Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of Sparce Time Relative Auditory Codes for Music

Many if not most audio features used in MIR research are inspired by work done in speech recognition and are variations on the spectrogram. Recently, much attention has been given to new representations of audio that are sparse and time-relative. These representations are efficient and able to avoid the time-frequency trade-off of a spectrogram. Yet little work with music streams has been conducted and these features remain mostly unused in the MIR community. In this paper we further explore the use of these features for musical signals. In particular, we investigate their use on realistic music examples (i.e. released commercial music) and their use as input features for supervised learning. Furthermore, we identify three specific issues related to these features which will need to be further addressed in order to obtain the full benefit for MIR applications.
Fecal Glucocorticoids: A Noninvasive Method of Measuring Adrenal Activity in Wild and Captive Rodents

To determine the utility of fecal corticosteroid concentration as a measure of chronic stress under laboratory and field conditions, we biochemically and physiologically validated a radioimmunoassay for corticosteroids in three rodent species, house mice (Mus musculus), deer mice (Peromyscus maniculatus), and red‐back voles (Clethrionomys gapperi). The biochemical validations demonstrated that the assay accurately and precisely measured corticosteroid concentration in the feces. The physiological validation indicated that the assay was sensitive enough to detect the stress associated with (a) brief handling and bleeding of animals, (b) chronic caloric restriction, (c) exposure to a novel environment, and (d) exposure to a novel cold environment. Our results suggest that fecal measurements reflect stress levels experienced by these animals approximately 6–12 h before defecation. Therefore, given a judicious trapping and trap‐monitoring protocol, this assay has considerable utility for measuring the stress levels at which animals actually exist in the field.
Ipsilateral Hand Input to Area 3b Revealed by Converging Hemodynamic and Electrophysiological Analyses in Macaque Monkeys

Functional magnetic resonance imaging (fMRI) of the hand representation in primary somatosensory cortex (area 3b) of macaque monkeys revealed an ipsilateral hand input undetected by most previous studies. Ipsilateral responses had a hemodynamic signature indistinguishable from that of contralateral hand responses. We explored the neural mechanisms of the fMRI effects using a second derivative analysis of field potentials [current source density (CSD) analysis] combined with action potential profiles, sampled from area 3b using linear array multielectrodes. In contrast to the predominantly excitatory contralateral response, the colocated ipsilateral response appeared dominated by inhibition, suggesting that ipsilateral inputs may have modulatory effects on contralateral input processing. Our findings confirm bimanual convergence at the earliest stage of cortical somatosensory processing in primates. They also illustrate the value of combined CSD and fMRI analyses in monkeys for defining hidden aspects of sensory function and for investigating the neuronal processes generating fMRI signals.
Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition

Illumination compensation and normalization play a crucial role in face recognition. The existing algorithms either compensated low-frequency illumination, or captured high-frequency edges. However, the orientations of edges were not well exploited. In this paper, we propose the orientated local histogram equalization (OLHE) in brief, which compensates illumination while encoding rich information on the edge orientations. We claim that edge orientation is useful for face recognition. Three OLHE feature combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard protocols. We further evaluated the average performance of the proposed algorithm when the images lighted differently were observed, and the proposed algorithm yielded the promising results.
Efflux-Mediated Resistance to Fluoroquinolones in Gram-Positive Bacteria and the Mycobacteria

The fluoroquinolone (FQ) group of antimicrobial agents is increasingly popular in the treatment of a variety of gram-negative infections, against which they are often highly effective. FQs are traditionally less active against gram-positive pathogens, although they are clinically useful against Mycoplasma pneumonia and have been employed in the treatment of drug-resistant mycobacterial infections (5), as well as infections caused by Staphylococcus aureus, Enterococcus faecalis, and penicillin-resistant Streptococcus pneumoniae (reviewed in references 6 and 24). With the development of newer FQs exhibiting enhanced activity against gram-positive bacteria (18, 35, 43, 62) it is likely, too, that this class of compounds will see more frequent use against these organisms. Still, it is clear that FQ use promotes FQ resistance, which is already a problem in methicillin-resistant S. aureus (MRSA), and has, in fact, been reported in all gram-positive pathogens for which FQ use has occurred. As with gram-negative pathogens, FQ resistance in gram-positive organisms usually results from target site mutations (gyrA [DNA gyrase] and parC or grlA [topoisomerase IV]) or active export of the agents via efflux pumps (24, 32). This review focuses on efflux mechanisms of FQ resistance, their distribution and clinical significance in gram-positive pathogens, the possible natural function(s) of these, and finally, the therapeutic potential of efflux pump inhibitors.
The internet of Bio-Nano things

