Miracle-dz/UniFAR-FTUs huggingface dataset:
An Accessible Library for Machine Learning with Homomorphic Encryption

Homomorphic encryption allows computation on encrypted ciphertext so that decrypted plaintext produces a semantically useful computation. Since this comes at a tremendous computation cost for general-purpose computing, the synthesis of these approaches remains in the nascent stages of research and reaches practicability only on small data despite the dire need for privacy-preserving techniques in an age of rampant data surveillance. There is, therefore, a need for an accessible toolkit exposing the power of homomorphic encryption for data scientists to apply to machine learning problems. Privacy-preserving machine learning can have a tremendously positive societal benefit by allowing ethical biomedical research, building public trust about the security of sensitive data, and preventing potential abuses and fraud that result from data breaches in increasingly common hacking incidents. This thesis will attempt to contribute to this direction by building a software library that operates on homomorphically encrypted data but remains approachable to data scientists and simultaneously runs on at least some modest datasets and low-power devices, key limitations that so far have hampered uptake of homomorphic encryption to machine learning. Finally, it will seek to quantify the feasible bounds of data sizes and trade-offs of encryption, decryption, prediction, model training time and the performance penalty incurred from encryption.
LIFE-CYCLE GREENHOUSE GAS EMISSIONS OF BIOMETHANE AND HYDROGEN PATHWAYS IN THE EUROPEAN UNION

Results from this study suggest several policy recommendations regarding hydrogen and biomethane pathways in the REDII and other relevant climate or gas policies in the EU. To be eligible to be counted towards REDII targets, renewable hydrogen and electrofuels used as transport fuel must achieve a 70% life-cycle GHG reduction compared to fossil fuels. The target for biomethane is 50-65% reduction when used in transport or 70-80% reduction when used for electricity, heating and cooling. Fuels with lower life-cycle GHG intensity make a greater contribution toward the proposed 13% GHG target for the transportation sector and should thus be incentivized more than fuels with higher GHG intensity. Biomethane producers can choose to use the default GHG intensity value provided in the REDII or to calculate a facility-specific GHG intensity using data that reflects their real-world operations. Our recommendations pertain both to excluding certain pathways from eligibility toward meeting climate targets as well as to a life-cycle methodology for calculating GHG intensity values to determine compliance. This study aims to help European policymakers understand the uncertainties in carbon reduction potentials from gaseous fuel pathways. Specifically, this study conducts a thorough sensitivity analysis of the GHG intensity of four biomethane pathways and eight hydrogen pathways. For each pathway, we identify key impacting parameters; collect a range of values for each parameter from the literature; and model the uncertainty range in the pathway’s final GHG intensity. We climate
A NEW METHOD FOR ESTIMATION OF MULTI-HARMONIC POWER SIGNAL PARAMETERS NOVA METODA ZA ESTIMACIJU PARAMETARA SLOŽENO- HARMONIJSKOG ENERGETSKOG SIGNALA

A systematic analytical procedure for simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves was proposed in this paper, which enabled fast and precise estimation with a small numerical error. Individual sinusoidal components have been extracted from the input complex-harmonic signal with finite-impulse response (FIR) comb filters. The proposed algorithm is based on the application of partial derivate of the processed and filtered input signal, after which it is performed weighted estimation procedure to better estimate the size of the values of the fundamental frequency, amplitude and the multi-sinusoid signal phase. The designed algorithm can be used in the signal reconstruction and estimation procedures, spectral processing, in procedures for the identification of the system that is observed, as well as other important signal processing areas. Through the simulation check, the effectiveness of the proposed algorithm was assessed, which confirmed its high performance.
Revisiting Weight Initialization of Deep Neural Networks

