comparative study and Performance Analysis of FSR, ZRP and AODV Routing Protocols for MANET

A mobile adhoc network (MANET) is autonomous, self-organizing and self-configuring network with the capability of rapid deployment in response to application needs. Each host is equipped with a CSMA/CA (carrier sense multiple access with collision avoidance) transceiver. The mobile characteristic of mobile network creates the scenario of multihop, where the packets originated from the source host are relayed by several intermediate hosts before reaching the destination. Routing is the process of finding a path from a source to destination among randomly distributed routers. In this paper hybrid routing protocol called Zone Routing Protocol (ZRP), Fisheye State Routing Protocol (FSR) and Ad Hoc On-Demand Distance-Vector Protocol (AODV) are examined. The comparative characteristic study and performance analysis is presented using performance metrics throughput, end-to-end delay packet delivery ratio is presented using network simulator Qualnet 5.0. 2.
Chromatin without the 30-nm fiber

Several hierarchical levels of DNA packaging are believed to exist in chromatin, starting from a 10-nm chromatin fiber that is further packed into a 30-nm fiber. Transitions between the 30-nm and 10-nm fibers are thought to be essential for the control of chromatin transcriptional status. However, recent studies demonstrate that in the nuclei, DNA is packed in tightly associated 10-nm fibers that are not compacted into 30-nm fibers. Additionally, the accessibility of DNA in chromatin depends on the local mobility of nucleosomes rather than on decompaction of chromosome regions. These findings argue for reconsidering the hierarchical model of chromatin packaging and some of the basic definitions of chromatin. In particular, chromatin domains should be considered as three-dimensional objects, which may include genomic regions that do not necessarily constitute a continuous domain on the DNA chain.
Are change strategies affecting users' transportation choices?

This paper presents results from an in situ user study aimed to explore the effect of a mobile app on supporting eco transport choices by citizens of an urban area. We provided eight participants with a mobile app deploying a novel combination of goal-setting, self-monitoring, rewards and sharing features in order to observe, over a month period, relevant changes in their transport choices and habits. Overall, we observed that the digital intervention produced an increase of sustainable transport choices of 14% and contributed to raise participants' environmental awareness, particularly regarding the consequences of their daily transportation choices.
Liver-Targeted Drug Delivery Using HepDirect1 Prodrugs

Targeting drugs to specific organs, tissues, or cells is an attractive strategy for enhancing drug efficacy and reducing side effects. Drug carriers such as antibodies, natural and manmade polymers, and labeled liposomes are capable of targeting drugs to blood vessels of individual tissues but often fail to deliver drugs to extravascular sites. An alternative strategy is to use low molecular weight prodrugs that distribute throughout the body but cleave intracellularly to the active drug by an organ-specific enzyme. Here we show that a series of phosphate and phosphonate prodrugs, called HepDirect prodrugs, results in liver-targeted drug delivery following a cytochrome P450-catalyzed oxidative cleavage reaction inside hepatocytes. Liver targeting was demonstrated in rodents for MB06866 [(2R,4S)-9-[2-[4-(3-chlorophenyl)-2-oxo-1,3,2-dioxaphosphorinan-2-yl]methoxyethyl]adenine (remofovir)], a Hep-Direct prodrug of the nucleotide analog adefovir (PMEA), and MB07133 [(2R,4S)-4-amino-1-[5-O-[2-oxo-4-(4-pyridyl)-1,3,2-dioxaphosphorinan-2-yl]-β-d-arabinofuranosyl]-2(1H)-pyrimidinone], a HepDirect prodrug of cytarabine (araC) 5′-monophosphate. Liver targeting led to higher levels of the biologically active form of PMEA and araC in the liver and to lower levels in the most toxicologically sensitive organs. Liver targeting also confined production of the prodrug byproduct, an aryl vinyl ketone, to hepatocytes. Glutathione within the hepatocytes rapidly reacted with the byproduct to form a glutathione conjugate. No byproduct-related toxicity was observed in hepatocytes or animals treated with HepDirect prodrugs. A 5-day safety study in mice demonstrated the toxicological benefits of liver targeting. These findings suggest that HepDirect prodrugs represent a potential strategy for targeting drugs to the liver and achieving more effective therapies against chronic liver diseases such as hepatitis B, hepatitis C, and hepatocellular carcinoma.
Application of in silico and in vitro methods in the development of adverse outcome pathway constructs in wildlife

