Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of Legged Balance Control to Uncertainties and Sampling Period

We propose to quantify the effect of sensor and actuator uncertainties on the control of the center of mass and center of pressure in legged robots, since this is central for maintaining their balance with a limited support polygon. Our approach is based on robust control theory, considering uncertainties that can take any value between specified bounds. This provides a principled approach to deciding optimal feedback gains. Surprisingly, our main observation is that the sampling period can be as long as 200 ms with literally no impact on maximum tracking error and, as a result, on the guarantee that balance can be maintained safely. Our findings are validated in simulations and experiments with the torque-controlled humanoid robot Toro developed at DLR. The proposed mathematical derivations and results apply nevertheless equally to biped and quadruped robots.
Variance Reduction for Matrix Games

We present a randomized primal-dual algorithm that solves the problem $\min_{x} \max_{y} y^\top A x$ to additive error $\epsilon$ in time $\mathrm{nnz}(A) + \sqrt{\mathrm{nnz}(A)n}/\epsilon$, for matrix $A$ with larger dimension $n$ and $\mathrm{nnz}(A)$ nonzero entries. This improves the best known exact gradient methods by a factor of $\sqrt{\mathrm{nnz}(A)/n}$ and is faster than fully stochastic gradient methods in the accurate and/or sparse regime $\epsilon \le \sqrt{n/\mathrm{nnz}(A)}$. Our results hold for $x,y$ in the simplex (matrix games, linear programming) and for $x$ in an $\ell_2$ ball and $y$ in the simplex (perceptron / SVM, minimum enclosing ball). Our algorithm combines Nemirovski's "conceptual prox-method" and a novel reduced-variance gradient estimator based on "sampling from the difference" between the current iterate and a reference point.
A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features

The explainability of Convolutional Neural Networks (CNNs) is a particularly challenging task in all areas of application, and it is notably under-researched in music and audio domain. In this paper, we approach explainability by exploiting the knowledge we have on hand-crafted audio features. Our study focuses on a well-defined MIR task, the recognition of musical instruments from user-generated music recordings. We compute the similarity between a set of traditional audio features and representations learned by CNNs. We also propose a technique for measuring the similarity between activation maps and audio features which typically presented in the form of a matrix, such as chromagrams or spectrograms. We observe that some neurons' activations correspond to well-known classical audio features. In particular, for shallow layers, we found similarities between activations and harmonic and percussive components of the spectrum. For deeper layers, we compare chromagrams with high-level activation maps as well as loudness and onset rate with deep-learned embeddings.
Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features

This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic character embeddings along with the conditions to corresponding pronunciations. We obtain the word-level condition from a pre-trained word-to-vector lookup table. One goal of polyphone disambiguation is to address the homograph problem existing in the front-end processing of Mandarin Chinese text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly available polyphonic character dataset. To further validate our choices on the conditional feature, we investigate polyphone disambiguation systems with multi-level conditions respectively. The experimental results show that both the sentence-level and the word-level conditional embedding features are able to attain good performance for Mandarin Chinese polyphone disambiguation.
Recurrent Network Classifier for Ultrafast Skyrmion Dynamics

By using the supervised learning we train a recurrent neural network to recognize and classify ultrafast magnetization processes realized in two-dimensional nanosystems with Dzyaloshinskii-Moriya interaction. Our focus is on the different types of skyrmion dynamics driven by ultrafast magnetic pulses. Each process is represented as a sequence of the sorted magnetization vectors inputted into the network. The trained network can perform an accurate classification of the skyrmionic processes at zero temperature in wide ranges of parameters that are the magnetic pulse width and damping factor. The network performance is also demonstrated on different types of unseen data including finite temperature processes. Our approach can be easily adapted for creating an autonomous control system on skyrmion dynamics for experiments or device operations.
Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing

Edit distance similarity search, also called approximate pattern matching, is a fundamental problem with widespread applications. The goal of the problem is to preprocess n strings of length d to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. A data structure solving this problem is analyzed using two criteria: the amount of extra space used in preprocessing, and the expected time to answer a query. Previous approaches to this problem have either used trie-based methods, which give exact solutions at the cost of expensive queries, or embeddings, which only work for large (superconstant) values of c. In this work we achieve the first bounds for any approximation factor c, via a simple and easy-to-implement hash function. This gives a running time of $\tilde{O}(d3^rn^{1/c})$, with space $\tilde{O}(3^r n^{1 + 1/c} + dn)$. We show how to apply these ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Quantum Thermodynamics

This book provides an introduction to the emerging field of quantum thermodynamics, with particular focus on its relation to quantum information and its implications for quantum computers and next generation quantum technologies. The text, aimed at graduate level physics students with a working knowledge of quantum mechanics and statistical physics, provides a brief overview of the development of classical thermodynamics and its quantum formulation in Chapter 1. Chapter 2 then explores typical thermodynamic settings, such as cycles and work extraction protocols, when the working material is genuinely quantum. Finally, Chapter 3 explores the thermodynamics of quantum information processing and introduces the reader to some more state-of-the-art topics in this exciting and rapidly developing research field.
Period 4 stripe in the extended two-dimensional Hubbard model

We study the competition between stripe states with different periods and a uniform d-wave superconducting state in the extended two-dimensional Hubbard model at 1/8 hole doping using infinite projected entangled-pair states (iPEPS). With increasing strength of negative next-nearest neighbor hopping t′ , the preferred period of the stripe decreases. For the values of t′ predicted for cuprate high-Tc superconductors, we find stripes with a period 4 in the charge order, in agreement with experiments. Superconductivity in the period 4 stripe is suppressed at 1/8 doping. Only at larger doping, 0.18 ≲ δ < 0.25, the period 4 stripe exhibits coexisting d-wave superconducting order. The uniform d-wave state is only favored for sufficiently large positive t′.
Acoustic Hawking radiation from an evolving horizon in a dynamical analogue spacetime

Our knowledge of dynamical black holes suffers from a lack of observational insight. In an analogue model of gravity, we can design a longitudinally symmetric dynamical acoustic black hole with a moving horizon. In this symmetric spacetime, the marginally outer trapped surface and the so called evolving horizon are degenerate. Interestingly, there are many ways of assigning a surface gravity to the horizon in the absence of time translation invariance. Here, we present two of them that are distinguished by whether they are defined only locally or take into account the global properties of spacetime outside the black hole. It is expected that a dynamical black hole would emit spontaneous thermal radiation and its temperature would be proportional to a surface gravity of the moving horizon but there is no consensus on which of the surface gravities would give the Hawking temperature. We propose that a non-stationary analogue spacetime when realised experimentally can possibly help resolve these mysteries and provide the first observational signatures of dynamical Hawking radiation.
A 100-MHz 4-Mb cache DRAM with fast copy-back scheme

A 4-Mb cache dynamic random access memory (CDRAM), which integrates 16-kb SRAM as a cache memory and 4-Mb DRAM into a monolithic circuit, is described. This CDRAM has a 100-MHz operating cache, newly proposed fast copy-back (FCB) scheme that realizes a three times faster miss access time over with the conventional copy-back method, and maximized mapping flexibility. The process technology is a quad-polysilicon double-metal 0.7- mu m CMOS process, which is the same as used in a conventional 4-Mb DRAM. The chip size of 82.9 mm/sup 2/ is only a 7% increase over the conventional 4-Mb DRAM. The simulated system performance indicated better performance than a conventional cache system with eight times the cache capacity. >
Generalized Proportional Allocation Policies for Robust Control of Dynamical Flow Networks

