Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach for Data Efﬁcient Action Recognition and Mitigating Scene Bias

Datasets such as UCF101 and Kinetics have inherent scene bias; this is demonstrated by the fact that scene classiﬁers score around 60% on UCF101. This bias hinders the ability of networks to accurately detect actions, especially in more of a real world sense. There are datasets available, such as HACS, that have action and non-action clips to help train networks in a way that mitigates this bias. However, HACS is extremely large and difﬁcult to obtain off of YouTube. These problems lead to the topics of this paper. We propose data-efﬁcient methods for action recognition as well as mitigating scene bias. These methods utilize varying spatio-temporal resolutions to increase results and decrease memory requirements. The results won the VIPriors workshop action recognition competition, and increase accuracy when training models from scratch on UCF101.
Analysis of Gaze Trajectories in Natural Reading with Hidden Markov Models

The process of natural reading, registered with a modern eye-tracking system generates a signal of complicated structure that can be considered as a time series consisting of gaze point coordinates. Signal properties are supposed to depend on various properties of presented text as well as on current cognitive condition of a reader such as attention focus, level of fatigue, level of text understanding and other parameters. The task of cognitive state recognition can be approached with the modeling of gaze trajectories using probabilistic models, which parameters may contain information relevant to read text properties and reader’s cognitive state. In this work a new approach of gaze trajectories modeling based on Hidden Markov Models is proposed. HMM’s transition probability matrix corresponds to probabilities of saccades between words and emission probability functions correspond to words coordinates and over-all measurement noise. Two variants of HMM are proposed: text-related HMM models multiple gaze trajectories collected on the same text from diﬀerent readers, subject-related parametric HMM models gaze trajectories produced by a single reader on a set of consecutive pages from the same text. A series of experiments on simulated data were performed to estimate a required sample size and a required level of measurement accuracy for a forthcoming data collection procedure.
Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness

Frozen fish usually have a much lower market price than fresh fish, hence, adulteration could occur. This paper focuses on the use of mid-infrared (MIR) spectroscopy combined with chemometric tools to determine whether fish has been frozen-thawed. A total of twenty-four fish samples (fresh fish (n = 12) and frozen-thawed fish (n = 12)) were investigated. The principal component analysis and the factorial discriminant analysis were applied to the three spectral regions in the MIR of 3000-2800 cm -1 , 1700-1500 cm -1 and 1500-900 cm -1 . Within the 1500-900 cm -1 spectral region, correct classification (CC) of 100% and 75% was observed for the calibration and validation spectra, respectively. Improved classification was obtained from the 3000-2800 cm -1 spectral region, with CC of 100% and 87.5% of the calibration and validation spectra, respectively. It can be concluded that the 3000-2800 cm -1 and 1500-900 cm -1 spectral regions may provide useful fingerprints allowing the differentiation between fresh and frozen-thawed fish. These regions can be considered as a reliable indicator of fish freshness.
Cluster Head Selection using Fuzzy based routing protocol in Wireless Sensor Network

: The wireless sensor networks is the type of network in which sensor nodes sense the network environmental conditions and pass the sensed information to the base station. The sizes of the sensor nodes are very small, due to which battery of sensor nodes is very limited. In the recent times various techniques has been proposed to reduce energy consumption of the sensor networks. The clustering is the most efficient technique which increase network lifetime. The clustering protocol, divide the network into fixed size clusters using location based clustering. In each cluster, cluster heads are selected on the basis of energy consumption and distance. In the various existing scenarios, techniques of LEACH had been applied for the selection of most efficient cluster head in the network. The technique of fuzzy logic may increase the routing overhead in the network. In this research, fuzzy logic based LEACH protocol will be proposed to reduce packet overhead and energy consumption in the network
Breast Cancer Detection From a Urine Sample by Dog Sni �

