Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental study and parametric analysis for primary frequency regulation of hydropower unit

With the integration of new large-scale energy sources into grids, power system stability is facing huge challenges. The primary frequency regulation of hydropower generators is an important regulation method for grid frequency stability and power balance. It can effectively suppress the grid frequency fluctuations caused by the high proportion of new energy sources, and it plays a very important role in maintaining the stability of the power system. The regulation parameters of the primary frequency regulation of a hydropower unit have a significant impact on its regulation capabilities and performance indices. The experimental analysis of the effects of the regulation parameters is crucial for the safe and stable operation of a hydropower unit. A frequency regulation parameter test on a hydropower station was conducted in this study to determine the effects of the regulation parameters on the performance of the primary frequency regulation. This provides a reference for the selection of the regulation parameters of a hydropower unit.
The suitability of Technology, Organization and Environment (TOE) and Socio Technical System (STS) for assessing IT Hardware Support Services (ITHS) Model.

IT support services are the crucial part in daily business operation services in any organization. Special attention is needed, especially in managing users or client satisfaction when using the IT services to address the user’s problem or request IT hardware issues. Service quality (SQ) dimension is used to measure the effectiveness in delivering IT service support hardware to users. To strengthen the elements of the ITHS (IT Hardware Support Services), information system theories (IS) are used to harmonize the IT environment especially on the service support related to the IT hardware component. Two such theories taken from the IS environment are: Technology, Organization and Environment (TOE) and Social Technical System (STS). In this model, discussion is on the suitability of the above theories to assess ITHS, ability to achieve the service quality that reflect back to the users, client, and organization in achieving the service performance based on the SERVQUAL dimension.
Comparative Analysis in Execution of Machine Learning in Breast Cancer Identification: A Review

Carcinoma known as breast cancer is a significant common cancer among women worldwide. In line with the global trends, it accounts for many new cancer cases and cancer-related deaths, giving it a substantial public health issue in today’s culture. Early diagnosis is the most effective method to reduce the number of deaths in patients with breast cancer. Effective and early diagnosis of breast cancer ensure like mammography or biopsy to ensure the long-term survival of affected patients. Several conflicts arise in using traditional approaches, such as overdiagnosis or under-diagnosis. Machine learning is used to overcome the issues where it can strengthen the current conventional diagnosing of patients with breast cancer. The application of the classification method for diagnosing breast cancer is reviewed in this paper. Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbour (KNN), Decision Tree, Artificial Neural Network (ANN), and logistic regression are six methods presented in the review. These techniques are integrated with conventional methods, often allow physicians to diagnose breast cancer effectively. In summary, machine learning improvises in diagnosing breast cancer in terms of accuracy, sensitivity, and specificity with excellent performance and quality of patients.
Meta-Analysis of Adipose Tissue Derived Cell-Based Therapy for the Treatment of Knee Osteoarthritis

Osteoarthritis (OA) is a degenerative disorder associated with cartilage loss and is a leading cause of disability around the world. In old age, the capacity of cartilage to regenerate is diminished. With an aging population, the burden of OA is set to rise. Currently, there is no definitive treatment for OA. However, cell-based therapies derived from adipose tissue are promising. A PRISMA systematic review was conducted employing four databases (MEDLINE, EMBASE, Cochrane, Web of Science) to identify all clinical studies that utilized adipose tissue derived mesenchymal stem cells (AMSCs) or stromal vascular fraction (SVF) for the treatment of knee OA. Eighteen studies were included, which met the inclusion criteria. Meta-analyses were conducted on fourteen of these studies, which all documented WOMAC scores after the administration of AMSCs. Pooled analysis revealed that cell-based treatments definitively improve WOMAC scores, post treatment. These improvements increased with time. The studies in this meta-analysis have established the safety and efficacy of both AMSC therapy and SVF therapy for knee OA in old adults and show that they reduce pain and improve knee function in symptomatic knee OA suggesting that they may be effective therapies to improve mobility in an aging population.
ExpRace: Exploiting Kernel Races through Raising Interrupts

A kernel data race is notoriously challenging to detect, re-produce, and diagnose, mainly caused by nondeterministic thread interleaving. The kernel data race has a critical security implication since it often leads to memory corruption, which can be abused to launch privilege escalation attacks. Interestingly, due to the challenges above, the exploitation of the kernel data race is also challenging. Specifically, we find that some kernel races are nearly impossible to exploit due to their unique requirement on execution orders, which are almost impossible to happen without manual intervention. This paper develops a generic exploitation technique for kernel data races. To this end, we first analyze kernel data races, which finds an intrinsic condition classifying easy-to-exploit and hard-to-exploit races. Then we develop E XP R ACE , a generic race exploitation technique for modern kernels, including Linux, Microsoft Windows, and MAC OS X. E XP R ACE turns hard-to-exploit races into easy-to-exploit races by manipulating an interrupt mechanism during the exploitation. According to our evaluation with 10 real-world hard-to-exploit races, E XP R ACE was able to exploit all of those within 10 to 118 seconds, while an exploitation without E XP R ACE failed for all given 24 hours.
Study Investigating thè Effect of E-Service Quality on Customer’s Satisfaction in Online Shopping