We study a robust control problem for dynamical flow networks. In the considered framework, traffic flows along the links of a transportation network—modeled as a capacited multigraph—and queues up at the nodes, whereby control policies determine which incoming queues are to be allocated service simultaneously, within some predetermined scheduling constraints. We first prove fundamental performance limitations on the system performance by showing that for a dynamical flow network to be stabilizable by some control policy, it is necessary that the exogenous inflows belong to a certain stability region, that is determined by the network topology, the link capacities, and the scheduling constraints. Then, we introduce a family of distributed controls, referred to as generalized proportional allocation (GPA) policies, and prove that they stabilize a dynamical transportation network whenever the exogenous inflows belong to such stability region. The proposed GPA control policies are decentralized and fully scalable as they rely on local feedback information only. Differently from previously studied maximally stabilizing control strategies, the GPA control policies do not require any global information about the network topology, the exogenous inflows, or the routing, which makes them robust to unpredicted network load variations and changes in the link capacities or the routing decisions. Moreover, the proposed GPA control policies also take into account the overhead time while switching between services. Our theoretical results find one application in the control of urban traffic networks with signalized intersections, where vehicles have to queue up at junctions and the traffic signal controls determine the green light allocation to the different incoming lanes.
SPECIAL ISSUE ON DESIGN-USE RELATIONSHIPS IN SOSIOTECHNICAL CHANGE

The “user” is an ambiguous concept in human-computer interaction and information systems. Analyses of users as social actors, participants, or configured users delineate approaches to studying design-use relationships. Here, a developer’s reference to a figure of speech, termed the “average user,” is contrasted with design guidelines. The aim is to create an understanding about categorization practices in design through a case study about the virtual community, Habbo Hotel. A qualitative analysis highlighted not only the meaning of the “average user,” but also the work that both the developer and the category contribute to this meaning. The average user a) represents the unknown, b) influences the boundaries of the target user groups, c) legitimizes the designer to disregard marginal user feedback, and d) keeps the design space open, thus allowing for creativity. The analysis shows how design and use are intertwined and highlights the developers’ role in governing different users’ interests.
“ MICROBIOLOGICAL PROFILE OF URINARY TRACT INFECTION AT A TERTIARY CARE HOSPITAL IN KANPUR ”

Urinary tract infection (UTI) is one of the most common bacterial infections in humans and a major cause of morbidity. UTI has become difficult to treat because of appearance of pathogens with increasing resistance to antimicrobial agents. Objective: We aimed to study the antibiotic resistance pattern of the urinary pathogens isolated from patients is our tertiary care hospital. Methods: A prospective study was done during Dec 2016 – May2017at Rama medical college hospital, Kanpur. The study included all the adult patients of OPD & IPD, with symptoms of UTI, urine culture and sensitivity was carried out by standard microbiological procedures. Results: Out of 450 urine samples significant bacteriuria was seen in 34.66%. Female were 96(61.5%) and male were 60(38.4%) the incidence of UTI was more in patients in the age group of 20-40 years. E coli (42.3%) was the most common organism, followed by Klebsiella sps(23.1%), Staphylococcus(15.3%), Proteus mirabilis(7.6%),Citrobacter(3.8%), Enterococcus (3.8%), Candida albican(3.8%). E coli and Klebsiella were resistant to Amikacin (75.7% & 83.3%) respectively. More than 90% of strains were resistant to Ampicillin.GNB showed better sensitivity to Nitrofurantoin and Imipenam. All the isolates were 100% sensitive to polymyxins.15. 15% and 33.3% of E.coli and Klebsiella sps were ESBL producers. 50% of the S.aureus were methicillin resistant. Conclusion: In this study, female were mostly affected and the most common organism were E.coli and Klebsiella sps, These isolates are sensitive to nitrofurantoin, imipenem and polymyxins, and resistant to the most commonly used drugs like ampicillin, amikacin used in UTI. Regular monitoring are required to establish reliable information about resistance pattern of urinary pathogens for optimal empirical therapy of patients with UTIs.
Towards a Virtual Assistant Health Coach : Corpus Collection and Annotations

