Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Stream Learning in IoT Environment: A Survey

Smart systems need to take faster decisions. Computation of stored data induces delays and renders a real time system useless. Sensors are not smart devices, they collect data at edge level of network. Uploading the data to cloud for processing is slow, a faster way to predict from the data collected is by processing it at the lower levels of network. Machine Learning concepts can be exploited to train the collaborating devices to represent such data for information exchange. This paper discusses background of possible research areas open for proposing decision making systems at lower layers of network hierarchy.
Challenge Studies with Selected Pathogenic Bacteria on Freshly Peeled Hamlin Orange

The survival and growth of Salmonella spp. , Escherichia coli O157:H7, Listeria monocytogenes, and Staphylococcus aureus on peeled Hamlin orange were examined. Fruits were peeled by infusing the peel with water to assist hand removal. The peeled oranges had an average pH of 6.0–6.5 at the surface and 3.8 in the juice. After surface inoculation, peeled fruits were incubated for up to 14 days. Growth was observed with all tested pathogens only at the abusive storage temperature (24°C). Refrigeration (4 or 8°C) effectively inhibited the growth of all pathogens and caused population reduction of Salmonella spp. and S. aureus.
Investigate The Relation Between Polymorphism of ( CTLA-4 ) Gene And Asthma in Some Patients at Thi-Qar Province / IRAQ

The present study was carried out at the Biology Department laboratories, for the period of (December2017 to November 2018). The aim of the current study was to investigate polymorphism of CTLA-4 genein patients with Asthma. The study subject consisted of patients with asthma (74 females) and (26 males)were their aged between (15-55) years, with (50 persons as control group. After DNA extraction, PCRwas made by using specific primers for the CTLA-4 gene, the study results explained the incidence ofpolymorphisms in a samples of 25 out of asthma patients when using the restriction enzyme (BSTEII). After analyzing the results by statistical analysis results showed that no association between the incidenceof Asthma and appearance of mutation in CTLA-4 gene when compared to the control group
Pancreatic pseudocyst: Dilemma of its recent management (Review)

Advances in radiological techniques have led to an increase in the number of diagnoses of pancreatic pseudocyst, which is the most common pancreatic cyst lesion disease, accounting for two-thirds of all pancreatic cyst lesions. Historically, the management of pancreatic pseudocyst has been achieved through the use of conservative treatments and surgery; however, due to the complications and recurrence rate associated with these techniques, the management of pancreatic pseudocyst is challenging. Surgeons and gastroenterologists have attempted to determine the optimal management technique to treat pancreatic pseudocyst to reduce complications and the recurrence rate. From these investigations, percutaneous catheter, surgical and endoscopic drainage with ultrasonography guidance have become promising management techniques. The present review aimed to summarize the diagnostic and therapeutic methods used for the management of pancreatic pseudocyst and to compare percutaneous catheter, surgical and endoscopic drainage.
A Fast Adaptive Time-delay-estimation Sliding Mode Controller for Robot Manipulators

A R T I C L E I N F O A B S T R A C T Article history: Received: 14 August, 2020 Accepted: 25 November, 2020 Online: 08 December, 2020 A nonlinear adaptive robust controller is proposed in this paper for trajectory-tracking control problems of robot manipulators. On one hand, to effectively approximate the systematic dynamics, a simple time-delay estimator is first adopted. On the other hand, to minimize the control error, the controller is designed based on a sliding mode structure using the obtained estimation results. A fast learning mechanism is then proposed for automatically tuning control gains. Another proper adaptation law is furthermore developed to support the nominal inertia-matrix selection of the time-delay estimation. Effectiveness of the closed-loop system is intensively discussed using Lyapunov-based constraints and extended simulation results.
Nauči me igrati-igrifikacijom do boljih rezultata

Sažetak Aktivna uloga učenika jedna je od glavnih smjernica suvremene nastave pa se zbog toga u nastavi potiče upotreba različitih vrsta igara. Primjena mehanizama, dizajna i elemenata igre u nastavi naziva se igrifikacija. Cilj istraživanja ovog rada bio je ispitati utječe li korištenje edukativne društvene igre potpomognute IKT elementima „Angles and the Dragon“ na motivaciju za učenje predmeta geografije i povijesti kod učenika sedmih i osmih razreda I. osnovne škole Varaždin. Igra je proizašla kao rezultat Erasmus KA2 projekta „Teach me how to play“. Iako nisu potvrđene sve tri postavljene hipoteze, dobiveni rezultati u ovome istraživanju jasan su pokazatelj pozitivnog utjecaja primjene igrifikacije u nastavi kao jedne od mogućih metoda poučavanja koja pridonosi motivaciji učenika i osuvremenjivanju nastavnog procesa.
Synergistic Effect of Nanocurcumin and Platelet Rich Plasma on Resolving Experimentally Induced Osteoarthritis in Albino Rats

