Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech enhancement based on minimum mean-square error estimation and supergaussian priors

This paper presents a class of minimum mean-square error (MMSE) estimators for enhancing short-time spectral coefficients of a noisy speech signal. In contrast to most of the presently used methods, we do not assume that the spectral coefficients of the noise or of the clean speech signal obey a (complex) Gaussian probability density. We derive analytical solutions to the problem of estimating discrete Fourier transform (DFT) coefficients in the MMSE sense when the prior probability density function of the clean speech DFT coefficients can be modeled by a complex Laplace or by a complex bilateral Gamma density. The probability density function of the noise DFT coefficients may be modeled either by a complex Gaussian or by a complex Laplacian density. Compared to algorithms based on the Gaussian assumption, such as the Wiener filter or the Ephraim and Malah (1984) MMSE short-time spectral amplitude estimator, the estimators based on these supergaussian densities deliver an improved signal-to-noise ratio.
Display of whole proteins on inner and outer surfaces of grapevine fanleaf virus‐like particles

Summary Virus‐like particles (VLPs) derived from nonenveloped viruses result from the self‐assembly of capsid proteins (CPs). They generally show similar structural features to viral particles but are noninfectious and their inner cavity and outer surface can potentially be adapted to serve as nanocarriers of great biotechnological interest. While a VLP outer surface is generally amenable to chemical or genetic modifications, encaging a cargo within particles can be more complex and is often limited to small molecules or peptides. Examples where both inner cavity and outer surface have been used to simultaneously encapsulate and expose entire proteins remain scarce. Here, we describe the production of spherical VLPs exposing fluorescent proteins at either their outer surface or inner cavity as a result of the self‐assembly of a single genetically modified viral structural protein, the CP of grapevine fanleaf virus (GFLV). We found that the N‐ and C‐terminal ends of the GFLV CP allow the genetic fusion of proteins as large as 27 kDa and the plant‐based production of nucleic acid‐free VLPs. Remarkably, expression of N‐ or C‐terminal CP fusions resulted in the production of VLPs with recombinant proteins exposed to either the inner cavity or the outer surface, respectively, while coexpression of both fusion proteins led to the formation hybrid VLP, although rather inefficiently. Such properties are rather unique for a single viral structural protein and open new potential avenues for the design of safe and versatile nanocarriers, particularly for the targeted delivery of bioactive molecules.
Perturbing gauge/gravity duals by a Romans mass

We show how to produce algorithmically gravity solutions in massive IIA (as infinitesimal first-order perturbations in the Romans mass parameter) dual to assigned conformal field theories. We illustrate the procedure on a family of Chern–Simons-matter conformal field theories that we recently obtained from the theory by waiving the condition that the levels sum up to zero.
Yeast “Make-Accumulate-Consume” Life Strategy Evolved as a Multi-Step Process That Predates the Whole Genome Duplication

When fruits ripen, microbial communities start a fierce competition for the freely available fruit sugars. Three yeast lineages, including baker’s yeast Saccharomyces cerevisiae, have independently developed the metabolic activity to convert simple sugars into ethanol even under fully aerobic conditions. This fermentation capacity, named Crabtree effect, reduces the cell-biomass production but provides in nature a tool to out-compete other microorganisms. Here, we analyzed over forty Saccharomycetaceae yeasts, covering over 200 million years of the evolutionary history, for their carbon metabolism. The experiments were done under strictly controlled and uniform conditions, which has not been done before. We show that the origin of Crabtree effect in Saccharomycetaceae predates the whole genome duplication and became a settled metabolic trait after the split of the S. cerevisiae and Kluyveromyces lineages, and coincided with the origin of modern fruit bearing plants. Our results suggest that ethanol fermentation evolved progressively, involving several successive molecular events that have gradually remodeled the yeast carbon metabolism. While some of the final evolutionary events, like gene duplications of glucose transporters and glycolytic enzymes, have been deduced, the earliest molecular events initiating Crabtree effect are still to be determined.
Speaker dependent emotion recognition using speech signals

This paper examines three algorithms to recognize speaker’s emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likelihood Bayes), NN(Nearest Neighbor) and HMM(Hidden Markov Model) algorithms are used as the pattern matching techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB and NN are composed of pitch mean, pitch standard deviation, energy mean, energy standard deviation, etc. For HMM, vectors of delta pitch with delta-delta pitch and delta energy with delta-delta energy are used. A corpus of emotional speech data was recorded and the subjective evaluation of the data was performed by 23 untrained listeners. The subjective recognition result was 56% and was compared with the classifiers’ recognition rates. MLB, NN, and HMM classifiers achieved recognition rates of 68.9%, 69.3%, and 89.1%, respectively, for the speaker dependent and context-independent classification.
Social behaviour, socio-cognitive skills and attachment style in school-aged children: what is the relation with academic outcomes?