Customer satisfaction is derived from thè functionalities and values, a product or Service can provide. The current study aims to segregate thè dimensions of ordine Service quality and gather insights on its impact on web shopping. The trends of purchases have changed since thè advent of internet transactions. The mode of buying and selling is no more limited to thè boundaries of a city or state. The commercial activity is happening across borders of nations too, though, thè rural areas of India stili lack proper infrastructure or are not included in thè distribution networks of thè shopping portals. Customer satisfaction is thè most important aspect for online sellers, given to its vitality in growth & future of a company. The need for this research lies in understanding thè fact that how satisfied are thè newly added areas. The present study uses thè standardized scale Gefen (2003) to measure e-service quality and find out its impact on customer satisfaction. The researcher has established thè results on thè basis of robust analytical tests like correlation and regression. The outcomes of this study established a significant relationship between thè results of thè regression analysis and suggest thè factors that are applicable in current areas. This research will help marketers and organizations to gain more clientele and achieve more satisfied customers, hence retain them.
RET Regulates Human Medullary Thyroid Cancer Cell Proliferation through CDK5 and STAT3 Activation

Medullary thyroid cancer (MTC) is a neuroendocrine tumor that arises from the parafollicular C-cells, which produces the hormone calcitonin. RET is a transmembrane receptor protein-tyrosine kinase, which is highly expressed in MTC. Our previous studies reported that cyclin-dependent kinase 5 (CDK5) plays a crucial role in cancer progression, including MTC. However, the role of CDK5 in GDNF-induced RET signaling in medullary thyroid cancer proliferation remains unknown. Here, we investigated RET activation and its biochemically interaction with CDK5 in GDNF-induced medullary thyroid cancer proliferation. Our results demonstrated that GDNF stimulated RET phosphorylation and thus subsequently resulted in CDK5 activation by its phosphorylation. Activated CDK5 further caused STAT3 activation by its specific phosphorylation at Ser727. Moreover, we also found that GDNF treatment enhanced ERK1/2 and EGR1 activity, which is involved in p35 activation. Interestingly, we identified for the first time that CDK5 physically interacted with RET protein in MTC. Overall, our results provide a new mechanism for medullary thyroid cancer cell proliferation, suggesting that targeting CDK5 may be a promising therapeutic candidate for human medullary thyroid cancer in the near future.
Diversity of predatory arthropods in Bt and non-Bt cotton fields of Nalgonda district, Telangana state, India

The study was conducted to investigate diversity of predatory arthropods (Predatory insects and Spiders) in Bt cotton and non-Bt cotton fields during July 2018 to January 2019 at farmers field of Palem Village, Nalgonda District, T.S, India. A total 4768 individual arthropods on Bt cotton, 5232 individual arthropods on non-Bt cotton fields were collected with the help of sweep net and hand picking. The predatory arthropods were identified with the help of Guide on cotton pests and predators, Regional Agricultural Research Station PJTSAU Warangal and literature. Eleven predatory insects and two spiders in Bt cotton and thirteen predatory insects and three spiders in non-Bt cotton were recorded. The results indicated that minor differences found between the Bt and non-Bt cotton fields. Our findings conclude that Bt cotton may affect predatory arthropods indirectly through removal of eggs, larvae and pupa of insect pests that serve as food for predatory arthropods. Various diversity indices were measured.
Principal Component Discriminant Analysis: Face Recognition

ABSTRACT Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. The rapidly expanding research in face processing is based on the premise that information about a user’s identity, state, and intent can be extracted from images and that computers can then react accordingly, e.g., by knowing person’s identity, person may be authenticated to utilize a particular service or not. A first step of any face processing system is registering the locations in images where faces are present. However, face registration for whole database is a challenging task because of variability in scale, location, orientation (up-right, rotated), and pose (frontal, profile). Facial expression, occlusion, and lighting conditions also change the overall appearance of face. The Image registration algorithm will register all these images present in the database. The face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is to be implemented. Taking a pattern classification approach, each pixel in an image can be considered as a coordinate in a high-dimensional space. The advantage of this is that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing images will deviate from this linear subspace. Rather than explicitly modeling this deviation, project the image into a subspace in such a manner which discounts those regions of the face with large deviation. This is achieved by using dimension reduction techniques like Principal component analysis (PCA), Linear Discriminant analysis (LDA), Laplacian faces, and other modified approaches like A Priori Laplacian and PCDA.
Compost and Cover Crop Effects in Dryland Organic Wheat