The Internet of Things (IoT) has become an important research topic in the last decade, where things refer to interconnected machines and objects with embedded computing capabilities employed to extend the Internet to many application domains. While research and development continue for general IoT devices, there are many application domains where very tiny, concealable, and non-intrusive Things are needed. The properties of recently studied nanomaterials, such as graphene, have inspired the concept of Internet of NanoThings (IoNT), based on the interconnection of nanoscale devices. Despite being an enabler for many applications, the artificial nature of IoNT devices can be detrimental where the deployment of NanoThings could result in unwanted effects on health or pollution. The novel paradigm of the Internet of Bio-Nano Things (IoBNT) is introduced in this paper by stemming from synthetic biology and nanotechnology tools that allow the engineering of biological embedded computing devices. Based on biological cells, and their functionalities in the biochemical domain, Bio-NanoThings promise to enable applications such as intra-body sensing and actuation networks, and environmental control of toxic agents and pollution. The IoBNT stands as a paradigm-shifting concept for communication and network engineering, where novel challenges are faced to develop efficient and safe techniques for the exchange of information, interaction, and networking within the biochemical domain, while enabling an interface to the electrical domain of the Internet.
Fast and Accurate Large-Scale Stereo Reconstruction Using Variational Methods

This paper presents a fast algorithm for high-accuracy large-scale outdoor dense stereo reconstruction of man-made environments. To this end, we propose a structure-adaptive second-order Total Generalized Variation (TGV) regularization which facilitates the emergence of planar structures by enhancing the discontinuities along building facades. As data term we use cost functions which are robust to illumination changes arising in real world scenarios. Instead of solving the arising optimization problem by a coarse-to-fine approach, we propose a quadratic relaxation approach which is solved by an augmented Lagrangian method. This technique allows for capturing large displacements and fine structures simultaneously. Experiments show that the proposed augmented Lagrangian formulation leads to a speedup by about a factor of 2. The brightness-adaptive second-order regularization produces sub-disparity accurate and piecewise planar solutions, favoring not only fronto-parallel, but also slanted planes aligned with brightness edges in the resulting disparity maps. The algorithm is evaluated and shown to produce consistently good results for various data sets (close range indoor, ground based outdoor, aerial imagery).
Rule-Structured Facial Animation System

Our overall goal is to produce as automatic as possible facial expressions with wrinkles from spoken input. We focus on two aspects of this problem: integration of the expressive wrinkles and generation of synchronized speech-animation. Our facial model integrates facial muscles deformations and bulges. We have produced a high level programming language to automatically drive 3D animation of facial expressions from speech. Our system embodies rule-governed translation from speech and utterance meaning to facial expressions. We are concerned primarily with expressions conveying information correlated with the intonation of the voice, some of which are also correlated with affect or emotion. We apply our automatic animation model to a new facial animation system which integrates effects of the facial motion as expressive wrinkles and muscles dependencies. We obtain then with this subtle criteria of modeling and motion an animation much more expressive and natural.
Good Continuation in Layers: Shading flows, color flows, surfaces and shadows

We extend the concept of good continuation in a uniform fashion from boundaries to shading, hue, and texture. Each has the property that local measurements yield an orientation, which we explicitly establish for hue using geometric harmonic techniques. Good continuation arises in a geometric sense, because these orientations all vary smoothly in an appropriate sense. Thus they correspond to flows. Taken together they define a layered set of flows, in the sense the "horizontal" computations within each flow provide global consistency while "vertical" computations across flows enable the identification of shading and shadowing and different types of edges. Evidence is reviewed that primate visual systems enjoy such an organization.
Adaptive Localized Active Route Maintenance Mechanism to Improve Performance of VoIP over Ad Hoc Networks