Shoko Kure (  skure@nms.ac.jp ) Department of Integrated Diagnostic Pathology, Nippon Medical School, 1-1-5 Sendagi, Bunkyoku, Tokyo 113-8602, Japan Shinya Iida Nippon Medical School Chiba Hokusoh Hospital, Department of Breast Oncology Marina Yamada Nippon Sport Science University, Faculty of Medical Science Hiroyuki Takei Nippon Medical School Hospital, Department of Breast Surgery Naoyuki Yamashita Jizankai Medical foundation Tsuboi Cancer Center Hospital Yuji Sato St. Sugar Canine Cancer Detection Training Center Masao Miyashita Nippon Medical School and Twin Peaks Laboratory of Medicine
Evaluating Security of Executable Steganography for Digital Software Watermarking

Man-at-the-end (MATE) attacks against software programs are difficult to protect. Adversaries have complete access to the binary program and can run it under both static and dynamic analysis to find and break any software protection mechanisms put in place. Even though full-proof protection is not possible practically or theoretically, the goal of software protection should be to make it more difficult for an adversary to find program secrets by increasing either their monetary cost or time. Protection mechanisms must be easy to integrate into the software development lifecycle, or else they are of little to no use. In this paper, we evaluate the practical security of a watermarking technique known as Weaver, which is intended to support software watermarking based on a new transformation technique called executable steganography. Weaver allows hiding of identification marks directly into a program binary in a way that makes it difficult for an adversary to find and remove. We performed instruction frequency analysis on 106 programs from the GNU coreutils package to understand and define Weaver’s limitations and strengths as a watermarking technique. Our evaluation revealed that the initial prototype version of Weaver suffers from limitations in terms of standard benchmarks for steganography evaluation, such as its stealth. We found that this initial prototype of Weaver relied heavily on one type of instruction that does not frequently occur in standard programs, namely the mov instruction with an 8-byte immediate operand. Our instruction frequency analysis revealed a negative impact due to Weaver’s over-reliance on this mov instruction.
Computational study of the strong binding mechanism of SARS-CoV-2 spike and ACE2

The spike protein of SARS-CoV-2 (SARS-CoV-2-S) helps the virus attach to and infect human cells. With various computational methods applied in this work, the accessibility of its RBD to ACE2, its key residues for stronger binding to ACE2 than the SARS-CoV spike (SARS-CoV-S), the origin of the stronger binding, and its potential sites for drug and antibody design were explored. It was found that the SARS-CoV-2-S could bind ACE2 with an RBD-angle ranging from 52. 2º to 84. 8º, which demonstrated that the RBD does not need to fully open to bind ACE2. Free energy calculation by an MM/GBSA approach not only revealed much stronger binding of SARS-CoV-2-S to ACE2 (ΔG=-21.7~-29.9 kcal/mol) than SARS-CoV-S (ΔG=-10.2~-16.4 kcal/mol) at different RBD-angles but also demonstrated that the binding becomes increasingly stronger as the RBD-angle increases. In comparison with the experimental results, the free energy decomposition disclosed more key residues interacting strongly with ACE2 than with the SARS-CoV-S, among which the Q493 might be the decisive residue variation (-5.84 kcal/mol) to the strong binding. With the mutation of all 18 different residues of SARS-CoV-S on the spike-ACE2 interface to the corresponding residues of SARS-CoV-2-S, it was found that the mutated SARS-CoV-S has almost the same binding affinity as SARS-CoV-2-S to ACE2, demonstrating that the remaining mutations outside the spike-ACE2 interface have little effect on its binding affinity to ACE2. Simulation of the conformational change pathway from “down” to “up” states disclosed 5 potential ligand-binding pockets correlated to the conformational change. Taking together the key residues, accessible RBD-angle and pocket correlation, potential sites for drug and antibody design were proposed, which should be helpful for interpreting the high infectiousness of SARS-CoV-2 and for developing a cure.
An Energy-Preserving Scheme for the Coupled Gross-Pitaevskii Equations

An energy-preserving scheme is proposed for the coupled Gross-Pitaevskii equations. The scheme is constructed by high order compact method in the spatial direction and average vector field method in the temporal direction, respectively. The scheme is energy-preserving, stable, and of sixth order in space and of second order in time. Numerical experiments verify the theoretical results. The dynamic behavior modeled by the coupled Gross-Pitaevskii equations is also numerically investigated. AMS subject classifications: 65M06, 65M12, 65Z05, 70H15
The Effect of Age on Performance in Marathon Running