The proper initialization of weights is crucial for the eﬀective training and fast convergence of deep neural networks (DNNs). Prior work in this area has mostly focused on the principle of balancing the variance among weights per layer to maintain stability of (i) the input data propagated forwards through the network, and (ii) the loss gradients propagated backwards, respectively. This prevalent heuristic is however agnostic of dependencies among gradients across the various layers and captures only ﬁrst-order eﬀects per layer. In this paper, we investigate a unifying approach , based on approximating and controlling the norm of the layers’ Hessians , which both generalizes and explains existing initialization schemes such as smooth activation functions , Dropouts , and ReLU . We empirically demonstrate that tracking the Hessian norm is a useful diagnostic tool which helps to more rigorously initialize weights over a variety of DNN applications, including both (i) shallow networks like simpliﬁed GoogleNet using Flowers dataset, and (ii) deep networks like EﬃcientNet and ResNet using fashion MNIST, CIFAR-10 and Google SVHN for image processing.
PHYSICS OF MAGNETIC PHENOMENA AND PHYSICS OF FERROICS

We study the electronic structure, magnetization, and thermoelectric properties of CsPd0.875Cr0. 125I3 obtained by doping CsPdI3 with atoms of the 3d transition metal Cr. By applying the generalized-gradient-approximation (GGA) and the GGA + U one, we found that CsPd0.875Cr0. 125I3 alloy exhibits a completely metallic characteristic. Changes in the thermoelectric properties of the alloy are determined with the use of the BoltzTrap code. The electronic thermal conductivities (k/τ), Seebeck coefficients (S), power factors (PF), and electrical conductivities (σ/τ) are calculated. The value of the ZT merit factor is near 1 at room temperature, by indicating that CsPd0.875Cr0. 125I3 is a good candidate for thermoelectric applications at high and low temperatures.
Stretch not flex: programmable rubber keyboard

Stretchability is a property that brings versatility and design freedom to human interface devices. We present a soft, flexible and stretchable keyboard made from a dielectric elastomer sensor sheet. Using a multi-frequency capacitance sensing technique based on a transmission line model, we demonstrate how this keyboard can detect touch in two dimensions, programmable to increase the number of keys and into different layouts, all without adding any new wires, connections or modifying the hardware. The method is efficient and scalable for large sensing systems with multiple degrees of freedom.
SDR-NNP: Sharpened Dimensionality Reduction with Neural Networks

: Dimensionality reduction (DR) methods aim to map high-dimensional datasets to 2D scatterplots for visual exploration. Such scatterplots are used to reason about the cluster structure of the data, so creating well-separated visual clusters from existing data clusters is an important requirement of DR methods. Many DR methods excel in speed, implementation simplicity, ease of use, stability, and out-of-sample capabilities, but produce suboptimal cluster separation. Recently, Sharpened DR (SDR) was proposed to generically help such methods by sharpening the data-distribution prior to the DR step. However, SDR has prohibitive computational costs for large datasets. We present SDR-NNP, a method that uses deep learning to keep the attractive sharpening property of SDR while making it scalable, easy to use, and having the out-of-sample ability. We demonstrate SDR-NNP on seven datasets, applied on three DR methods, using an extensive exploration of its parameter space. Our results show that SDR-NNP consistently produces projections with clear cluster separation, assessed both visually and by four quality metrics, at a fraction of the computational cost of SDR. We show the added value of SDR-NNP in a concrete use-case involving the labeling of astronomical data.
Support Vector Machines for Biomarkers Detection in in vitro and in vivo Experiments of Organochlorines Exposure

