Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-channel signal detection and speech enhancement based on the transient beam-to-reference ratio

In reverberant and noisy environments, multichannel systems are designed for spatially filtering interfering signals coming from undesired directions. In case of incoherent or diffuse noise fields, beamforming alone does not provide sufficient noise reduction, and post-filtering is normally required. In this paper, we present a two-channel post-filtering approach for signal detection and speech enhancement. A mild assumption is made, that a desired signal component is stronger at the beamformer output than at the reference noise signal, and a noise component is stronger at the reference signal. The ratio between the transient power at the beamformer output and the transient power at the reference noise signal is used for indicating whether such a transient is desired or interfering. Experimental results demonstrate the usefulness of the proposed approach in a car environment.
Functional family therapy for adolescent behavior problems.

Adolescents with disruptive behaviour problems represent one of the most difficult and recalcitrant treatment populations. These youth and their parents often enter treatment unwilling or unable to try new behaviours. Family interactions are laced with blame, hostility, hopelessness, and rejection, all of which create a context that interferes with adaptive change. This book explains how to provide Functional Family Therapy (FFT), an empirically supported, highly successful family intervention for delinquent and substance-using adolescents. FFT systematically alters important risk and protective factors associated with the problem behaviours. The FFT therapist first increases family members' motivation to change by disrupting their dysfunctional attributions about themselves and each other and replacing them with more positive and adaptive perceptions. The therapist then works with the family to eliminate the problem behaviours and generalise new skills and interaction patterns to broader community relations. Robust yet flexible, the FFT model has been replicated successfully in juvenile justice, mental health, and child welfare settings, as well as traditional substance treatment and school-based programs. Given FFT's high success rate and ability to fit an array of settings, this book is ideal for all therapists and administrators who work with troubled youth and their families. Readers will learn the therapy goals and techniques for each phase of FFT, as well as recommendations for supervision and administration.
Angle-Domain Frequency-Selective Sparse Channel Estimation for Underwater MIMO-OFDM Systems

In this letter we propose a fully angle-domain frequency-selective sparse channel estimation algorithm for multiple-input multiple-output orthogonal frequency division multiplexing underwater acoustic communication (MIMO-OFDM UAC) systems. The algorithm is divided into two stages (finding nonzero tap positions and finding nonzero angle-domain coefficients) to reduce the computational complexity. It has been demonstrated that the 2-stage orthogonal matching pursuits algorithm provides an efficient way to estimate the under-determined frequency-selective sparse channel with reduced complexity for MIMO-OFDM UAC systems.
Microfluidic-Based Multi-Organ Platforms for Drug Discovery

Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC) model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.
CAN A DIAGNOSTIC TEST PREDICT PERFORMANCE IN NUMERACY ASSESSMENTS IN PHARMACY STUDENTS

A diagnostic test comprising questions ranging from simple arithmetic to calculations of concentration was presented to a cohort of first year pharmacy students in the first week of term. Each student was assigned to a group (red, amber or green) based upon their performance (39% or less, 40-69% and 70% or better, respectively) and was informed of their group via their academic tutors. Most students (67%) were assigned to the amber group. Following a lecture series in pharmaceutical calculations, the students’ performance was then monitored in an in-course assessment and an MCQ examination. Our data show that including calculations in the curriculum improved performance in both assessments. The majority (70%) of students assigned to the green group remained there in the in-course assessment and all but one passed the MCQ at the first attempt. Of the 15 students in the red group, five remained in this group following the in-course assessment and four of these failed the MCQ at the first attempt. There was significantly more movement, both up and down, in the amber group. We also monitored student access to calculations support resources on our e-learning system but found little difference between any of the groups. In all, the diagnostic test proved to be useful in identifying the weakest and most capable students and predicting their subsequent performance.
Online discriminative dictionary learning via label information for multi task object tracking

In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a compact and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multi-classifier simultaneously. Combined with multi task sparse learning, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between multi task sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy and robustness.
Mining in its application to