ABSTRACT The present research explored the relation between socio-cognitive skills, as Theory of Mind and affective empathy, social behaviour, attachment style and scholastic success in children, aged from 8 to 11 years (N = 159; 90 females, 69 males; Mage = 9,60; DS = .78). Several assessment tools were administered to children on mentalizing abilities (Stories), empathy (FASTE), language (PPVT-R), attachment style (SAT) and learning (M.T. and AC-MT 6-11), while the teachers were asked to complete a questionnaire about every child’s social behaviour (SDQ). The correlational analysis and MANOVA prove that ‘secure’ children show more prosocial behaviour, moral mentalizing and empathic capacities in the classroom, receiving better evaluations from teachers and in learning tests. Moreover, socio-cognitive skills and prosocial behaviour are associated with academic performance, while the affective empathy is only related to teachers’ evaluations. Finally, the implications of these findings for theoretical and research development on socio-cognitive skills are discussed.
On the interaction of TCP and Routing Protocols in MANETs

Recent research in mobile ad hoc networks (MANETs) has highlighted the detrimental effect of multiple retransmission timeouts (RTOs) on TCP throughput. However, the effect of the routing protocol’s buffering strategy on this phenomenon has not been explicitly demonstrated or studied. In this paper, we present and analyse through simulations using functional testbed routing agents, TCP throughput performance during a route break event. The results of our analysis on three popular routing protocols, namely AODV, DSR and OLSR, produces insight into the different behavioural patterns of TCP during this event, and highlights the mechanisms of each routing protocol that affect it. Further, trade-offs in the choice of the routing parameters with respect to TCP performance are discussed.
Linguistic Evaluation in the Classification in Portuguese Texts

This paper evaluates the performance of support vector machines, Naive Bayes, and neural networks as classifiers for the categorization of Portuguese texts. We present several experiments with two different corpora with different feature selection strategies. We consider the use of linguistic information in the definition of grammatical groups. A comparison of classifiers is presented and the error margins show excellent results when using a specific feature selection in association with the right classifier.
Impact of Realistic Simulation on the Evaluation of Mobile Ad Hoc Routing Protocols

Today's advanced simulators facilitate thorough studies on vehicular ad hoc networks (VANETs). However, the choice of the physical layer and the mobility models in such simulators is a crucial issue that greatly impacts the results. Realistic simulation of routing protocols in VANETs is still an open question. Indeed, only a few works address routing protocols comparison performed under realistic conditions. This paper compares common reactive, proactive, hybrid, and geographic routing protocols using a simulation platform integrating a realistic physical layer and mobility models. It also presents and analyzes several reactive protocol enhancement propositions dedicated to the VANETs context, such as multipath routing, but also protocols tuning, which allows it to adapt faster. They all have lot of attention and are typically proposed to increase the reliability of data transmission. This paper studies the behavior of each protocol in different situations and analyzed their advantages and drawbacks. Results presented in this paper give an important explanation on the contradictory results found in similar works. Finally, our realistic simulations show that reactive protocols are the best suited for VANETs, and more especially the dynamic Mobile Adhoc NETwork on-demand protocol.
The Protein Secondary Structure Flexibility

Abstract - Modeling protein flexibility is a long standing challenge in computational biology with a special impact to protein docking. Relating problems are protein structures alignment and identification of flexible and rigid protein regions, as well as a general definition of a region degree of flexibility. Determination of protein flexibility from a single protein conformation, so called apriori approach is still computationally demanding and quite unreliable. As a part of our research on finding an accurate procedure for predicting flexible and rigid protein domains and in order to learn as much as possible about protein flexibility we developed a fast and simple aposteriori method (method based on comparison of different conformations of the same protein) for characterization of protein regions regarding their flexibility. We applied it to the test set consisting of 220 proteins in which we identified about 6500 on the secondary structure based regions and classified them regarding their flexibility. We investigated possible correlations between the protein flexibility and different 3D structural parameters and found that a region flexibility correlate with population of certain amino acid residues, the protein solvent accessible surface area and B-factors. The set of protein regions characterized by flexibility and the results obtained should be valuable in testing the new apriori procedures, but also the procedure itself can be used in discrimination the regions in proteins by flexibility.
Inductance and Capacitance Modeling of RF Board and High Speed Package Interconnects based on Planar EM Simulation