Metabolomic studies generate large amounts of data, whose complexity increases if they are derived from in vivo experiments. As a result, analysis methods highly used in metabolomics, such as Partial Least Squares Discriminant Analysis (PLS-DA), can have particular difficulties with this type of data. However, there is evidence that indicates that Support Vector Machines (SVMs) can better deal with complex data. On the other hand, chronic exposure to organochlorines is a public health problem. It has been associated with diseases such as cancer. Therefore, its identification is relevant to reduce their impact on human health. This study explores the performance of SVMs in classifying metabolic profiles and identifying relevant metabolites in studies of exposure to organochlorines. For this purpose, two experiments were conducted: in the first one, organochlorine exposure was evaluated in HepG2 cells; and, in the second one, it was evaluated in serum samples of agricultural workers exposed to pesticides. The performance of SVMs was compared with that of PLS-DA. Four kernel functions were assessed in SVMs, and the accuracy of both methods was evaluated using a k-fold cross-validation test. In order to identify the most relevant metabolites, Recursive Feature Elimination (RFE) was used in SVMs and Variable Importance in Projection (VIP) in PLS-DA. The results show that SVMs exhibit a higher percentage of accuracy with fewer training samples and better performance in classifying the samples from the exposed agricultural workers. Finally, a workflow based on SVMs for the identification of biomarkers in samples with high biological complexity is proposed.
A DEEP LEARNING APPROACH FOR DIABETIC RETINOPATHY DETECTION

: Diabetic retinopathy is an eye ailment caused due to prolonged diabetes. If unacknowledged or ineffective treatment can lead to irreversible vision loss. This ailment can be identified by scrutinizing colour fundus photographs of the human retina. Our work assesses in deep learning field which is used for the detection and class segregation of diabetic retinopathy into binary or multiclass and collate them based on different metric measures like precision, recall, accuracy. The classification of diabetic retinopathy involves multiple steps like pre-processing of image, feature extraction, dimensionality reduction and classification of exudates, woolen spot, hemorrhages & microaneurysm. In this paper, Deep neural network approach is implemented by making use of online available Kaggle dataset, various metrics are calculated and applied pre-processing techniques.
EFFICACY OF COGNITIVE BEHAVIOUR THERAPY REGARDING ATTITUDE TOWARDS HALLUCINATORY VOICES IN PATIENTS WITH SCHIZOPHRENIA

Schizophrenia is still considered to be chronic and depilating illness. Aim: The aim of the present study is to see the changes in attitude towards hallucinatory voices in patients with schizophrenia. Method: Twenty four patients diagnosed with schizophrenia according to ICD-10 DCR were chosen from different inpatient department of RINPAS, Kanke using the simple random sampling technique. After taking informed consent from the patients, socio-demographic and clinical data sheet and BAVQ were administered and they were randomly assigned to CBT+TAU and TAU group. Cognitive behaviour therapy was given to CBT+TAU group, total 1720 sessions of 45 minutes each with a frequency of thrice a week. After completion of the therapy sessions, post assessment was done and follow up assessment was done after six months of post assessment. Data was analyzed with the help of Mann-Whitney U test and Chi-square Test was used for statistical analysis. Result: Significant reduction was found in the attitude towards hallucinatory voices and improvement was found in the post intervention phase which was maintained at follow up. Conclusion: Cognitive behaviour therapy was found to be effective in changing attitude towards hallucinatory voices.
Auditory abstraction from spectro-temporal features to coding auditory entities

The auditory system extracts behaviorally relevant information from acoustic stimuli. The average activity in auditory cortex is known to be sensitive to spectro-temporal patterns in sounds. However, it is not known whether the auditory cortex also processes more abstract features of sounds, which may be more behaviorally relevant than spectro-temporal patterns. Using recordings from three stations of the auditory pathway, the inferior colliculus (IC), the ventral division of the medial geniculate body (MGB) of the thalamus, and the primary auditory cortex (A1) of the cat in response to natural sounds, we compared the amount of information that spikes contained about two aspects of the stimuli: spectro-temporal patterns, andabstract entities present in the same stimuli such as a bird chirp, its echoes, and the ambient noise. IC spikes conveyed on average approximately the same amount of information about spectro-temporal patterns as they conveyed about abstract auditory entities, but A1 and the MGB neurons conveyed on average three times more information about abstract auditory entities than about spectro-temporal patterns. Thus, the majority of neurons in auditory thalamus and cortex coded well the presence of abstract entities in the sounds without containing much information about their spectro-temporal structure, suggesting that they are sensitive to abstract features in these sounds.
ORIGINAL ARTICLE

