Miracle-dz/UniFAR-FTUs huggingface dataset:
BIRD: Learning Binary and Illumination Robust Descriptor for Face Recognition

Recently face recognition has made signiﬁcantly progress due to the advancement of large scale Deep Convolutional Neural Network (DeepCNNs). Despite the great success, the known deﬁciencies of DeepCNNs have not been addressed, such as the need for too much labeled training data, energy hungry, lack of theoretical interpretability, lack of robustness to image transformations and degradations, and vulnerable to attacks, which limit DeepCNNs to be used in many real world applications. Therefore, these factors make previous predominating Local Binary Patterns (LBP) based face recognition methods still irreplaceable. In this paper we propose a novel approach called BIRD (learning Binary and Il-lumination Robust Descriptor) for face representation, which nicely balances the three criteria: distinctiveness, robustness, and computationally inexpensive cost. We propose to learn discriminative and compact binary codes directly from six types of Pixel Difference Vectors (PDVs). For each type of binary codes, we cluster and pool these compact binary codes to obtain a histogram representation of each face image. Six global histograms derived from six types of learned compact binary codes are fused for the ﬁnal face recognition. Experimental results on the CAS_PERL_R1 and LFW databases indicate the performance of our BIRD surpasses all previous binary based face recognition methods on the two evaluated datasets. More impressively, the proposed BIRD is shown to be highly robust to illumination changes, and produces 89.5% on the CAS_PEAL_R1 illumination subset, which, we believe, is so far the best reported results on this dataset. Our code is made available
In vitro antimicrobial and antioxidant studies of active principle hexacosylidenecyclohexane extracted from bark of Madhuca longifolia L

Plants are benefactor of humankind. The enormous increase in multiresistant strains irrespective of potent antibiotics has resulted in an exploration of new plant based products. Free radicals produced in the body are responsible for oxidation of biomolecules and increases the risk of chronic disorders. Antioxidants are the compounds which gives protection against cell damage. Active principle hexacosylidenecyclohexane isolated from the bark of Madhuca longifolia L. is used in the present antimicrobial and antioxidant studies. The antimicrobial activity of active principle was evaluated against Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus and Candida albicans by disk diffusion method and Minimum inhibitory concentration assay. The antioxidant activity of crude extract was tested by Superoxide dismutase assay and active principle was evaluated by DPPH and TRPA assay. The results obtained from the present work proved active principle have strong antimicrobial and antioxidant activity and can be used in herbal formulations.
Institute 2007-06-01 Rare : Resource Aware Routing for mEsh

An important element of any routing protocol used for Wireless Mesh Networks (WMNs) is the link cost function used to represent the radio link characteristic. The majority of the routing protocols for WMNs attempt to accurately characterise the radio link quality by constructing the link cost function from the measurements obtained using active probing techniques, which introduces overhead. In this paper we propose a new approach called Resource Aware Routing for mEsh (RARE) which instead employs passive monitoring to gather radio link information. This results in a smaller overhead than the other methods that require active network probing, and is load independent since it does not require an access to the medium. Moreover, we show that our RARE approach performs well in a real radio environment through a number of experiments performed on a static 17 node WLAN mesh testbed.
Research of LSTM Additions on Top of SQuAD BERT Hidden Transform Layers

The Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is either a segment of text from the corresponding reading passage, or unanswerable [1]. Current NLP tasks aim at answering these questions at as close to a human level of accuracy as possible. Improving reading comprehension NLP models is an extremely pervasive subject, as online search machines could be improved significantly with increased robustness in these models. One NLT model currently ranked extremely high on the SQUAD leaderboards is Bidirectional Encoder Representations from Transformers (BERT). Our aim in this project was to improve on F1 and EM scores from a baseline BERT implementation by modifying the architecture to use Long-ShortTerm-Memory (LSTM). We trained a baseline BERT model [2] on the SQUAD 2.0 dataset (a series of over 150,000 reading comprehension questions, where over 50,000 questions do not have an answer found in the associated text) which yielded satisfactory F1 and EM scores. From there, we modified the architecture to use an LSTM to attempt to improve on the scores acheived by the baseline BERT model. We successfully trained a modified BERT model with an LSTM, but were unable to tune the model to achieve better results than the baseline.
Element-Embedded Style Transfer Networks for Style Harmonization

Neural image style transfer has been receiving increasing attention on the creation of artistic images. Given a reference image with style of interest, image style harmonization aims to blend an element from one image into this reference, achieving harmonization for the stylized output. We present an Element-Embedded Style Transfer Network (E2STN) for addressing this task. Our proposed network uniquely integrates style transfer and image matting modules. Together with global and local discriminators, both context and style information can be properly preserved in the embedded output. In the experiments, we show that our proposed network performs favorably against existing style transfer models and is able to produce results with satisfactory quality.
Traffic Flow Auralisation based on Single Vehicle Pass-by Noise Synthesis