This paper presents a systematic study of how running times in foot races vary with age. Most existing methods are based either on ad hoc comparisons of race results in different age groups, or so-called age-graded performances which are based on world records. I argue that a more appropriate methodology is based on the individual performances of runners who have run races over many years. Using archived online results from the Boston Marathon, I have assembled a database of 750 runners [exact number to be inserted later] who have run the Boston marathon at least six times in the case of men or five times for women. Statistical analysis of these results leads us to an age profile for all runners combined. The analysis is carried out separately for men and women, and can be further subdivided in various ways, for instance, constructing different age-performance curves for different ability groups. The results are compared both with current Boston qualifying times and with the USATF age-graded tables.
First principles calculations on CeO2 doped with Tb ions

The atomic and electronic structure of CeO2 doped with Tb has been calculated from first principles with inclusion of strong correlation effects on the basis of Hubbard model (GGA+U). The two values of Hubbard U-parameter were applied separately on Ce and Tb ions, in order to treat correctly two oxidation states of Tb (3+ and 4+). Crystal structure distortion is also discussed for Tb3+ ions in ceria without oxygen vacancies. The corresponding total energy difference between the 3+ and 4+ states is very small and, thus, these states can co-exist without oxygen vacancy formation (unlike Gd doping). Multiple configurations have been obtained with localization of electrons on different number of cations, if the Tb ion has an oxygen vacancy nearby. A site symmetry approach has been successfully applied to identify the ground state configuration. Gibbs formation energy of oxygen vacancy due to Tb doping is reduced by almost a factor of four, in comparison with the pure CeO2. The dependence of Gibbs formation energy on the temperature and oxygen partial pressure is discussed. It has been also shown that the lowest formation energy for the small polaron occurs when the Ce and Tb ions are located as nearest neighbors to oxygen vacancy. The results obtained are compared with the existing literature data from the electrical conductivity and optical measurements.
Evidence Estimation by Kullback-Leibler Integration for Flow-Based Methods

where p(y|x) is the likelihood of the data, and p0(x) is the prior. For simplicity we drop the conditioning on y and write only p(x) := p(x|y) for the posterior. The evidence Z = p(y) is an important quantity for both model comparison and hyper-parameter optimization, but computing it is typically intractable for interesting problems. There is a large class of methods to estimate the evidence, including advanced methods like Thermodynamic Integration (TI) (Gelman and Meng, 1998; Lartillot and Philippe, 2006) or Bayesian quadrature (O’Hagan, 1991; Rasmussen and Ghahramani, 2003). Most of these techniques are standalone methods and do not compute nor approximate the posterior. On the other hand, many methods, such as Variational Inference (VI) aim to find the best posterior approximation. We are particularly interested in a class of approaches, which we refer as Flow Based Variational Inference, where the optimization of the variational distribution can be described as an Ordinary Differential Equation (ODE), we give a precise definition in Section 2.1. In this work we show that for flow based VI we can approximate the posterior p(x) and estimate the evidence Z at the same time. More specifically we give a general approach on the computation of the log evidence for flow based methods. We illustrate our approach by using the Stein Variational Gradient Descent (SVGD) (Liu and Wang, 2016) algorithm and show preliminary results on toy problems.
TAXONOMIC DYSBIOSIS OF GUT MICROBIOTA AND SERUM BIOMARKERS REFLECT SEVERITY OF CENTRAL NERVOUS SYSTEM INJURY