Standard predictive data mining techniques operate on the implicit assumption of random sampling, but data bases seldomly contain random samples from the population of interest. This is not surprising, considering company data bases are primarily maintained to support vital business processes, rather than for the purpose of analysis. The bias present in many data bases poses a major threat to the validity of data mining results. ~Ve focus on a form of selectivity bias that occurs frequently in applications of data mining to scoring. Our approach is illustrated on a credit data base of a large Dutch bank, containing financial data of companies that applied for a loan, as well as a class label indicating the repayment behavior of accepted applicants. With respect to the missing class labels of the rejected applicants, we argue that the missing at rondom (MAR) case becomes increasingly important, because many banks nowadays use formal selection models. The classification problem is modeled with mixture distributions, using likelihood-based inference via the EM-algorithm. Since the distribution of financial ratios is notably non-normal, class-conditional densities are modeled by mixtures of normal components as well. The analysis shows that mixtures of two normal components usually give a satisfactory fit to the withinclass empirical distribution of the ratios. The results of our comparative study show the selectivity bias caused by ignoring the rejects in the learning process.
Potential Stream Density in Mid-Atlantic U.S. Watersheds

Stream network density exerts a strong influence on ecohydrologic processes in watersheds, yet existing stream maps fail to capture most headwater streams and therefore underestimate stream density. Furthermore, discrepancies between mapped and actual stream length vary between watersheds, confounding efforts to understand the impacts of land use on stream ecosystems. Here we report on research that predicts stream presence from coupled field observations of headwater stream channels and terrain variables that were calculated both locally and as an average across the watershed upstream of any location on the landscape. Our approach used maximum entropy modeling (MaxEnt), a robust method commonly implemented to model species distributions that requires information only on the presence of the entity of interest. In validation, the method correctly predicts the presence of 86% of all 10-m stream segments and errors are low (<1%) for catchments larger than 10 ha. We apply this model to the entire Potomac River watershed (37,800 km2) and several adjacent watersheds to map stream density and compare our results with the National Hydrography Dataset (NHD). We find that NHD underestimates stream density by up to 250%, with errors being greatest in the densely urbanized cities of Washington, DC and Baltimore, MD and in regions where the NHD has never been updated from its original, coarse-grain mapping. This work is the most ambitious attempt yet to map stream networks over a large region and will have lasting implications for modeling and conservation efforts.
Modeling Haplotype-Haplotype Interactions in Case-Control Genetic Association Studies

Haplotype analysis has been increasingly used to study the genetic basis of human diseases, but models for characterizing genetic interactions between haplotypes from different chromosomal regions have not been well developed in the current literature. In this article, we describe a statistical model for testing haplotype-haplotype interactions for human diseases with a case-control genetic association design. The model is formulated on a contingency table in which cases and controls are typed for the same set of molecular markers. By integrating well-established quantitative genetic principles, the model is equipped with a capacity to characterize physiologically meaningful epistasis arising from interactions between haplotypes from different chromosomal regions. The model allows the partition of epistasis into different components due to additive × additive, additive × dominance, dominance × additive, and dominance × dominance interactions. We derive the EM algorithm to estimate and test the effects of each of these components on differences in the pattern of genetic variation between cases and controls and, therefore, examine their role in the pathogenesis of human diseases. The method was further extended to investigate gene-environment interactions expressed at the haplotype level. The statistical properties of the models were investigated through simulation studies and its usefulness and utilization validated by analyzing the genetic association of sarcoidosis from a human genetics project.
Advances in the biomedical applications of the EELA Project

In the last years an increasing demand for Grid Infrastructures has resulted in several international collaborations. This is the case of the EELA Project, which has brought together collaborating groups of Latin America and Europe. One year ago we presented this e-infrastructure used, among others, by the biomedical groups for the studies of oncological analysis, neglected diseases, sequence alignments and computational phylogenetics. After this period, the achieved advances are summarised in this paper.
On probe-response attacks in Collaborative Intrusion Detection Systems

Cyber-attacks are steadily increasing in both their size and sophistication. To cope with this, Intrusion Detection Systems (IDSs) are considered mandatory for the protection of critical infrastructure. Furthermore, research is currently focusing on collaborative architectures for IDSs, creating a Collaborative IDS (CIDS). In such a system a number of IDS monitors work together towards creating a holistic picture of the monitored network. Nevertheless, a class of attacks exists, called probe-response, which can assist adversaries to detect the network position of CIDS monitors. This can significantly affect the advantages of a CIDS. In this paper, we introduce PREPARE, a framework for deploying probe-response attacks and also for studying methods for their mitigation. Moreover, we present significant improvements on both the effectiveness of probe-response attacks as well as on mitigation techniques for detecting them. We evaluate our approach via an extensive simulation and a real-world attack deployment that targets two CIDSs. Our results show that our framework can be practically utilized, that our proposals significantly improve probe-response attacks and, lastly, that the introduced detection and mitigation techniques are effective.
Astrocyte-Neuron Lactate Transport IsRequiredforLong-TermMemoryFormation

