Miracle-dz/UniFAR-FTUs huggingface dataset:
The Downlink Performance of Massive MIMO Systems under Different Uplink Pilot Allocation Schemes

In the scenario of time division duplexing (TDD) massive multiple-input multiple-output (MIMO) system, when there is pilot contamination (PC) in the uplink, different pilot allocation schemes will affect the results of the uplink channel estimation. When the channel estimation results are used for downlink transmission precoding, which will further affects the downlink performance. This paper analyses the impact of different pilot allocation (PA) schemes on downlink performance. Through theoretical analysis, the system downlink signal to interference plus noise ratio (SINR) and spectral efficiency expressions of different pilot allocation schemes are obtained respectively. The simulation results show that the pilot allocation schemes with cell grouping is better than the schemes without cell grouping. The SINR increases as the number of cell grouping increases, and the spectrum efficiency increases first and then decreases as the number of cell grouping increases.
Osteocalcin gene promoter: Unlocking the secrets for regulation of osteoblast growth and differentiation

The bone tissue‐specific osteocalcin gene remains one of a few genes that exhibits osteoblast‐restricted expression. Over the last decade, characterization of the promoter regulatory elements and complexes of factors that control suppression of the osteocalcin gene in osteoprogenitor cells and transactivation in mature osteoblasts has revealed transcriptional regulatory mechanisms that mediate development of the osteoblast phenotype. In this review, we have focused on emerging concepts related to molecular mechanisms supporting osteoblast growth and differentiation based on the discoveries that the osteocalcin gene is regulated by homeodomain factors, AP‐1 related proteins, and the bone restricted Cbfa1/AML3 transcription factor. J. Cell. Biochem. Suppls. 30/31:62–72, 1998. © 1998 Wiley‐Liss, Inc.
Does Medicare managed care provide equal treatment for mental illness across races?

BACKGROUND While disparities in access to care are well documented, little is known about the quality of mental health care received by racial and ethnic minorities. We examined the quality of mental health care received by elderly enrollees in Medicare + Choice plans. METHODS An observational study was performed using individual-level Health Plan Employer Data and Information Set data. From 4182 to 5,016,028 individuals 65 years or older and enrolled in Medicare + Choice plans in 1999 were involved in different measures. Rates of mental health inpatient discharges, average length of stay, percentage of members receiving mental health services, rates of follow-up after hospitalization for mental illness, optimal practitioner contacts for antidepressant medication management, and effective acute- and continuation-phase treatment were assessed. RESULTS Compared with whites, minorities received substantially less follow-up after hospitalization for mental illness. The 30-day follow-up rates for whites, African Americans, Asians, and Hispanics were 60.2%, 42.4%, 54.1%, and 52.6%, respectively. Minorities also had lower rates of antidepressant medication management for newly diagnosed episodes of depression. The rates of optimal practitioner contacts for whites, African Americans, Asians, and Hispanics were 12.5%, 12.0%, 11.1%, and 10.6%; the rates of effective acute-phase treatment were 60.1%, 48.5%, 40.7%, and 57.6%; and the rates of effective continuation-phase treatment were 46.7%, 32.7%, 31.9%, and 39.6%, respectively. The statistically significant disparities persisted after adjusting for effects of age, sex, income, plan model, profit status, and region of the country. CONCLUSIONS The overall quality of mental health care for people enrolled in Medicare + Choice managed care plans is far from optimal. There are large and persistent racial differences that merit further attention to better understand their underlying causes and solutions.
Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection

Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background subtraction (BS) refers to the process of segmenting moving regions from video sensor data and is usually performed at pixel level. In its standard form this technique involves building a model of the background and extracting regions of the foreground. In this paper, we propose a cluster-based BS technique using a mixture of Gaussians. An adaptive mechanism is developed that allows automated learning of the model parameters. The efficiency of the designed technique is demonstrated in comparison with a pixel-based BS.
Research on Feature Extraction based on Deep Learning