| An efficient therapeutic approach for osteoarthritis (OA) is yet to be discovered. In the current study, we evaluated coadministration of nanocurcumin (NCur) and platelet rich plasma (PRP) as a potential therapeutic strategy for treating experimentally induced osteoarthritis. Thirty male albino rats weighing 180± 20 gm were divided into 5 groups, 6 rats each. The first group was injected with saline (Control group). To induce osteoarthritis in the other four groups, rats were injected with 2 mg of monosodium-iodoacetate (MIA) in a total volume of 50 ul of saline. All injections were done in the right knee joint through the infra-patellar ligament to induce osteoarthritis. Two weeks after injection of MIA, one group was left untreated (Osteoarthritic group) and another group received 200 mg/kg NCur through gastric intubation for 14 consecutive days. A fourth group was intra-articularly injected with 150 ul activated PRP. The final group was concurrently treated by both PRP injection and NCur gavage. Fifteen days post treatment, radiography and Mankin scoring of joint lesions beside serum inflammatory cytokines were analyzed. Lowest Mankin scores with normal radiographic picture of joint structure and normal serum levels of IL6, IL1β and TNFα were observed in rats concomitantly treated with PRP and NCur. It can be concluded that concurrently use of nanocurcumin and platelet rich plasma is a potential efficient therapy for osteoarthritis and can greatly restore joint structure and function.
DyClee-C: a clustering algorithm for qualitative data based diagnosis

Sensors are multiplying on machines, networks and living things. Reasoning and extracting knowledge from this huge amount of data is among nowadays challenges. In data-based diagnostic applications, large amounts of data are often available but a key issue is that data remain unlabelled because labelling would require too much time and imply prohibitive costs. The different situations, e.g. normal or faulty, must hence be learned from the data. Clustering methods, also qualified as unsupervised classification methods, can then be used to create groups of samples according to some similarity criterion. The different groups can supposedly be associated to different situations. Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to qualitative (categorical) data. However, in many application domains, qualitative features are key to properly describe the different situations. This paper presents DyClee-C, an extension of the numeric feature based DyClee algorithm to qualitative data. DyClee-C is applied to two data sets: a soybean data set to diagnose the disease soybean plants and a breast cancer data set to assess the current diagnosis in terms of recurrence events and prognose possible relapse.
Water quality problems in China

Owing to the rapid growth of population and intensive industrialization and urbanization during the last thirty years, water quailty has become an increasingly important problem and major national concern in China. Some features of the problem are presented, including its relation to population, industry, wastes and pollution. Some examples of attempts to protect the quality of the water are provided with a report on some achievements during the last decade.
Quantitative disorder effects in low-dimensional spin systems

The Imry-Ma phenomenon, predicted in 1975 by Imry and Ma and rigorously established in 1989 by Aizenman and Wehr, states that first-order phase transitions of low-dimensional spin systems are `rounded' by the addition of a quenched random field to the quantity undergoing the transition. The phenomenon applies to a wide class of spin systems in dimensions $d\le 2$ and to spin systems possessing a continuous symmetry in dimensions $d\le 4$. This work provides quantitative estimates for the Imry--Ma phenomenon: In a cubic domain of side length $L$, we study the effect of the boundary conditions on the spatial and thermal average of the quantity coupled to the random field. We show that the boundary effect diminishes at least as fast as an inverse power of $\log\log L$ for general two-dimensional spin systems and for four-dimensional spin systems with continuous symmetry, and at least as fast as an inverse power of $L$ for two- and three-dimensional spin systems with continuous symmetry. Specific models of interest for the obtained results include the two-dimensional random-field $q$-state Potts and Edwards-Anderson spin glass models, and the $d$-dimensional random-field spin $O(n)$ models ($n\ge 2$) in dimensions $d\le 4$.
Intrinsic Torsional Potential Parameters for Conformational Analysis of Peptides and Proteins