The term “chronic critical illness” (CCI) refers to patients with prolonged dependence on intensive care. In most patients, CCI is triggered by severe brain injury. Ever more studies researching the microbiota in pathologic conditions are published every year, but a lot is yet to be elucidated about the composition of the gut microbiota in CCI. The aim of this study was to investigate possible correlations between changes in the taxonomic abundance of the gut microbiota, levels of proinflammatory and neurological serum biomarkers and the severity of central nervous system injury in patients with CCI. Our prospective observational pilot study included 29 patients with CCI. Using real-time PCR allowed us to detected changes in the taxonomic abundance of the gut microbiota. The correlation analysis of serum biomarkers and the taxonomic composition of the gut microbiota revealed statistically significant correlations between cortisol levels and the abundance of F. prausnitzii (r = ‒0.62; p < 0.05) and B. thetaiotaomicron (r = ‒0.57; p < 0.05) in vegetative state patients; between the CRP/albumin ratio and the abundance of S. aureus (r = 0.72; p < 0.05); between the abundance of B. fragilis group/F. prausnitzii and S100 levels (r = 0.45; p <0.05) in conscious patients; between Glasgow coma scale scores and the abundance of Enterococcus spp. (r = ‒0.77; p <0.05) in both groups. Thus, the association between the changes in the taxonomic composition of the gut microbiota and the severity of neurologic deficit can be evaluated using PCR-based diagnostic techniques and blood serum biomarkers. This approach will help to optimize antibacterial treatment regimens and/or develop alternative strategies to minimize the aggressive effect of antibiotics on the gut microbiota.
Alternative Management Paradigms for the Future of the Colorado and Green Rivers

Our ability to sustainably manage the Colorado River is clearly in doubt. The Bureau of Reclamation’s 2012 Water Supply and Demand Study demonstrated the precarious balance that currently exists between water supply and the amount consumptively used by society. A future with either declining water supplies or additional consumptive uses will undoubtedly upset this balance. This balance is threatened, because:
Running head: SEMANTIC RELATEDNESS AND BINDING 1 Semantic Relatedness Corrects the Age-Related Binding Deficit in Working Memory and Episodic Memory

Word count: 185/200 Objectives: It is well-known that age differentially impacts aspects of long-term episodic memory (EM): Whereas a binding deficit indicates that older adults are less capable than younger adults to encode or retrieve associations between information (e.g., the pairing between two memoranda, such as lock – race), item memory is relatively intact (e.g., recognizing lock without its original pairing). Method: We tested whether this deficit could be corrected by facilitating establishment of the bindings in working memory (WM) through adapting the semantic relatedness of studied pairs according to participants’ ongoing performance (Experiments 1 and 2). We also examined whether this was evident for the long-term retention of pairs that were not tested in WM (Experiment 2). Results: The results revealed matched binding and item memory in WM and EM between age groups. Most importantly, older adults required increased semantic strength between word pairs to achieve similar performance to that of younger adults, regardless of whether pairs were immediately tested during the WM task. Discussion: These findings indicate that relying on their superior semantic memory can correct the commonly exhibited profound deficit in binding memory in older age.
Microsoft Word-Paper5_Final

Modern-day world is facing problems such as, electricity generation deficiency, mounting energy demand, GHG (Greenhouse Gas) emissions, reliability and soaring prices. To resolve these issues, sustainable and renewable energy resources like SPV (Solar Photovoltaic) would be quite helpful. In this regard, the extraction of maximum power from SPV array in PSC (Partial Shading Weather Conditions) remains a challenge. Creation of multiple power peaks in the P-V (Power-Voltage) curve of a PV array due to partial shading, makes it difficult to track GMPP (Global Maximum Power Point) out of multiple power peaks known as LMPP (Local Maximum Power Points). Conventional algorithms are not able to perform in any condition other than UWC (Uniform Weather Condition). Nature inspired SC (Soft Computing) algorithms efficiently track the GMPP in PSC. The top performing SC algorithm named, FPA (Flower Pollination Algorithm) presents an efficient solution for GMPP tracking in PSCs. In this paper, the efficiency, accuracy and tracking speed of FPA algorithm is optimized. Comparison of the proposed OFPA (Optimized Flower Pollination Algorithm) and the existing FPAs is performed for zero shading condition, weak PSC, strong PSC, and changing weather conditions. In zero shading conditions, improvement of 0.7% in efficiency and 33% in tracking speed is achieved. In weak shading conditions, improvement of 0.97% in efficiency and 32.2% in tracking speed is achieved. In strong shading conditions, improvement of 0.24% in efficiency and 30.6% in tracking speed is achieved. OFPA is also tested for changing weather conditions (entering from Case-1 to Cae-3) and it retains its outstanding performance in the changing weather conditions. Simulations are performed in MATLAB/Simulink.
Thermal Performance Analysis of the Charging/Discharging Process of a Shell and Horizontally Oriented Multi-Tube Latent Heat Storage System