Content-Based Image Retrieval and Feature Extraction: A Deep learning for specific information extraction from Bing: research on feature extraction based on deep learning Drug Label Extraction using Deep Learning | Engineering A systematic extraction of glacial lakes for satellite research on feature extraction based on deep learning Keras: Feature extraction on large datasets with Deep LearningFeature Extraction - an overview | ScienceDirect TopicsFeature Extraction Definition - DeepAIFeature Extraction - MATLAB & Simulink - MathWorksPlant diseases and pests detection based on deep learning Image Feature Extraction Using Scikit Image - A Hands-On GuideDeep Learning Research Papers - Academia.eduDeep Learning: A Comprehensive Overview on Techniques Research - Apple Machine Learning ResearchInvoice Information extraction using OCR and Deep Learning Audio Feature Extraction - Devopedia Content-Based Image Retrieval and Feature Extraction: A Deep Learning approach considers unstructured audio representations such as the spectrogram or MFCCs. It extracts the patterns on its own. By late 2010s, this became the preferred approach since feature extraction is automatic. It's also supported by the abundance of data and computation power.
vrrB, a Hypervariable Open Reading Frame in Bacillus anthracis

ABSTRACT Bacillus anthracis appears to be the most molecularly homogeneous bacterial species known. Extensive surveys of worldwide isolates have revealed vanishingly small amounts of genomic variation. The biological importance of the resting-stage spore may lead to very low evolutionary rates and, perhaps, to the lack of potentially adaptive genetic variation. In contrast to the overall homogeneity, some gene coding regions contain hypervariability that is translated into protein variation. During marker analysis of diverse strains, we have discovered a novel ca. 750-nucleotide open reading frame (ORF) that contains in-frame, variable-number tandem-repeat sequences. Four distinct variable regions exist within vrrB, giving rise to 11 distinct alleles in eight different length categories among B. anthracis strains. This ORF putatively codes for a 241- to 265-amino-acid protein, rich in glutamine (13.2%), glycine (23.4%), and histidine (23.0%). The variable-region amino acids of the vrrB ORF are strongly hydrophilic. Coupled with putative transmembrane domains flanking the variable regions, this suggests a membrane-anchored cytosolic or extracellular location for the putative protein. Sequence analysis of the complete ORFs from three Bacillus cereus strains shows maintenance of the ORF across species boundaries, including strong conservation of the amino acid sequence and the capacity to vary among strains. The presence of 11 different alleles of the vrrB locus is in stark contrast to the near homogeneity of B. anthracis. Evolution of hypervariable genes can negate the lack of genetic variability in species such as B. anthracisand provide select rapid evolution in other more variable species.
Neoadjuvant FOLFOX chemotherapy in 34 consecutive patients with mucinous peritoneal carcinomatosis of appendiceal origin

A treatment option for patients with peritoneal mucinous carcinomatosis (PMCA) from an appendiceal neoplasm is cytoreductive surgery and perioperative intraperitoneal chemotherapy. Also, these patients are recommended for systemic chemotherapy using an oxaliplatin and 5‐fluorouracil (FOLFOX) regimen. A major question concerns the proper timing (neoadjuvant vs. adjuvant) of the systemic chemotherapy.
Abstract—debates on Residential Satisfaction Topic Have Been

vigorously discussed in family house setting. Nonetheless, less or lack of attention was given to survey on student residential satisfaction in the campus house setting. This study, however, tried to fill in the gap by focusing more on the relationship between students' socioeconomic backgrounds and student residential satisfaction with their on-campus student housing facilities. Two-stage cluster sampling method was employed to classify the respondents. Then, self-administered questionnaires were distributed face-to-face to the students. In general, it was confirmed that the students' socioeconomic backgrounds have significantly influence the students' satisfaction with their on-campus student housing facilities. The main influential factors were revealed as the economic status, sense of sharing, and the ethnicity of roommates. Likewise, this study could also provide some useful feedback for the universities administration in order to improve their student housing facilities.
Design of a cloud naming framework

In cloud computing environments, naming and resource location become critical issues. Exploiting the concept of resource virtualization, a cloud offers a variety of entities which can be moved from a place to another, needing to be identified with one or more names and logical representations in various contexts. In such scenario, the management of cloud name spaces may become very difficult. This paper proposes a Cloud Naming System Framework (CNSF) able to: I) integrate independent cloud name spaces in federated scenario, II) support the "mounting" of names from a cloud name space to another, III) interact with other URI-based naming systems.
Mutual Alignment Transfer Learning

Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can adjust to optimize its behaviour for states commonly visited by the real-world agent.
Experience report: A field analysis of user-defined security configurations of Android devices