In our study, the auxin effect on rooting and sprouting behavior of stem cuttings of Excoecaria agallocha L. has been studied. Initially stem cuttings were pretreated to remove the phenol content of cutting and then the stem cuttings that were devoid from phenols were subjected to hormonal treatment with auxins including indole-3-butyric acid (IBA) 2000ppm, indole-3-pyruvic acid (IPA) 2000ppm, naphthalene acetic acid (NAA) 2000 ppm and IBA+NAA combination 2000 ppm. The root length, root number, rooting and sprouting percentage, number of leaves per cutting, leaf area and photosynthetic pigments were analyzed on 40, 50 and 60 days after planting. Besides this, peroxidase isoenzyme pattern of root and leaf was also analyzed. All the growth attributes have shown an increasing trend at all stages of growth with auxin treatments. Among the auxin treatments, IBA 2000ppm vastly enhances rooting and sprouting behaviour of blinding eye mangrove. The isoenzyme analysis for peroxidase clearly showed that peroxidase (POX) highly supported root initiation and root elongation processes in Excoecaria agallocha.
CORRECTION OF KAZAKH SYNTHETIC TEXT USING FINITE STATE AUTOMATA

In this paper we investigate the correction of generated synthetic text for resource-poor languages. In most cases, this synthetic text contains many errors that need to be carefully checked and corrected by additional tools. These errors must be corrected automatically to avoid degrading the performance of the system. Our approach to automatic error correction is based on the use of finite automata to suggest candidates for correction of the misspelled word. After selecting correction candidates, a language model is used to assign points to the correction candidates and choose the best correction in a given context. The proposed approach is language-independent and requires only dictionary and text data to construct the language model. The approach was evaluated in Kazakh and achieved an accuracy of 91%.
NDT_A_335028 3781..3790

1Department of Psychiatry, Tokyo Medical University, Tokyo, Japan; 2Japan Medical Office, Takeda Pharmaceutical Co., Ltd, Tokyo, Japan; 3Takeda Development Center – Japan, Takeda Pharmaceutical Co., Ltd, Osaka, Japan; 4Medical Affairs, Lundbeck Japan K.K. , Tokyo, Japan; 5Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan Aim: Antidepressants, including selective serotonin reuptake inhibitors, often elicit a poor response in patients with major depressive disorder (MDD) with significant anxiety symptoms. This study investigated the effects of the multimodal antidepressant vortioxetine in patients with MDD and associated anxiety. Methods: This was a post hoc analysis of data from an 8-week, randomized, double-blind, placebo-controlled, Phase 3 study of vortioxetine (10 mg or 20 mg) in Japanese patients aged 20–75 years with recurrent MDD and a Montgomery–Åsberg Depression Rating Scale (MADRS) score of at least 26. Changes from baseline to week 8 in MADRS total score and Hamilton Depression Rating Scale (HAM-D) anxiety/somatization factor score were assessed in patients with anxious depression (HAM-D anxiety/somatization factor score ≥7) and without anxious depression. Results: Data were available for 489 patients. In patients with anxious depression, the leastsquares (LS) mean difference (95% confidence interval [CI]) versus placebo in change in MADRS total score was −3.44 (−6.10, −0.77) for vortioxetine 10 mg and −4.51 (−7.15, −1.87) for vortioxetine 20 mg. In patients with non-anxious depression, the LS mean difference (95% CI) versus placebo was −1.81 (−4.71, 1.09) and −1.05 (−4.00, 1.90) for vortioxetine 10 mg and 20 mg, respectively. Changes from baseline in HAM-D anxiety/ somatization factor score were greater in patients treated with vortioxetine 10 mg or 20 mg than in those treated with placebo. Conclusion: Vortioxetine may be effective for patients with anxiety symptoms in MDD. Further research is warranted to investigate these effects in a real-world clinical setting. Clinical Trials Registration: ClinicalTrials.gov identifier for primary study: NCT02389816.
S UPPLEMENTARY M ATERIAL Do Different Tracking Tasks Require Different Appearance Models?