An important characteristic of a mobile ad hoc network routing protocol is connectivity maintenance. The protocol’s ability to maintain routes and react to topology changes due to link failures affects the delay, packet loss, and throughput of real-time applications. However, the time required to recover from a link failure in the current AODV protocol is too long for real-time applications. The objective of this paper is to develop a mechanism to allow fast response to link breaks to enable real-time communications, such as Voice over IP. We propose AODVM-ALARM, an adaptive localized route maintenance mechanism, over an existing ad hoc multipath routing protocol. The mechanism dynamically monitors and maintains on-going communications by utilizing special control messages and packet analysis techniques to enable fast response to link breaks. The mechanism can recognize, respond and recover quickly from link breaks due to topology changes. AODV-ALARM has been tested in an ad hoc experimental testbed, and compared with AODV-UU and AODVM protocols. Results show that AODVM-ALARM significantly improves the response time to link failures and achieves an overall improvement in the network performance in terms of the failover delay, failover loss and throughput when compared with the previous ad hoc routing protocols. AODVM-ALARM which is easily deployable, fault tolerant and have a fast response to topology changes can be used to rapidly establish VoIP communications, playing a significant role in emergency response for disaster recovery when the network infrastructure might be broken.
Frequency-Division Multiplexing for Electrical Impedance Tomography in Biomedical Applications

Electrical impedance tomography (EIT) produces an image of the electrical impedance distribution of tissues in the body, using electrodes that are placed on the periphery of the imaged area. These electrodes inject currents and measure voltages and from these data, the impedance can be computed. Traditional EIT systems usually inject current patterns in a serial manner which means that the impedance is computed from data collected at slightly different times. It is usually also a time-consuming process. In this paper, we propose a method for collecting data concurrently from all of the current patterns in biomedical applications of EIT. This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once. The signals from various current injecting electrodes are separated by injecting different frequencies through each electrode. This is called frequency-division multiplexing (FDM). At the voltage measurement electrodes, the voltage related to each current injecting electrode is isolated by using Fourier decomposition. In biomedical applications, using different frequencies has important implications due to dispersions as the tissue's electrical properties change with frequency. Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast. This method allows simultaneous measurements of all the current patterns, which may be important in applications where the tissue changes occur in the same time scale as the measurement. We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.
Fish consumption and respiratory symptoms among young adults in a Norwegian community.

The aim of this study was to investigate the relationship between dietary fish consumption and self-reported respiratory symptoms among young adults. A random sample of 4,300 subjects, aged 20-44 yrs, living in Bergen, Norway, received a postal questionnaire on respiratory symptoms, of whom 80% responded. Mean fish consumption was assessed in a food-frequency questionnaire by asking how often the subject consumed units of fish (150 g) during the last year. Average fish consumption was 1.8 units x week(-1). Fish intake of <1 unit x week(-1) was reported by 24%, 41% reported consumption of 1 unit x week(-1) and 35% intake of >1 unit x week(-1). A high fish intake was significantly associated with increasing age after adjusting for smoking. Adjusted for smoking habits, the prevalence of "cough at night" and "chest tightness" showed a decreasing trend with increasing fish consumption (p<0.05), while such a trend for "wheeze" was demonstrated only in smokers (p=0.008 for interaction). In logistic regression models (adjusting for age, sex, body mass, smoking habits and occupational exposure) fish consumption (three categories) was not significantly associated with "wheeze", "chest tightness", "breathless at night" or "asthma attack", although the odds ratios (OR) were consistently less than 1 (except for "asthma attack"). Fish consumption was of borderline significance as a protective factor of "cough at night", OR = 0.86 (95% confidence interval: 0.76-0.97) but in stratified analyses only in smokers. Subjects reporting very high levels of fish consumption (>14 units x week(-1)) did not have lower prevalences of respiratory symptoms. In conclusion, among young Norwegian adults, with a relatively low prevalence of asthma and an overall high fish intake, fish consumption was not a significant predictor of four out of five respiratory symptoms.
An Architecture-Aware Approach to Hierarchical Online Failure Prediction