Itika Gupta Department of Computer Science University of Illinois at Chicago igupta5@uic.edu Barbara Di Eugenio Department of Computer Science University of Illinois at Chicago bdieugen@uic.edu Brian Ziebart Department of Computer Science University of Illinois at Chicago bziebart@uic.edu Bing Liu Department of Computer Science University of Illinois at Chicago liub@uic.edu Ben Gerber Institute for Health Research and Policy University of Illinois at Chicago bgerber@uic.edu Lisa Sharp Institute for Health Research and Policy University of Illinois at Chicago sharpl@uic.edu
EFFECTS OF TiO 2 NANOPARTICLES ON MAIZE ( ZEA MAYS L . ) GROWTH , CHLOROPHYLL CONTENT AND NUTRIENT UPTAKE

Nanotechnology, as the source of new industrial materials, has been widely used in recent years and has appeared in almost every field. Nowadays, nanoparticles are used in many areas like textiles, cosmetics, pharmaceuticals, automotive and defense (military) industries. The usage of nanoparticles has increased in recent years, but their effects on the environment are not exactly known. In this study, TiO2 nanoparticle (TiO2-NP) was applied at increasing concentrations (0, 5, 10 and 20 mg L ) to maize (Zea mays L.) plant grown in hydroponic culture at 16 h light/8 h dark, 25/20 °C temperature, 60% humidity and 10 Klux light intensity for 20 days. Application of TiO2-NP on biomass production, nitrogen (N), phosphorus (P), potassium (K), zinc (Zn), iron (Fe), manganese (Mn), copper (Cu) and chlorophyll contents were investigated. The results showed that the plants’ shoot and root biomass and leaf chlorophyll contents were significantly reduced with increasing TiO2-NP applications. On the other hand, TiO2-NP applications positively affected macro(N, P, K) and micronutrients’ (Zn, Mn, Cu) concentration except for Fe. Compared to the control treatment, the concentration of Zn, Mn, Cu, N and P significantly increased with the application of TiO2 nanoparticles. Consequently, TiO2-NPs treatments have increased nutrient uptake of maize excluding iron while decreased the dry biomass of maize at high doses.
Call center arrival modeling: A Bayesian state‐space approach

In this article, we introduce three discrete time Bayesian state‐space models with Poisson measurements, each aiming to address different issues in call center arrival modeling. We present the properties of the models and develop their Bayesian inference. In so doing, we provide sequential updating and smoothing for call arrival rates and discuss how the models can be used for intra‐day, inter‐day, and inter‐week forecasts. We illustrate the implementation of the models by using actual arrival data from a US commercial bank's call center and provide forecasting comparisons. © 2011 Wiley Periodicals, Inc. Naval Research Logistics 58: 28–42, 2011
СУЧАСНІ ПРОБЛЕМИ РЕГІОНАЛЬНОГО РОЗВИТКУ MODERN PROBLEMS OF REGIONAL DEVELOPMENT

Вартанян Г. В. Теоретико-методичні підходи до типізації поліфункціональних екосистем. У статті відображено результати узагальнення існуючих напрацювань щодо типізації екосистем. З урахуванням специфіки поліфункціональних екосистем уточнено підходи та критерії їх типізації, визначено види ПФЕС в межах кожного критерію. Деталізовано типи ПФЕС за основним призначенням з урахуванням домінуючого виду природно-рекреаційних ресурсів та напрямами туристично-рекреаційної діяльності. Наведено приклади статусних ПФЕС природного та штучного походження. Ключові слова: поліфункціональні екосистеми, туристичний потенціал, туризм, природно заповідний фонд, системний підхід
CD4-FITC / CD8-PE