Compost and Cover Crop Effects in Dryland Organic Wheat by Michael D. Deakin, Master of Science Utah State University, 2021 Major Professor: Dr. J. Earl Creech Department: Plant Soils and Climate Producers of dryland organic wheat (Triticum aestivum L.) in the western USA struggle to maintain adequate soil fertility due to the high cost of organic fertilizers and concerns over moisture use of cover crops. Low soil fertility results in decreased wheat yield and quality, and increased year-to-year variability in yield and quality based on weather. This study was conducted to measure the effects of, and interactions between, cover crop mixes and a one-time compost application on soil health and winter wheat yield and quality. The study was located on three adjacent certified organic wheat farms near Snowville, UT, each with a calcareous silt loam soil under conventional tillage in a wheat-fallow system. Four composted steer manure treatments (0, 12.5, 25, and 50 Mg ha-1) and three cover crop treatments (oat [Avena sativa]-pea [Pisum sativum] mixture, vetch [Vicia villosa]-pea mixture, and a mechanically tilled fallow control) were evaluated from fall 2014 to 2017. Treatments were arranged in a randomized complete block split-split plot with three replications, with each farm hosting a single replicate. In iv 2015, the oat-pea cover crop treatment decreased grain yield, while compost amendment caused yields to more than double. However, neither compost nor cover crops affected yield in 2017. The 2015 crop year was much drier than 2017, suggesting that grain yield and quality benefits may be dependent on other factors such as the precipitation patterns of a particular year. The vetch-pea cover crop treatment increased total soil N in 2017 only, and compost amendment increased nitrogen fixation in the vetch-pea cover crop in that year. Compost treatments increased soil N, P, K, and organic C, both years without affecting soil EC or pH long-term. Nitrogen fixing cover crops may be a viable option for producers to increase soil N when sufficient soil moisture is present, however may be detrimental in a dry year. Compost amendment was shown to increase soil nutrition for multiple years, although that may only translate into increased grain yield when prerequisite weather conditions are present. (68 pages)
Probability of Resolution of MUSIC and g-MUSIC: An Asymptotic Approach

In this article, the outlier production mechanism of the conventional Multiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival (DoA) estimation technique is investigated using tools from Random Matrix Theory (RMT). A general Central Limit Theorem (CLT) is derived that allows to analyze the asymptotic stochastic behavior of eigenvector-based cost functions in the asymptotic regime where the number of snapshots and the number of antennas increase without bound at the same rate. Furthermore, this CLT is used to provide an accurate prediction of the resolution capabilities of the MUSIC and the g-MUSIC DoA estimation method. The finite dimensional distribution of the MUSIC and the g-MUSIC cost function is shown to be asymptotically jointly Gaussian distributed in this asymptotic regime.
Flux of information in scale-free networks

Scale-free networks constitute a fast-developing field that has already provided us with important tools to understand natural and social phenomena. From biological systems to environmental modifications, from quantum fields to high energy collisions, or from the number of contacts one person has, on average, to the flux of vehicles in the streets of urban centres, all these complex, non-linear problems are better understood under the light of the scale-free network’s properties. A few mechanisms have been found to explain the emergence of scale invariance in complex networks, and here we discuss a mechanism based on the way information is locally spread among agents in a scale-free network. We show that the correct description of the information dynamics is given in terms of the q-exponential function, with the power-law behaviour arising in the asymptotic limit. This result shows that the best statistical approach to the information dynamics is given by Tsallis Statistics. We discuss the main properties of the information spreading process in the network and analyse the role and behaviour of some of the parameters as the number of agents increases. The different mechanisms for optimization of the information spread are discussed.
On the proper role of linguistically-oriented deep net analysis in linguistic theorizing

A lively research field has recently emerged that uses experimental methods to probe the linguistic behavior of modern deep networks. While work in this tradition often reports intriguing results about the grammatical skills of deep nets, it is not clear what their implications for linguistic theorizing should be. As a consequence, linguistically-oriented deep net analysis has had very little impact on linguistics at large. In this chapter, I suggest that deep networks should be treated as theories making explicit predictions about the acceptability of linguistic utterances. I argue that, if we overcome some obstacles standing in the way of seriously pursuing this idea, we will gain a powerful new theoretical tool, complementary to mainstream algebraic approaches.
Spoofing Generalization: When Can't You Trust Proprietary Models?