Research into the auralisation of road traffic noise has drawn more attention in the recent years, as it can provide a more direct and immersive experience with which to communicate the implication of major urban design decisions with city planners and the public. Some synthesis models of single vehicle pass-by noise, and methods for outdoor sound propagation modelling have been proposed, which may be integrated for auralisation of traffic flow scenes. In this paper, a framework for traffic flow auralisation is presented. The synthesis model is composed of multiple single vehicle pass-by examples, based on tyre noise and propulsion sound synthesis. A first-order image source method is implemented, as a first approximation to represent sound propagation across a local region of a wider urban environment. The resulting plausibility of the proposed traffic flow auralisation model under different traffic flow scenes are under assessment using subjective listening tests.
Scientific Publishing: Education as the Key Enabler for the Transition to Open Science

After showing how the advent of the internet, in an almost opposite fashion to what happened to newspaper publishing, has led to further flourishing of the $25 billion scholarly publishing industry, I show how the unexpected expansion of preprints to all scientific disciplines beyond physics, mathematics and computer science is actually reshaping scientific communication at large and then, inevitably, scientific publishing. I thus provide arguments substantiating my viewpoint on why and how expanding the education of today’s students and young researchers to include modern scholarly communication will be instrumental for the transition to open science.
Identifying the best antibiotic-target genes in essential biochemical pathways

3 Novel antibiotics: a solution to a post-antibiotic apocalypse . 4 Introduction . 5 The Non-Mevalonate (MEP) Pathway . 5 The Coenzyme A biosynthetic Pathway . 6 The Nicotinamide Adenine Dinucleotide (NAD) Biosynthetic Pathway . 8 Aim of the project . 9 Materials and Methods . 10 Strains and Growth conditions . 10 Polymerase chain reaction . 10 Purification of the PCR products . 11 Plasmid extraction . 11 Plasmid transformation . 11 λ Red Recombineering . 11 Spot assay . 12 Bioscreen . 12 Minimal Inhibitory Concentration (MIC) assay. 13 MACS Flow Cytometry . 13 Sequencing . 13 Results . 14 Construction of strains . 14 Spot Assay . 15 Bioscreen . 15 MIC Assay . 17 Selection of ispG and ispH mutants . 17 Yfp-kan cassette fusions . 18 Discussion . 20 Acknowledgements . 22 References . 23 Supplementary Material . 27
Screening and characterization of actinomycetes with antibacterial activity from shore line soil of Tiruchendur

The aim of the present study was to screen, characterize and to identify the actinomycetes having antibacterial activity. The actinomycetes isolates obtained from coastal soil of Tiruchendur Tamil Nadu, India were screened for their antibacterial activity by Kirby Bauer well diffusion method. In screening, three actinomycetes Tir7, Tir12 and Tir13 were selected for further studies. Morphological, Biochemical and Physiological characteristics were studied to characterize the actinomycetes. The three isolates were mass cultured in ISP2 broth and the bioactive metabolites were isolated using centrifugation phase separation method by adding equal amount of eight different solvents. Bacterial strains such as Staphylococcus aureus, Micrococcus luteus, Bacillus cereus, Proteus mirabilis, Proteus vulgaris, Neisseria mucosa, and Enterococcus faecalis were obtained from MTCC, Chandigarh, were challenged with the extracts by Kirby Bauer well diffusion method. Isolate Tir12 was selected as the most potent actinomycete among the three isolates and the solvent ethyl acetate and chloroform extracted the bioactive compounds better than other solvents. Ethyl acetate extracts was able to inhibit Staphylococcus aureus, Micrococcus luteus and Enterococcus faecalis whereas chloroform extract of Tir12 isolate was able to inhibit Proteus mirabilis and Bacillus cereus better than other solvent extracts. The potent isolate Mah25 was characterized by 16S rRNA sequencing and the isolate was identified as Streptomyces sp. MAH25 (AB899158).
A new approach to investigate dividend smoothing: Theoretical and empirical evidence