AND EXPLANATION Flow Cytometry (FC) is a powerful tool for the analytical and quantitative characterization of cells which provides rapid, quantitative and multiparametric analysis of heterogeneous cell populations on a cell-by-cell basis. Flow cytometry is performed on cells in liquid suspension that have been incubated with fluorescently-labeled antibodies directed against specific cellular proteins. The relative fluorescence intensity of the positive cells indicates the amount of antibody bound to specific binding sites on the cells, and therefore provides a relative measure of antigen expression. Human lymphocytes may be classified in three main populations according to their biological function and their cell surface antigen expression: T lymphocytes, B lymphocytes and natural killer cells (NK). T lymphocytes (CD3+), the precursors of which originate in the bone marrow and then migrate and mature in the thymus, can be subdivided as well in functionally different populations. The most clearly defined of these are helper/inducer T cells (CD3+CD4+) and suppressor/cytotoxic T cells (CD3+CD8+). T cells produce no antibodies and are the mediators of cell immunity. The CD4-FITC/CD8-PE reagent recognizes the antigens CD4, present in helper/inducer T cells, and CD8, present in suppressor/cytotoxic T cells, and can therefore be used in the characterization studies for immunophenotyping of lymphocytes. These studies are widely applied for monitoring of the immunologic status of post-transplant patients and in the characterization and follow-up of immunodeficiencies, autoimmune diseases, leukemia etc . Individuals with HIV typically exhibit a steady decrease of helper/inducer T lymphocyte counts as the infection progresses. The CD4 antigen is also detected on some monocytes.
Design and Implementation of Architectural Framework of Recommender System for e-Commerce

The rapid development of internet technologies in recent decades has imposed a heavy information burden on users. The popularity of recommender systems has evolved to provide suggestions and recommendations to the user for relevant information from web according to their preferences. Most recommender systems use collaborative-filtering or contentbased methods to predict new items of interest for users. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. In this paper, we propose a standard architectural framework Semantic Enhanced Personalizer (SEP) which integrates three recommendation techniques i.e., original, semantic and categorybased. This framework fulfills user-based and item-based approach of recommendations. The original recommendation will be based on contextual information and the ratings provided by users explicitly, while, the semantic and category-based recommendation will be based on various data mining techniques such as, association rule mining, clustering and similarity measures. This framework overcomes the problem of cold-start
Tumor-infiltrating Lymphocytes Expression in Stage IIIc/IV of High-grade Serous Ovarian Cancer: Variation with Neoadjuvant Chemotherapy and Prognostic Value

1 Citation: Rojas K (2019). Tumor-infiltrating lymphocytes expression in stage IIIc/IV of high-grade serous ovarian cancer: Variation with neoadjuvant chemotherapy and prognostic value. Oncogen Journal 2(2): 8. Tumor-infiltrating Lymphocytes Expression in Stage IIIc/IV of High-grade Serous Ovarian Cancer: Variation with Neoadjuvant Chemotherapy and Prognostic Value K Rojas1, G Prado-Vazquez2, C Barcena3, L Trilla-Fuertes2, A Gamez-Pozo 2,4, A Zapater-Moros4, M Ferrer-Gomez4, L Lema1, R Garcia-Martin3, A Maroto3, JL Rodriguez3, C Mendiola1, JA Fresno Vara4, L Paz-Ares1 1Department of Medical Oncology, Hospital 12 de Octubre, Madrid, Spain. 2Biomedica Molecular Medicine SL, Madrid, Spain. 3Department of Pathology, Hospital 12 de Octubre, Madrid, Spain. 4Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz, Madrid, Spain.
Investigation of Adaptive Multipath Routing For Load Balancing In MANET

In mobile ad hoc network (MANET), congestion is one of the most important limitations that affect the performance of the whole network. Multi-path routing protocol can balance and share the load better than the single path routing protocol in ad hoc networks, thereby reducing the congestion possibility by dividing the traffic in alternative paths. The performance of the network can be improved by using a load balancing mechanism. Such a mechanism transfers load from overloaded nodes to under loaded nodes. The objective of multipath routing is to improve the reliability and throughput and favors load balancing. Multipath routing allows the establishment of multiple paths between a pair of source and destination node. It is typically proposed in order to increase the reliability of data transmission or to provide load balancing and has received more and more attentions. In this paper, some of the congestion removing and load balancing routing schemes have been surveyed. The relative strengths and weaknesses of the protocols have also been studied which allow us to identify the areas for future research.