We report that, in the rat hippocampus, learningleads to a signiﬁcant increase in extracellular lactatelevels that derive from glycogen, an energy reserveselectively localized in astrocytes. Astrocyticglycogen breakdown and lactate release areessential for long-term but not short-term memoryformation, and for the maintenance of long-termpotentiation (LTP) of synaptic strength elicitedin vivo. Disrupting the expression of the astrocyticlactate transporters monocarboxylate transporter 4(MCT4) or MCT1 causes amnesia, which, like LTPimpairment, is rescued by L-lactate but not equica-loric glucose. Disrupting the expression of theneuronal lactate transporter MCT2 also leads toamnesia that is unaffected by either L-lactate orglucose, suggesting that lactate import into neuronsis necessary for long-term memory. Glycogenolysisand astrocytic lactate transporters are also criticalfor the induction of molecular changes required formemory formation, including the induction of phos-pho-CREB, Arc, and phospho-coﬁlin. We concludethat astrocyte-neuron lactate transport is requiredfor long-term memory formation.
Corroboration of mechanobiological simulations of tissue differentiation in an in vivo bone chamber using a lattice‐modeling approach

It is well established that the mechanical environment modulates tissue differentiation, and a number of mechanoregulatory theories for describing the process have been proposed. In this study, simulations of an in vivo bone chamber experiment were performed that allowed direct comparison with experimental data. A mechanoregulation theory for mesenchymal stem cell differentiation based on a combination of fluid flow and shear strain (computed using finite element analysis) was implemented to predict tissue differentiation inside mechanically controlled bone chambers inserted into rat tibae. To simulate cell activity, a lattice approach with stochastic cell migration, proliferation, and selected differentiation was adopted; because of its stochastic nature, each run of the simulation gave a somewhat different result. Simulations predicted the load‐dependency of the tissue differentiation inside the chamber and a qualitative agreement with histological data; however, the full variability found between specimens in the experiment could not be predicted by the mechanoregulation algorithm. This result raises the question whether tissue differentiation predictions can be linked to genetic variability in animal populations. © 2009 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 27:1659–1666, 2009
Error control for wireless progressive video transmission

We present and compare several video transmission systems with various feedback and coding strategies for wireless real-time transmission. A channel coding system based on high-memory convolutional codes (CCs) is deployed which aims at delaying the first error within a progressively coded data frame as far as possible. A low-rate feedback channel is introduced to communicate the number of error-free received bits to the transmitter or to request additional redundancy. Thus, drift problems are avoided, since video encoder and decoder predict from identical reference frames. Several video communication systems based on forward error correction (FEC) and automatic repeat request (ARQ) methods are presented and compared. Experimental results are provided. The benefits of the proposed combination of unequal error protection (UEP) or ARQ systems with progressively coded sources and network feedback are highlighted.
What is the long term safety of a negative capsule endoscopy in patients with obscure gastrointestinal bleeding?

BACKGROUND AND AIMS There are contradictory findings regarding long-term outcome in patients with obscure gastrointestinal bleeding and negative capsule endoscopy. Factors associated with rebleeding after a negative videocapsule are not entirely known. OBJECTIVE The aim of this study was to compare the rebleeding rate between negative and positive capsule endoscopy patients and to identify predictive factors for rebleeding in patients with negative findings. MATERIAL AND METHODS Consecutive patients with obscure gastrointestinal bleeding referred to a single center over a period of 5 years were identified. After exclusion of patients with a follow time < 6 months, 173 patients were included. Clinical information was retrospectively collected from medical records. Rebleeding was defined as evidence of melena/hematochezia, a drop in hemoglobin of ≥ 2 g/dL, or the need for transfusion 30 days after the index episode. RESULTS The mean age was 61.7 years and 60% were female. The median follow up time was 27 months. Most patients were referred for occult gastrointestinal bleeding (67.1%) while 32.9% were referred for overt bleeding. More than 50% of the patients had negative capsule endoscopy. The rebleeding rate in negative capsule endoscopy is 16%, with a mean follow-up time of 25.8 months and is significantly lower than positive capsule endoscopy (16% vs. 30.2%, p = 0.02). Rebleeding after negative capsule endoscopy is higher in patients who need more transfusions of packet red blood cells before capsule endoscopy (3.0 vs. 0.9, p = 0.024) and have overt bleeding (46% vs. 13.9%, p = 0.03). In 53% of these patients, rebleeding occurs > 12 months after a negative capsule endoscopy. CONCLUSIONS Patients with obscure gastrointestinal bleeding and a negative capsule endoscopy had a significantly lower rebleeding rate and can be safely followed. However, a higher transfusion of red blood cells previous to capsule endoscopy and an overt bleeding are associated with a higher rebleeding. So, it is reasonable to consider that these patients may benefit of at least one year of follow-up.
Kostant Convexity for affine buildings