New sets of intrinsic torsional parameters, obtained by fitting to experimental and/or theoretical values of torsional barriers and relative conformational energies of various uncharged model organic compounds, are presented. They are intended for use in conformational energy computations on peptides and proteins. A three-term Fourier series expansion is used to represent the intrinsic torsional energy. Each set of intrinsic torsional parameters, obtained from a single model compound, reproduces experimental torsional barriers, relative conformational energies, and torsion angles of related molecules not used for the parametrization. The sets of parameters of a new potential function, including electrostatic interactions based on partial atomic charges, and nonbonded, hydrogen-bond, and intrinsic torsional energies, are tested in conformational energy calculations on a model peptide N-acetyl-N‘-methylalanineamide. The electrostatic energy component plays a significant role in the total conformational ener.
Small deviation estimates and small ball probabilities for geodesics in last passage percolation

For the exactly solvable model of exponential last passage percolation on Z, consider the geodesic Γn joining (0, 0) and (n, n) for large n. It is well known that the transversal fluctuation of Γn around the line x = y is n 2/3+o(1) with high probability. We obtain the exponent governing the decay of the small ball probability for Γn and establish that for small δ, the probability that Γn is contained in a strip of width δn 2/3 around the diagonal is exp(−Θ(δ−3/2)) uniformly in high n. We also obtain optimal small deviation estimates for the one point distribution of the geodesic showing that for t 2n bounded away from 0 and 1, we have P(|x(t)−y(t)| ≤ δn) = Θ(δ) uniformly in high n, where (x(t), y(t)) is the unique point where Γn intersects the line x+y = t. Our methods are expected to go through for other exactly solvable models of planar last passage percolation and, upon taking the n→∞ limit, provide analogous estimates for geodesics in the directed landscape.
Dry-Matter Partitioning across Parts of the Wheat Internode during the Grain Filling Period as Influenced by Fertilizer and Tillage Treatments

Dry-matter transportation and redistribution is an important factor influencing grain weight during the grain filling period. This study, carried out over two consecutive winter wheat growth seasons (2010/2011 and 2011/2012), examined how dry-matter partitioning across segments of the wheat internode during grain filling period is influenced by fertilizer and tillage treatments. The contributions of current assimilates and remobilization of dry matter to final grain weight were calculated. To precisely illustrate the changes in dry matter during the grain filling period, the whole wheat stem was divided into two parts, the top three part and bottom two part. The former included three segments: the exposed part of the uppermost internode (EXUI), the enclosed part of the uppermost internode (ENUI), and the penultimate internode (PI). The latter included two segments: the lower internode (ERI) and the lowest internode (ESTI). During postanthesis, two different phases characterized by different rates of dry-matter accumulation could be distinguished. The first phase was the dry-matter accumulation; the second phase was mobilization to grains. The bottom two parts contributed more dry matter during pre-anthesis to the final grain; on the other hand, the top three parts contributed more dry matter to the final grain during postanthesis. On average, for the bottom two parts, the ERI segment had the greatest contribution of pre-anthesis dry matter to grain weight (CRAP) (3.68% in 2010/2011 and 5.31% in 2011/2012 growth seasons). For the top three parts, the EXUI segment had the greatest contribution of postanthesis photosynthetic assimilate to grain weight (CAPA) (99.71% in 2010/2011 and 99.08% in 2011/2012 growth season). The top three parts achieved maximum dry weight at 15 days after anthesis (daa), 5 days earlier than the bottom two parts. The heavy rainfall in the late grain filling period of 2011/2012 growth season inhibited postanthesis dry mobilization, and to maintain the grain filling requirements, more dry matter stored during pre-anthesis was redistributed. The present results suggest that rainfall occurring late in the grain filling period may have adverse impact on postanthesis accumulation. To avoid this phenomenon, early sowing with plastic film cover and split nitrogen application may be recommended. Fertilizing with 195 kg hm−2 (120 + 75) N plus 45000 kg hm−2 of ox manure and 120 kg hm−2 phosphate with wheat straw retention during the summer fallow period under conservation minimum tillage produced the greatest stem dry matter at anthesis.
Equality Saturation for Tensor Graph Superoptimization

One of the major optimizations employed in deep learning frameworks is graph rewriting. Production frameworks rely on heuristics to decide if rewrite rules should be applied and in which order. Prior research has shown that one can discover more optimal tensor computation graphs if we search for a better sequence of substitutions instead of relying on heuristics. However, we observe that existing approaches for tensor graph superoptimization both in production and research frameworks apply substitutions in a sequential manner. Such sequential search methods are sensitive to the order in which the substitutions are applied and often only explore a small fragment of the exponential space of equivalent graphs. This paper presents a novel technique for tensor graph superoptimization that employs equality saturation to apply all possible substitutions at once. We show that our approach can find optimized graphs with up to 16% speedup over state-of-the-art, while spending on average 48x less time optimizing.
Fast flavor oscillations of astrophysical neutrinos with 1, 2, …, ∞ crossings