This paper describes a methodology to simulate the distributed inductive and capacitive effects introduced by the interconnects on RF board circuits and High Speed packages. This methodology is based on an equivalent RLC model, derived directly from the discretization of the planar electromagnetic field equations. We discuss the relation of the voltage and the current in the equivalent network with the electric and magnetic fields in the physical circuit. We also present a new technology based on mesh reduction to eliminate redundancy and hence reduce the order of the equivalent RLC network. This new technology comprises a generalization of the basis functions used in the disretization of the surface currents. The generalized basis functions allow for a more efficient meshing of complex geometrical structures in terms of polygonal shaped cells. The increased simulation performance is demonstrated in the examples. We present numerical simulation results for two real-life examples: an RF board interconnect layout and a high-density ball-grid array package.
Multiple sub-row buffers in DRAM: unlocking performance and energy improvement opportunities

The twin demands of energy-efficiency and higher performance on DRAM are highly emphasized in multicore architectures. A variety of schemes have been proposed to address either the latency or the energy consumption of DRAMs. These schemes typically require non-trivial hardware changes and end up improving latency at the cost of energy or vice-versa. One specific DRAM performance problem in multicores is that interleaved accesses from different cores can potentially degrade row-buffer locality. In this paper, based on the temporal and spatial locality characteristics of memory accesses, we propose a reorganization of the existing single large row-buffer in a DRAM bank into multiple sub-row buffers (MSRB). This re-organization not only improves row hit rates, and hence the average memory latency, but also brings down the energy consumed by the DRAM. The first major contribution of this work is proposing such a reorganization without requiring any significant changes to the existing widely accepted DRAM specifications. Our proposed reorganization improves weighted speedup by 35.8%, 14.5% and 21.6% in quad, eight and sixteen core workloads along with a 42%, 28% and 31% reduction in DRAM energy. The proposed MSRB organization enables opportunities for the management of multiple row-buffers at the memory controller level. As the memory controller is aware of the behaviour of individual cores it allows us to implement coordinated buffer allocation schemes for different cores that take into account program behaviour. We demonstrate two such schemes, namely Fairness Oriented Allocation and Performance Oriented Allocation, which show the flexibility that memory controllers can now exploit in our MSRB organization to improve overall performance and/or fairness. Further, the MSRB organization enables additional opportunities for DRAM intra-bank parallelism and selective early precharging of the LRU row-buffer to further improve memory access latencies. These two optimizations together provide an additional 5.9% performance improvement.
Normal-branch quasi-periodic oscillations during the high-intensity state of Cygnus X-2

Using data obtained with the Rossi X-ray Timing Explorer, we report the detection of a 5-Hz quasi-periodic oscillation (QPO) in the bright low-mass X-ray binary and Z source Cygnus X-2 during high overall intensities (the high-intensity state). This QPO was detected on the so-called normal-branch and can be identified with the normal-branch QPO or NBO. Our detection of the NBO is the first one during times when Cygnus X-2 was in the high-intensity state. The rms amplitude of this QPO decreased from 2.8 per cent between 2 and 3.1 keV to <1.9 per cent between 5.0 and 6.5 keV. Above 6.5 keV, its amplitude rapidly increased to ∼12 per cent rms above 16 keV. The time lags of the QPO were consistent with being zero below 5 keV (compared with the 2–3.1 keV band), but they rapidly increased to ∼70 ms (140°) around 10 keV, above which the time lags remained approximately constant near 70 ms. The photon energy dependences of the rms amplitude and the time lags are very similar to those observed for the NBO with other satellites (Ginga, EXOSAT) at different (i.e. lower) intensity states.
A secure multi-tier authentication scheme in cloud computing environment

Day by day users has been adopting remote storage system due to its secure service. But some time it may possible that people comes to know what security policy has been applied in server side. As the result, new security risks and attacks are coming in cloud. So, it is necessary to provide more secured and updated authentication scheme. For any cloud application, in which personal or private information are exchanged, single-tier authentication is not sufficient for authentication. In above situation, multi-tier authentication scheme is much more secured than single-tier authentication scheme. In computing environment, there are various multitier authentication schemes, but they do not provide security against insider attacks and virtualization attacks. In cloud environment, whole authentication control lies in the server side. So, it is hard to trust the third party server in cloud system. This paper proposes a secured and more advanced multi-tier authentication scheme for accessing cloud services.
The psi-cube: a bus-based cube-type network for high-performance on-chip systems