We prove an analogue of Kostants convexity theorem for thick affine buildings and give an application for groups with affine BN-pair. Recall that there are two natural retractions of the affine building onto a fixed apartment A: The retraction r centered at an alcove in A and the retraction $\rho$ centered at a chamber in the spherical building at infinity. We prove that for each special vertex x in A the set $\rho(r^{-1}(W.x))$ is a certain convex hull of W.x. The proof can be reduced to a statement about Coxeter complexes and heavily relies on a character formula for highest weight representations of algebraic groups.
Gender Differences in Rapid Eye Movement-Related Sleep Disordered Breathing

Sleep disordered breathing (SDB) is an independent risk factor for cardiovascular disease. It is known to be associated more frequently with men than women, particularly in the premenopausal age range. The goal of this study is to evaluate gender differences among Korean patients diagnosed with SBD. This study included 309 patients who visited our Sleep Clinic due to sleep-related symptoms and were diagnosed with SDB by overnight polysomnography (PSG). We analyzed age, gender, body mass index, various PSG indices including sleep stages, apnea-hypopnea index (AHI) and AHI ratio in rapid eye movement (REM) versus non-REM (NREM) sleep stages (R:N ratio). Of those 309 patients diagnosed with obstructive sleep apnea, 217 (70.2%) were men (mean age 51.05 ± 12.64 years) and 92 (29.8%) were women (mean age 64.53 ± 10.43 years). The mean AHI during total sleep time was 30.34 ± 21.17 in men and 21.47 ± 17.14 in women (P 60 years old. These findings suggest the possibility of different pathophysiologic mechanisms of SDB between genders and also between NREM versus REM sleep, which can be partly explained by the influence of female sex hormones.
The mechanics of the temporomandibular joint--a theoretical and electromyographical study.

summary A theoretical analysis of the force actions at the temporomandibular joint is presented. The equations of equilibrium of the mandible under a static load are derived and the solutions yielding the minimum joint force are explored. Using integrated electromyography, an estimate is presented of the forces at the joint with the mandible supporting a static load on the incisor and canine teeth. The conclusion reached is that the joint is load bearing during function.
Robustness of Link-prediction Algorithm Based on Similarity and Application to Biological Networks

Many algorithms have been proposed to predict missing links in a variety of real networks. These studies focus on mainly both accuracy and efficiency of these algorithms. However, little attention is paid to their robustness against either noise or irrationality of a link existing in almost all of real networks. In this paper, we investigate the robustness of several typical node-similarity-based algorithms and find that these algorithms are sensitive to the strength of noise. Moreover, we find that it also depends on networks' structure properties, especially on network efficiency, clustering coefficient and average degree. In addition, we make an attempt to enhance the robustness by using link weighting method to transform un-weighted network to weighted one and then make use of weights of links to characterize their reliability. The result shows that proper link weighting scheme can enhance both robustness and accuracy of these algorithms significantly in biological networks while it brings little computational effort.
Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression

Public engagement (PE) is habitually recognized and advocated across the higher education (HE) community – especially by regulator and funder constituencies – as an intrinsically good thing. In the UK, a number of initiatives focused on embedding a culture of PE within universities have sought to further this claim, yet have done so without considering or reporting upon some of the less positive elements of its undertaking. In this paper, we report upon evidence from interviews with n = 40 UK academics, drawn from across the academic hierarchy, disciplines and a diverse range of higher education institutions. The testimony of respondents points towards a number of issues for public engagement in higher education (PE-HE), specifically the deleterious effects of its undertaking on academic identity; research practice and career progression.