In the early Universe, as well as in supernovae and merging neutron stars, neutrinos have such high densities that they affect each other and exhibit collective flavor oscillations. A crucial ingredient for fast collective flavor oscillations is that the electron lepton number (ELN) distribution changes its sign as a function of direction, i.e., has a zero crossing. We present a study in two spatial dimensions and time, focussing on the fast and linear regime, and show how fast flavor oscillations depend on the ELN and its crossings. We show that a large number of crossings can inhibit flavor oscillations. This may be a natural self-limiting mechanism that stabilizes the flavor content of the dense neutrino gas in a vast majority of scenarios, especially the early Universe, where the angular distributions for all flavors are very similar and crossings occur mainly due to fluctuations.
Incremental learning with online SVMs on LiDAR sensory data

The pipelines transmission system is one of the growing aspects, which has existed for a long time in the energy industry. The cost of in-pipe exploration for maintaining service always draws lots of attention in this industry. Normally exploration methods (e.g. Magnetic flux leakage and eddy current) will establish the sensors stationary for each pipe milestone or carry sensors to travel inside the pipe. It makes the maintenance process very difficult due to the massive amount of sensors. One of the solutions is to implement machine learning techniques for the analysis of sensory data. Although SVMs can resolve this issue with kernel trick, the problem is that computing the kernel depends on the data size too. It is because the process can be exaggerated quickly if the number of support vectors becomes really large. Particularly LiDAR spins with an extremely rapid rate and the flow of input data might eventually lead to massive expansion. In our proposed approach, each sample is learned in an instant way and the supported kernel is computed simultaneously. In this research, incremental learning approach with online support vector machines (SVMs) is presented, which aims to deal with LiDAR sensory data only.
A Splitting-Detection Joint-Decision Receiver for Ultrasonic Intra-Body Communications

Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two streams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for $M$ -ary pulse position modulation ( $M$ -PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation.
Learning Sign-Constrained Support Vector Machines

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes $O(nd)$ computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires $O(nd)$ cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an $\epsilon$-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.
The Young Massive Star Cluster Westerlund 2 Observed with MUSE. III. A Cluster in Motion—The Complex Internal Dynamics

Analyzing the dynamical state of nearby young massive star clusters is essential for understanding star cluster formation and evolution during their earliest stages. In this work we analyze the stellar and gas kinematics of the young massive star cluster Westerlund 2 (Wd2) using data from the integral field unit Multi Unit Spectroscopic Explorer (MUSE) and complement them with proper motions from the Gaia DR2. The mean gas radial velocity of 15.9 km s−1 agrees with the assumption that Wd2 is the result of a cloud–cloud collision. The gas motions show the expansion of the H ii region, driven by the radiation from the many OB stars in the cluster center. The velocity profile of the cluster member stars reveals an increasing velocity dispersion with decreasing stellar mass and that the low-mass stars show five distinct velocity groups. Based on their spatial correlation with the cluster’s two clumps, we concluded that this is the imprint of the initial cloud collapse that formed Wd2. A thorough analysis of the dynamical state of Wd2, which determines a dynamical mass range of M dyn,Wd2 = (7.5 ± 1.9) × 104 − (4.4 ± 1.1) × 105 M ⊙ and exceeds the photometric mass by at least a factor of two, leads to the conclusion that Wd2 is not massive enough to remain gravitationally bound. Additionally we also identify 22 runaway candidates with peculiar velocities between 30 and 546 km s−1.
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time. However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of this challenge. This paper attempts to identify these trends and summarize the various approaches to the topic.
On the BPS spectrum of 5d SU(2) super-Yang-Mills

We propose a closed-form expression for the motivic Kontsevich-Soibelman invariant for M-theory in the background of the toric Calabi-Yau threefold KF0 . This encodes the refined BPS spectrum of SU(2) 5d N = 1 Yang-Mills theory on S1×R4, corresponding to rank-zero Donaldson-Thomas invariants for KF0 , anywhere on the Coulomb branch. January 7, 2021 ar X iv :2 10 1. 01 68 1v 2 [ he pth ] 6 J an 2 02 1
A New Formula for the Minimum Distance of an Expander Code

An expander code is a binary linear code whose parity-check matrix is the bi-adjacency matrix of a bipartite expander graph. We provide a new formula for the minimum distance of such codes. We also provide a new proof of the result that $2(1-\varepsilon) \gamma n$ is a lower bound of the minimum distance of the expander code given by a $(m,n,d,\gamma,1-\varepsilon)$ expander bipartite graph.