This paper proposes a bus-based cube-type network, called psi-cube, to cope with the two problems, long wires and a limited number of I/O pins, against high-performance on-chip systems. We alleviate the long-wire problem with the small diameter, [n/2], of the 2/sup n/-node psi-cube, that is organized on the sets of node-partitions produced based on the n-bit Hamming code (Takesue, 1999) if we connect the nodes in each partition to a single bus. We mitigate the pin-neck problem through dynamic clusters. For each off-chip target such as a memory block, we dynamically produce a set of clusters of the nodes requesting for the target. The traffic to the target reduces in the same way as with the static clusters that are fixed in hardware. We lay out the psi-cube to make the bus length as short as possible and formalize the length; it is O(2/sup p-1/) or O(2/sup k-1/), where p and k are the sizes of parity and information parts of n-bit addresses. From the results of preliminary evaluation, the psi-cube outperforms over the mesh, assuming a highway bus of which delay is less that 4 times the delay of the mesh link.
Suppression of tumour-specific CD4+ T cells by regulatory T cells is associated with progression of human colorectal cancer

Background There is indirect evidence that T cell responses can control the metastatic spread of colorectal cancer (CRC). However, an enrichment of CD4+Foxp3+ regulatory T cells (Tregs) has also been documented. Objective To evaluate whether CRC promotes Treg activity and how this influences anti-tumour immune responses and disease progression. Methods A longitudinal study of Treg activity on a cohort of patients was performed before and after tumour resection. Specific CD4+ T cell responses were also measured to the tumour associated antigens carcinoembryonic antigen (CEA) and 5T4. Results Tregs from 62 preoperative CRC patients expressed a highly significant increase in levels of Foxp3 compared to healthy age-matched controls (p=0.007), which returned to normal after surgery (p=0.0075). CD4+ T cell responses to one or both of the tumour associated antigens, CEA and 5T4, were observed in approximately two-thirds of patients and one third of these responses were suppressed by Tregs. Strikingly, in all patients with tumour recurrence at 12 months, significant preoperative suppression was observed of tumour-specific (p=0.003) but not control CD4+ T cell responses. Conclusion These findings demonstrate that the presence of CRC drives the activity of Tregs and accompanying suppression of CD4+ T cell responses to tumour-associated antigens. Suppression is associated with recurrence of tumour at 12 months, implying that Tregs contribute to disease progression. These findings offer a rationale for the manipulation of Tregs for therapeutic intervention.
S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

With numerous new quantum chemistry methods being developed in recent years and the promise of even more new methods to be developed in the near future, it is clearly critical that highly accurate, well-balanced, reference data for many different atomic and molecular properties be available for the parametrization and validation of these methods. One area of research that is of particular importance in many areas of chemistry, biology, and material science is the study of noncovalent interactions. Because these interactions are often strongly influenced by correlation effects, it is necessary to use computationally expensive high-order wave function methods to describe them accurately. Here, we present a large new database of interaction energies calculated using an accurate CCSD(T)/CBS scheme. Data are presented for 66 molecular complexes, at their reference equilibrium geometries and at 8 points systematically exploring their dissociation curves; in total, the database contains 594 points: 66 at equilibrium geometries, and 528 in dissociation curves. The data set is designed to cover the most common types of noncovalent interactions in biomolecules, while keeping a balanced representation of dispersion and electrostatic contributions. The data set is therefore well suited for testing and development of methods applicable to bioorganic systems. In addition to the benchmark CCSD(T) results, we also provide decompositions of the interaction energies by means of DFT-SAPT calculations. The data set was used to test several correlated QM methods, including those parametrized specifically for noncovalent interactions. Among these, the SCS-MI-CCSD method outperforms all other tested methods, with a root-mean-square error of 0.08 kcal/mol for the S66 data set.
Semantic Classifier for Affective Computing

One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, support vector machines, linear discriminant analysis, neural networks or k-nearest neighbors, which classify emotions using several features obtained from facial expressions, body gestures, speech or different physiological signals. In this paper, we propose a semantic classifier as a new, simple and efficient approach to the problem of automatic emotion recognition. The implementation of the semantic classifier is based on the basic, natural principles used to decrease the complexity of problems found in n-dimensional spaces: discretization, structure identification and semantic optimization. The proposed classifier exhibits some self-organizing features and supports learning by repetition, generalization and specialization. It will be used to implement a distributed and robust system for emotion recognition.