In this study, the thermal performance of latent heat thermal energy storage system (LHTESS) prototype to be used in a range of thermal systems (e.g., solar water heating systems, space heating/domestic hot water applications) is designed, fabricated, and experimentally investigated. The thermal store comprised a novel horizontally oriented multitube heat exchanger in a rectangular tank (forming the shell) filled with 37.8 kg of phase change material (PCM) RT62HC with water as the working fluid. The assessment of thermal performance during charging (melting) and discharging (solidification) was conducted under controlled several operational conditions comprising the heat transfer fluid (HTF) volume flow rates and inlet temperatures. The experimental investigations reported are focused on evaluating the transient PCM average temperature distribution at different heights within the storage unit, charging/discharging time, instantaneous transient charging/discharging power, and the total cumulative thermal energy stored/released. From the experimental results, it is noticed that both melting/solidification time significantly decreased with increase HTF volume flow rate and that changing the HTF inlet temperature shows large impacts on charging time compared to changing the HTF volume flow rate. During the discharging process, the maximum power output was initially 4.48 kW for HTF volume flow rate of 1.7 L/min, decreasing to 1.0 kW after 52.3 min with 2.67 kWh of heat delivered. Based on application heat demand characteristics, required power levels and heat demand can be fulfilled by employing several stores in parallel or series.
Classical and 3D-QSAR studies of cytochrome 17 inhibitor imidazole-substituted biphenyls

Classical and 3D-quantitative structure–activity relationship (3D-QSAR) studies have been performed for a series of imidazole-substituted biphenyl derivatives (n = 28) having 17α-hydroxylase/17-20-lyase (CYP17) inhibitory activity. 3D-QSAR analyses were performed using molecular shape analysis (MSA), receptor surface analysis (RSA) and molecular field analysis (MFA). The chemometric tools used for the analysis are genetic function approximation (classical QSAR and MSA) and genetic partial least squares (MFA and RSA). The developed QSAR models suggest that the presence of an imidazole group at the para position of the biphenyl ring is favourable for the inhibitory activity. The MSA-derived model indicates the importance of different shadow parameters (Lx, Syz) as well as molecular rigidity. The MFA model suggests the interaction points near the phenyl ring and the imidazole ring, while the RSA model indicates the nature of the hypothetical receptor surface around the ring systems. The RSA-derived model was found to be the best model based on the highest internal (Q 2 = 0.932) predictive power as well as criteria (0.858). According to external prediction statistics ( = 0.876), the MFA-derived model outperforms other models. Overall, there should be small (–H, –OH, –NH2) hydrophilic hydrogen bond donor groups at the terminal phenyl ring (meta or para position) for optimum inhibitory activity.
Using Machine Learning Algorithms for Finding the Topics of COVID-19 Open Research Dataset Automatically

The COVID-19 Open Research Dataset (CORD-19) is a collection of over 400,000 of scholarly papers (as of January 11th, 2021) about COVID-19, SARS-CoV-2, and related coronaviruses curated by the Allen Institute for AI. Carrying out an exploratory literature review of these papers has become a time-sensitive and exhausting challenge during the pandemic. The topic modeling pipeline presented in this thesis helps researchers gain an overview of the topics addressed in the papers. The preprocessing framework identifies Unified Medical Language System (UMLS) entities by using MedLinker, which handles Word Sense Disambiguation (WSD) through a pretrained Bidirectional Encoder Representations from Transformers (BERT) model. The topic model used in this research is a Variational Autoencoder implementing ProdLDA, which is an extension to the Latent Dirichlet Allocation (LDA) model. Applying the pipeline to the CORD19 dataset achieved a topic coherence value of 0.7 and topic diversity measures of almost 100%.