In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data. In particular, we study the power of a malicious agent whose goal is to construct a model g that fits its training data and nothing else, but is indistinguishable from an accurate model f. We say that g strongly spoofs f if no polynomial-time algorithm can tell them apart. If instead we restrict to algorithms that run in $n^c$ time for some fixed $c$, we say that g c-weakly spoofs f. Our main results are 1. Under cryptographic assumptions, strong spoofing is possible and 2. For any c>0, c-weak spoofing is possible unconditionally While the assumption of a malicious agent is an extreme scenario (hopefully companies training large models are not malicious), we believe that it sheds light on the inherent difficulties of blindly trusting large proprietary models or data.
Multi-Class Classification from Single-Class Data with Confidences

Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully learn a multi-class classifier from only data of a single class with a rigorous consistency guarantee when confidences (i.e., the class-posterior probabilities for all the classes) are available. Specifically, we propose an empirical risk minimization framework that is loss-/model-/optimizer-independent. Instead of constructing a boundary between the given class and other classes, our method can conduct discriminative classification between all the classes even if no data from the other classes are provided. We further theoretically and experimentally show that our method can be Bayes-consistent with a simple modification even if the provided confidences are highly noisy. Then, we provide an extension of our method for the case where data from a subset of all the classes are available. Experimental results demonstrate the effectiveness of our methods.
On matrices whose exponential is a P-matrix

A matrix is called a P-matrix if all its principal minors are positive. P-matrices have found important applications in functional analysis, mathematical programming, and dynamical systems theory. We introduce a new class of real matrices denoted EP . A matrix is in EP if and only if its matrix exponential is a P-matrix for all positive times. In other words, A ∈ EP if and only if the transition matrix of the linear system ˙ x = Ax is a P-matrix for any positive time t . We analyze the properties of this new class of matrices and describe an application of our theoretical results to opinion dynamics.
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs

We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming. Our algorithm non-trivially combines acceleration motions from convex optimization with coordinate power iteration and matrix factorization techniques. The algorithm is extremely simple to implement, and adds only a single extra hyperparameter -- momentum. We prove that our method admits local linear convergence in the neighborhood of the optimum and always converges to a first-order critical point. Experimentally, we showcase the merits of our method on three major application domains: MaxCut, MaxSAT, and MIMO signal detection. In all cases, our methodology provides significant speedups over non-convex and convex SDP solvers -- 5X faster than state-of-the-art non-convex solvers, and 9 to 10^3 X faster than convex SDP solvers -- with comparable or improved solution quality.
Homotopy Transfer and Effective Field Theory II: Strings and Double Field Theory

We continue our study of effective field theory via homotopy transfer of L∞$L_\infty$ ‐algebras, and apply it to tree‐level non‐Wilsonian effective actions of the kind discussed by Sen in which the modes integrated out are comparable in mass to the modes that are kept. We focus on the construction of effective actions for string states at fixed levels and in particular on the construction of weakly constrained double field theory. With these examples in mind, we discuss closed string theory on toroidal backgrounds and resolve some subtle issues involving vertex operators, including the proper form of cocycle factors and of the reflector state. This resolves outstanding issues concerning the construction of covariant closed string field theory on toroidal backgrounds. The weakly constrained double field theory is formally obtained from closed string field theory on a toroidal background by integrating out all but the ‘doubly massless’ states and homotopy transfer then gives a prescription for determining the theory's vertices and symmetries. We also discuss consistent truncation in the context of homotopy transfer.
Loki: Hardening Code Obfuscation Against Automated Attacks

Software obfuscation is a crucial technology to protect intellectual property. Despite its importance, commercial and academic state-of-the-art obfuscation approaches are vulnerable to a plethora of automated deobfuscation attacks, such as symbolic execution, taint analysis, or program synthesis. While several enhanced techniques were proposed to thwart taint analysis or symbolic execution, they either impose a prohibitive runtime overhead or can be removed by compiler optimizations. In general, they suffer from focusing on a single attack vector, allowing an attacker to switch to other more effective techniques, such as program synthesis. In this work, we present L OKI , an approach for code obfuscation that is resilient against all known automated deob-fuscation attacks. To this end, we deploy multiple techniques, including a generic approach to synthesize formally veriﬁed expressions of arbitrary complexity. Contrary to state-of-the-art approaches that rely on a few hardcoded generation rules, our expressions are more diverse and harder to pattern match against. Moreover, L OKI protects against previously unac-counted attack vectors such as program synthesis, for which it reduces the success rate to merely 19%. Overall, our design incurs signiﬁcantly less overhead while providing a much stronger protection level.