There is a long history of using both in silico and in vitro methods to predict adverse effects in humans and environmental species where toxicity data are lacking. Currently, there is a great deal of interest in applying these methods to the development of so-called ‘adverse outcome pathway’ (AOP) constructs. The AOP approach provides a framework for organizing information at the chemical and biological level, allowing evidence from both in silico and in vitro studies to be rationally combined to fill gaps in knowledge concerning toxicological events. Fundamental to this new paradigm is a greater understanding of the mechanisms of toxicity and, in particular, where these mechanisms may be conserved across taxa, such as between model animals and related wild species. This presents an opportunity to make predictions across diverse species, where empirical data are unlikely to become available as is the case for most species of wildlife.
Assuring Consumer Safety without Animal Testing: A Feasibility Case Study for Skin Sensitisation

Allergic Contact Dermatitis (ACD; chemical-induced skin sensitisation) represents a key consumer safety endpoint for the cosmetics industry. At present, animal tests (predominantly the mouse Local Lymph Node Assay) are used to generate skin sensitisation hazard data for use in consumer safety risk assessments. An animal testing ban on chemicals to be used in cosmetics will come into effect in the European Union (EU) from March 2009. This animal testing ban is also linked to an EU marketing ban on products containing any ingredients that have been subsequently tested in animals, from March 2009 or March 2013, depending on the toxicological endpoint of concern. Consequently, the testing of cosmetic ingredients in animals for their potential to induce skin sensitisation will be subject to an EU marketing ban, from March 2013 onwards. Our conceptual framework and strategy to deliver a non-animal approach to consumer safety risk assessment can be summarised as an evaluation of new technologies (e.g. ‘omics’, informatics), leading to the development of new non-animal (in silico and in vitro) predictive models for the generation and interpretation of new forms of hazard characterisation data, followed by the development of new risk assessment approaches to integrate these new forms of data and information in the context of human exposure. Following the principles of the conceptual framework, we have been investigating existing and developing new technologies, models and approaches, in order to explore the feasibility of delivering consumer safety risk assessment decisions in the absence of new animal data. We present here our progress in implementing this conceptual framework, with the skin sensitisation endpoint used as a case study.
Rapid prototyping and compact testing of CPU emulators

In this paper, we propose a novel rapid prototyping technique to produce a high quality CPU emulator at reduced development cost. Specification mining from published CPU manuals, automated code generation of both the emulator and its test vectors from the mined CPU specifications, and a hardware-oracle based test strategy all work together to close the gaps between specification analysis, development and testing. The hardware-oracle is a program which allows controlled execution of one or more instructions on the CPU, so that its outputs can be compared to that of the emulator. The hardware-oracle eliminates any guesswork about the true behavior of an actual CPU, and it helps in the identification of several discrepancies between the published specifications vs. the actual processor behavior, which would be very hard to detect otherwise.
Estimation error of the constrained lasso

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (n ≪ p) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be estimated is not exactly sparse (e.g., when it is weakly sparse), and shows explicitly the effect of overestimating the ℓ1-norm of the parameter to be estimated on the estimation performance. The results of this paper show that the constrained lasso is minimax optimal for estimating a parameter with bounded ℓ1-norm, and also for estimating a weakly sparse parameter if its ℓ1-norm is accessible.
Principal Component Analysis

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.
Composite Module Analyst: A Fitness-Based Tool for Prediction of Transcription Regulation

Functionally related genes involved in the same molecular-genetic, biochemical, or physiological process are often regulated coordinately Such regulation is provided by precisely organized binding of a multiplicity of special proteins (transcription factors) to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. Here we present a new approach for defining promoter models based on composition of transcription factor binding sites and their pairs. We utilize a multicomponent fitness function for selection of that promoter model fitting best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated data.
First Results from the Arecibo Galactic H I Survey: The Disk/Halo Interface Region in the Outer Galaxy

The consortium for Galactic studies with the Arecibo L-band Feed Array (ALFA) is conducting a neutral hydrogen (H I) survey of the whole Arecibo sky (declination range from -1° to 38°), with high angular (3.′5) and velocity resolution (0.2 km s-1). The precursor observations with ALFA of a region in the Galactic anticenter reveal numerous isolated, small (a few parsecs in size), and cold (Tk < 400 K) H I clouds at low negative velocities, distinctly separated from the H I disk emission ("low-velocity clouds" [LVCs]). These clouds are most likely located in the transition region between the Galactic disk and halo (at scale heights of 60-900 pc), yet they have properties of typical cold neutral clouds. LVCs are colder and, most likely, smaller and less massive than Lockman's clouds in the disk/halo interface region of the inner Galaxy. Our observations demonstrate that the cloudy structure of the interface region is most likely a general phenomenon, not restricted to the inner Galaxy. LVCs have sizes and radial velocities in agreement with the expectations for clouds formed in low-temperature fountain flows, although we measure a factor of 10 higher H I column densities. Alternatively, LVCs could represent the final stages of the infalling intergalactic material in the ongoing construction of the Galaxy. In the same data set at higher negative velocities, we have discovered a "companion" H I cloud located 50' southwest of HVC 186+19-114. HVC 186+19-114 is a typical compact high-velocity cloud (HVC) with a well-defined core/envelope structure. The companion cloud has a diameter of only 7′ × 9′ and is one of the smallest HVCs known, most likely stripped from the main cloud through the interactions with the halo medium.
Decentralized Online Big Data Classification - a Bandit Framework

Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We assume that the classification functions available to each processing element are fixed, but their prediction accuracy for various types of incoming data are unknown and can change dynamically over time, and thus they need to be learned online. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop distributed online learning algorithms for which we can prove that they have sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithms.
Beamforming Design in Relay-Based Full-Duplex MISO Wireless Powered Communication Networks

In this letter, we jointly design the beamformers of relay and source in full-duplex relay-based wireless powered communication networks. Equipped with multiple antennas, the relay/source is designed to transmit information and receive information/energy simultaneously. Furthermore, the hybrid access point (H-AP) acts as relay to assist the information transfer from source to destination. Moreover, source transmits signals only with the energy harvested from H-AP/relay and recycled energy from its own transmitted signals. In this setup, we propose a novel one-phase co-channel protocol and jointly design the beamforming of relay and source to maximize the system throughput. As the problem is non-convex, we first use zero forcing (ZF) method to cancel the self-interference at relay and then propose a low-complexity algorithm to obtain the global optimum when the ZF constraint is assumed. Numerical results show that our proposed optimal beamforming design is superior to other reference schemes.
Intrinsic horizontal connections process global tactile features in the primary somatosensory cortex: Neuroanatomical evidence

To understand manual tactile functions in primates, it is essential to explore the interactions between the finger pad representations in somatosensory cortex. To this end, we used optical imaging and electrophysiological mapping to guide neuroanatomical tracer injections into distal digit tip representations of Brodmann area 3b in the squirrel monkey. Retrogradely labeled cell densities and anterogradely labeled fibers and terminal patches in somatosensory areas were plotted and quantified with respect to tangential distribution. Within area 3b, reciprocal patchy distribution of anterograde and retrograde labeling spanned the representation of the distal pad of multiple digits, indicating strong cross‐digit connectivity. Inter‐areal connections revealed bundles of long‐range fibers projecting anteroposteriorly, connecting area 3b with clusters of labeled neurons and terminal axon arborizations in area 1. Inter‐areal linkage appeared to be largely confined to the representation of the injected finger. These findings provide the neuroanatomical basis for the interaction between distal finger pad representations observed by recent electrophysiological studies. We propose that intra‐areal connectivity may be heavily involved in interdigit integration such as shape discrimination, whereas long‐range inter‐areal connections may subserve active touch in a digit‐specific manner. J. Comp. Neurol. 521:2798–2817, 2013. © 2013 Wiley Periodicals, Inc.
The Identifiability of Tree Topology for Phylogenetic Models, Including Covarion and Mixture Models

For a model of molecular evolution to be useful for phylogenetic inference, the topology of evolutionary trees must be identifiable. That is, from a joint distribution the model predicts, it must be possible to recover the tree parameter. We establish tree identifiability for a number of phylogenetic models, including a covarion model and a variety of mixture models with a limited number of classes. The proof is based on the introduction of a more general model, allowing more states at internal nodes of the tree than at leaves, and the study of the algebraic variety formed by the joint distributions to which it gives rise. Tree identifiability is first established for this general model through the use of certain phylogenetic invariants.
Event Detection: Monitoring and Tracking the Dynamics of Social Networks Communities

This paper presents research done towards a robust real-time social network text stream event detection system that combines text stream mining and network analysis methods. It presents the current state-of-the-art systems, algorithms, and methodologies to perform event detection in streaming environments: If from the point of view of a natural language processing, text mining, and unsupervised learning the problem of detecting events in unbounded text streams is hard, dealing with dynamic networks with millions of nodes and edges is not also an easy task. Presented contributions and research directions are based on the premise that the precision and accuracy of an event detection algorithm could be improved by considering network properties of the social network when events happen. Network analysis algorithms, specifically algorithms to perform dynamic community detection, community identification and community tracking can be used to extract knowledge from users relations and interactions helping in the task of unveiling and detecting new or unforeseen events.