Failures in software systems during operation are inevitable. They cause system downtime, which needs to be minimized to reduce or avoid unnecessary costs and customer dissatisfaction. Online failure prediction aims at identifying upcoming failures at runtime to enable proactive maintenance actions. Existing online failure prediction approaches focus on predicting failures of either individual components or the system as a whole. They do not take into account software architectural dependencies, which determine the propagation of failures. In this paper, we propose a hierarchical online failure prediction approach, HORA, which employs a combination of both failure predictors and architectural models. We evaluate our approach using a distributed RSS reader application by Netflix and investigate the prediction quality for two representative types of failures, namely memory leak and system overload. The results show that, overall, our approach improves the area under the ROC curve by 10.7% compared to a monolithic approach.
Overcoming erroneous domain knowledge in plan-based reward shaping

Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided domain knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially erroneous. This paper introduces a novel use of knowledge revision to overcome erroneous domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.
Hypothesis: NDL proteins function in stress responses by regulating microtubule organization

N-MYC DOWNREGULATED-LIKE proteins (NDL), members of the alpha/beta hydrolase superfamily were recently rediscovered as interactors of G-protein signaling in Arabidopsis thaliana. Although the precise molecular function of NDL proteins is still elusive, in animals these proteins play protective role in hypoxia and expression is induced by hypoxia and nickel, indicating role in stress. Homology of NDL1 with animal counterpart N-MYC DOWNREGULATED GENE (NDRG) suggests similar functions in animals and plants. It is well established that stress responses leads to the microtubule depolymerization and reorganization which is crucial for stress tolerance. NDRG is a microtubule-associated protein which mediates the microtubule organization in animals by causing acetylation and increases the stability of α-tubulin. As NDL1 is highly homologous to NDRG, involvement of NDL1 in the microtubule organization during plant stress can also be expected. Discovery of interaction of NDL with protein kinesin light chain- related 1, enodomembrane family protein 70, syntaxin-23, tubulin alpha-2 chain, as a part of G protein interactome initiative encourages us to postulate microtubule stabilizing functions for NDL family in plants. Our search for NDL interactors in G protein interactome also predicts the role of NDL proteins in abiotic stress tolerance management. Based on published report in animals and predicted interacting partners for NDL in G protein interactome lead us to hypothesize involvement of NDL in the microtubule organization during abiotic stress management in plants.
Highly Sensitive Sensors Based on Photonic Crystal Fiber Modal Interferometers

We review the research on photonic crystal fiber modal interferometers with emphasis placed on the characteristics that make them attractive for different sensing applications. The fabrication of such interferometers is carried out with different post-processing techniques such as grating inscription, tapering or cleaving, and splicing. In general photonic crystal fiber interferometers exhibit low thermal sensitivity while their applications range from sensing strain or temperature to refractive index and volatile organic compounds.
Cellular and Biochemical Actions of Melatonin which Protect Against Free Radicals: Role in Neurodegenerative Disorders

Molecular oxygen is toxic for anaerobic organisms but it is also obvious that oxygen is poisonous to aerobic organisms as well, since oxygen plays an essential role for inducing molecular damage. Molecular oxygen is a triplet radical in its ground-stage (.O-O.) and has two unpaired electrons that can undergoes consecutive reductions of one electron and generates other more reactive forms of oxygen known as free radicals and reactive oxygen species. These reactants (including superoxide radicals, hydroxyl radicals) possess variable degrees of toxicity. Nitric oxide (NO•) contains one unpaired electron and is, therefore, a radical. NO• is generated in biological tissues by specific nitric oxide synthases and acts as an important biological signal. Excessive nitric oxide production, under pathological conditions, leads to detrimental effects of this molecule on tissues, which can be attributed to its diffusion-limited reaction with superoxide to form the powerful and toxic oxidant, peroxynitrite. Reactive oxygen and nitrogen species are molecular “renegades”; these highly unstable products tend to react rapidly with adjacent molecules, donating, abstracting, or even sharing their outer orbital electron(s). This reaction not only changes the target molecule, but often passes the unpaired electron along to the target, generating a second free radical, which can then go on to react with a new target amplifying their effects. This review describes the mechanisms of oxidative damage and its relationship with the most highly studied neurodegenerative diseases and the roles of melatonin as free radical scavenger and neurocytoskeletal protector.