The main purpose of this study is to use Lee et al.’s generalized dividend behavior model (1987) to re-examine whether previous dividend researches are appropriate. This study proposes a dividend smoothing model that integrates two prevailing dividend hypotheses to evaluate the degree of dividend smoothing behaviors and investigates cross-sectional variation in determining a firm’s propensity to smooth dividend. By using a sample of 1,193 U.S. firms, our empirical analysis classifies firms’ payout patterns into five different policies and suggests that 54% of observed dividend behavior among U.S. firms is attributable to signaling incentive. Furthermore, this study decomposes dividend smoothing behaviors through two channels: (1) lagging channel (via speed of partial adjustment, SOA) and (2) leading or signaling channel (via earnings expectation coefficient). Our findings show that firms with a stronger monitoring mechanism or are subject to more agency conflicts will smooth dividend more through partial adjustment channel. Also, firms subject to higher information asymmetry or lower growth opportunity will smooth dividend more through the signaling-incentive channel. In this paper, we propose four future research issues to be done. Especially, we argue that Lambrecht &Myers’s theoretical model (2012) which only use partial adjustment model may not be the most appropriate dividend behavior model. Therefore, we will re-examine their theoretical results in term of adaptive expectation model.
Transmit Antenna Selection with imperfect CQI feedback in Multi-user OFDMA systems

In this paper, we analyse the impact of imperfect Channel Quality Information (CQI) feedback on a multi-user OFDMA system which employs transmit antenna selection. As CQI, the instantaneous signal-to-noise ratio (SNR) of the different subcarriers is employed which is used at the base station to allocate the subcarriers and to select the applied modulation scheme and transmit antenna. The imperfectness of the CQI feedback arises from channel estimation errors, SNR value quantisation, feedback errors and time delays. Two different antenna selection feedback schemes are introduced, which differ in the composition of the feedback signalled to the BS. Closed form expressions for the average data rate and bit error rate (BER) are analytically derived for both schemes. Finally, the maximum achievable data rate subject to a BER constraint is analysed and the robustness against imperfect CQI is compared for both antenna selection feedback schemes.
What Can COADS Tell Us ? Potential and Limitations

Some of the potentials and limitations in the COADS sea surface temperature (SST examined. In particular, we address uncertainties due to a) secular changes in the samplin and those due to b) consistent under-sampling of some areas for the entire record. The firs questions relate the use of COADS to the examination of long time series for global ch studies, the second to systematic uncertainties in the analysis of contemporary spatial ana
Telemanipulated Long Bone Fracture Reduction

Fractures of the human thigh bone, the femur, are commonly caused by high-energy injury mechanisms, like traffic accidents, predominantly in young males or by low-energy mechanisms, like falling, in elderly females (Martinet et al., 2000; Zlowodzki et al., 2006). With approximately 37 per 100,000 persons per year this is an extremely frequently encountered injury (Arneson et al., 1988; Zlowodzki et al., 2006). In 1999 334,410 patients with fractures of the lower extremities have been counted in Germany1. 144,659 of which had fractures of the thigh bone. After subtracting fractures in the proximal (hip side) femur, 25,695 patients remain with fractures in the femoral shaft (the middle, diaphyseal) region to which this work is dedicated. Today, the treatment of choice for femur shaft fractures preferred by many surgeons is the minimal invasive technique of intramedullary nailing, which has been established as a standard technique for a definite stabilizing treatment in diaphyseal fractures of the lower extremities (Kempf et al., 1985; Krettek et al., 1996; Krettek, 2001; Winquist et al., 1984). The complete process of intramedullary nailing is shown as a sketch in figure 1. The process starts with the opening of the medullary cavity. A small soft tissue cut of about 5 cm has to be placed at the proximal end of the femur. In extension of the femoral shaft, the bone's cavity has to be opened. This is achieved with a surgical drill. Now the intramedullary nail is inserted into the bone's medullary cavity until it reaches the fracture region. Subsequently the two major bone fragments are aligned accordingly to their correct anatomical position. For this the distal (knee side) fracture segment is moved by the surgeon by means of a socalled Schanz' screw, whereas the proximal fracture segment is hold in its position by means of a second Schanz' screw. According to (Ruedi & Murphy, 2000), this form of manipulation is called “joystick” reduction. When the fracture segments are finally aligned correctly, the intramedullary nail is further inserted. Finally the nail is locked with the bone by means of lateral screws. During the final insertion and the locking of the nail, the correct retention, which means maintaining the correct segment positions, has to be ensured. The whole process is supervised by means of X-ray imaging. A detailed description of this surgical procedure can be found in (Ruedi & Murphy, 2000).
Thermoelectric Ca 3 Co 4 O 9 ceramics consolidated by Spark Plasma sintering

Hole-doped Ca3Co4O9 (Co349) ceramics were prepared using solid-state reaction. Two processing strategies have been used to produce the thermoelectric oxide ceramics, Conventional and Spark Plasma (SPS) Sintering to control the grains consolidation, texturation and sample densification. Thermoelectric properties were measured and the influence of the processing conditions on the properties was evidenced. SPS favours shorter elaboration times and produces samples with larger thermoelectric properties due to better densification and alignment. The effect of the free deformation and texturation using the SPS technique is discussed. Seebeck coefficient values of 180 μV/K at 873 K are obtained.
On the Design of Ultra-Fast Electro-Mechanical Actuators