For association-type tasks (MOT, MOTS and PoseTrack), we first report the MOTA metric since it highly-correlates with human’s perception in measuring tracking accuracy [3]. However, the MOTA metric disproportionately overweights good detection accuracy [28, 8]. Since most multi-object trackers (included UniTrack) adopt off-the-shelf detectors, it is desirable to also adopt detectionindependent measures of performance. For this reason, we also report identity based metrics such as IDF-1 and ID-switch. We also adopt the recently-introduced higher-order HOTA [28], to replace MOTA and to represent the overall tracking accuracy when comparing self-supervised methods.
Robust and Fast Detection of Moving Vechiles in Aerial Videos Using Sliding Windows in MATLAB

— Intelligent Transportation Systems (ITS) permit us to have high exceptional site visitors facts to lessen the danger of probably vital situations. Conventional image-primarily based site visitors detection strategies have difficulties obtaining precise photographs due to attitude and historical past noise, negative lights and weather conditions. In this paper, we suggest a brand new method to correctly phase and track cars. After disposing of perspective the use of Modified Inverse Perspective Mapping (MIPM), Hough remodel is carried out to extract avenue lines and lanes. Then, Gaussian Mixture Models (GMM) are used to segment transferring items and to tackle vehicle shadow results, we observe a chromacity-based totally strategy. Finally, performance is evaluated via three one of a kind video benchmarks: personal recorded videos in Madrid and Tehran (with distinct weather situations at urban and interurban regions); and two famous public datasets (KITTI and DETRAC). Our effects imply that the proposed algorithms are strong, and greater accurate in comparison to others, mainly when going through occlusions, lighting fixtures versions and climate situations.
EMOTION RECOGNITION BY ANALYSING HUMAN VOICE

: Emotion recognition is that a part of speech recognition which is gaining more popularity and wish for it increases enormously. Although there are methods to know emotion using machine learning techniques, this project attempts to use deep learning and image classification method to acknowledge emotion and classify the emotion consistent with the speech signals. Our proposed model outperforms previous state-of-the-art methods in assigning data to at least one of 4 emotion categories (i
Experimental verification of intersatellite clock synchronization at LISA performance levels

The Laser Interferometer Space Antenna (LISA) aims to observe gravitational waves in the mHz regime over its 10-year mission time. LISA will operate laser interferometers between three spacecrafts. Each spacecraft will utilize independent clocks which determine the sampling times of onboard phasemeters to extract the interferometric phases and, ultimately, gravitational wave signals. To suppress limiting laser frequency noise, signals sampled by each phasemeter need to be combined in postprocessing to synthesize virtual equal-arm interferometers. The synthesis in turn requires a synchronization of the independent clocks. This article reports on the experimental verification of a clock synchronization scheme down to LISA performance levels using a hexagonal optical bench. The development of the scheme includes data processing that is expected to be applicable to the real LISA data with minor modifications. Additionally, some noise coupling mechanisms are discussed.
Inductive output tube (IOT) – a review

This study is all about the evolution of inductive output tube (IOT) and its significance in the current scenario. The historical review with timeline is briefly discussed. The basic design considerations and operating principle of IOT have been described. This tube is opted for many applications these days over other conventional vacuum tubes for various advantages. Initially used for television transmitters, this tube is now well recognized for other industrial applications also which are discussed in the study. A comparative review of IOT over other conventional tubes is also given wherever required. The current state of the art of the tube is described to convey the present needs of the device.
Improved 2D Keypoint Detection in Out-of-Balance and Fall Situations - combining input rotations and a kinematic model

Injury analysis may be one of the most beneficial applications of deep learning based human pose estimation. To facilitate further research on this topic, we provide an injury specific 2D dataset for alpine skiing, covering in total 533 images. We further propose a post processing routine, that combines rotational information with a simple kinematic model. We could improve detection results in fall situations by up to 21% regarding the PCK@0.2 metric.