The continuously increasing demand for connecting electric grids with remote renewable energy sources such as wind power and photovoltaic cells has rekindled interest in high voltage direct current (HVDC) multi-terminal networks. Although HVDC networks have numerous benefits, their adoption relies entirely on the availability of HVDC circuit breakers which, compared to traditional alternating current circuit breakers, have to operate in a time frame of milliseconds. This thesis deals with the design of ultra-fast electro-mechanical actuators based on the so-called Thomson coil (TC) actuator. The simulation of a (TC) actuator constitutes a multi-physical problem where electromagnetic, thermal, and mechanical aspects must be considered. Moreover, it is complex since all those variables are co-dependent and have to be solved for simultaneously. As a result, a multi-physics simulation model that can predict the behavior and performance of such actuators with a high degree of accuracy was developed. Furthermore, other actuator concepts were also investigated and modeled in light of searching for a drive with a superior efficiency. The theory behind the force generation principles of two different types of ultra-fast electromechanical actuators, the TC and the double sided coil (DSC), were compared by the use of static, frequency, and comprehensive transient multi-physics finite element simulation models. Although, simulation models serve as a powerful tool for modeling and designing such state of the art actuators, without validation, they are weak and prone to errors since they rely on approximations and simplifications that might not always hold. Therefore, a prototype was built in the laboratory and the model was validated experimentally. Finally, it is important to note that the drives in this thesis are intended to actuate metallic contacts. As such, their behavior and performance upon mechanical loading was studied. Furthermore, some scaling techniques were applied to boost their performance and efficiency.
A model of the prespike at the calyx of Held synapse

The calyx of Held synapse is a giant axosomatic synapse in the brainstem that functions as a fast and reliable auditory relay. Because of its large size, the presynaptic action potential can be picked up in a recording of the postsynaptic cell. This so-called prespike is caused by the ephaptic coupling of presynaptic membrane currents via the synaptic cleft. We constructed an electric model of the prespike and estimated values for the leak conductance of the synaptic cleft, the capacitance of the release face and the presynaptic calcium current density based on simultaneous preand postsynaptic voltage clamp recordings. These values allowed us to estimate an impact of presynaptic capacitive and ionic currents on the cleft potential, and suggested that fenestration of the calyx of Held during development might be important to further reduce the cleft leak conductance of the cleft to minimize its impact on the presynaptic calcium channels.
Relation-aware Multiple Attention Siamese Networks for Robust Visual Tracking

Partial occlusion is a challenging problem in visual object tracking. Neither Siamese network based trackers nor conventional part-based trackers can address this problem successfully. In this paper, inspired by the fact that attentions can make the model focus on the most salient regions of an image, we propose a new method named Relation-aware Multiple Attention (RMA) to address the partial occlusion problem. In the RMA module, part features generated from a set of attention maps can represent the discriminative parts of the target and ignore the occluded ones. Meanwhile, an attention regularization term is proposed to force the multiple attention maps to localize diverse local patterns. Besides, we incorporate relation-aware compensation to adaptively aggregate and distribute part features to capture the semantic dependency among them. We integrate the RMA module into Siamese matching networks and verify the superior performance of the RMA-Siam tracker on ﬁve visual tracking benchmarks, including VOT-2016, VOT-2017, LaSOT, OTB-2015 and TrackingNet.
Auralization of interactive virtual scenes containing numerous sound sources

Typical everyday situations usually contain a large number of sound sources. In virtual reality applications, where the processing demands for the acoustical rendering of a scene should be kept a low level, it is challenging to simulate and spatialize a high number of virtual sound sources. This work presents different solutions for rendering sound sources in virtual scenes with varying level of interactivity and complexity. For a binaural free-field auralization of up to hundreds of virtual sound sources, a model based on k-means clustering was recently developed, with the main objective to limit the number of required convolutions. To improve the perceptual quality of the rendering, the model was extended with an efficient correction of the interaural time difference of each virtual sound source. In addition to a brief benchmark analysis of the rendering module, this work also describes how this clustering approach was integrated in the open source auralization framework Virtual Acoustics.
Comparison of Classifiers for use in a Learning by Demonstration System for a Situated Agent

In learning by demonstration systems, learning is performed by an agent observing how an expert behaves in response to a given input. The learning task is more difficult when the agent is situated in a dynamically-changing environment, of which only a partial view is available at any time. In this paper we propose a comparison of different learning by demonstration techniques, namely case-based reasoning, decision trees, support vector machines and naive bayes classifiers, in the dynamic environment constituted by the RoboCup robotic soccer simulation. An initial look at the results indicate that case-based reasoning behaves well across all experiments and outperforms the other classifiers. Case-based reasoning even outperforms a decision tree classifier when learning from an expert whose internal reasoning is represented as a decision tree.