The wide spreading of mobile devices, such as smart phones and tablets, and their always-advancing capabilities, ranging from taking photos to accessing banking accounts, makes them an attractive target for attackers. This, together with the fact that users frequently store critical personal information in such devices and that many organizations currently allow employees to use their personal devices to access the enterprise information infrastructure and applications, turns assessing the security of mobile devices into a key issue. In order to understand the common misconfiguration problems, this practical experience report presents a held analysis of 41 user-defined security settings of more than 500 Android devices. Findings suggest that most users neglect basic security configurations such as login mechanisms and (bat manufacturers should rethink their policies in terms of the security settings that can be modified by the users. The paper also proposes concrete security countermeasures to mitigate some of the identified misconfigurations.
Word-length Entropies and Correlations of Natural Language Written Texts

Abstract We study the frequency distributions and correlations of the word lengths of 10 European languages. Our findings indicate that (a) the word-length distribution of short words quantified by the mean value and the entropy distinguishes the Uralic (Finnish) corpus from the others, (b) the tails at long words, manifested in the high-order moments of the distributions, differentiate the Germanic languages (except for English) from the Romanic languages and Greek and (c) the correlations between nearby word lengths measured by the comparison of the real entropies with those of the shuffled texts are found to be smaller in the case of Germanic and Finnish languages.
Community building as an effective user engagement strategy: A case study in academic libraries

Organizations have recognized that engaging with customers on social media is now a critical element of their public relations strategy, but many struggle to find the optimal way to use these new tools. This study examined the success factors of online posts from organizations in evoking a high level of response from group members. Specifically, appeals to community belongingness were discovered to be effective at motivating user engagement on social media. The impact of community‐focused language on garnering likes, comments, and shares was assessed using a methodology that combined text analytics, supervised machine learning, and multilevel regression. In 51,760 postings from 100 academic libraries, it was found that general community appeals increase likes by 60%, comments by 130%, and shares by 39%, whereas specific intracommunity, locally meaningful references increase likes by 16% and shares by 12%. These findings have both theoretical implications, as they constitute a large‐scale, empirical confirmation of the belongingness hypothesis from social psychology, and practical implications, as they suggest best practices for maintaining an organizational online presence.
New evidence combination rules for activity recognition in smart home

The evidence theory and its propositional conflict redistribution variant rules are mathematical formalisms used to represent uncertain as well as ambiguous data. The evidence combination rules proposed in these formalisms do not satisfy the idempotence property. However, in a variety of applications, it is desirable that the evidence combination rules satisfy this property. In response to this challenge, the present work proposes a new formalism for reasoning under uncertainty based on new consensus and conflicting of evidence concepts. This mathematical formalism is evaluated using a real world activity recognition problem in smart home environment. The results show that one rule of our formalism respects the idempotence property and improves the accuracy of activity recognition.
Herbicide Metabolism: Crop Selectivity, Bioactivation, Weed Resistance, and Regulation

Abstract Several grass and broadleaf weed species around the world have evolved multiple-herbicide resistance at alarmingly increasing rates. Research on the biochemical and molecular resistance mechanisms of multiple-resistant weed populations indicate a prevalence of herbicide metabolism catalyzed by enzyme systems such as cytochrome P450 monooxygenases and glutathione S-transferases and, to a lesser extent, by glucosyl transferases. A symposium was conducted to gain an understanding of the current state of research on metabolic resistance mechanisms in weed species that pose major management problems around the world. These topics, as well as future directions of investigations that were identified in the symposium, are summarized herein. In addition, the latest information on selected topics such as the role of safeners in inducing crop tolerance to herbicides, selectivity to clomazone, glyphosate metabolism in crops and weeds, and bioactivation of natural molecules is reviewed.
Immunotherapy with CpG Oligonucleotides and Antibodies to TNF-α Rescues Neonatal Mice from Lethal Arenavirus-Induced Meningoencephalitis1

Viral encephalitides are life-threatening diseases in neonates partly due to the irreversible damage inflammation causes to the CNS. This study explored the role of proinflammatory cytokines in the balance between controlling viral replication and eliciting pathologic immune responses in nonlytic viral encephalitis. We show that neonatal mice challenged with arenavirus Tacaribe (TCRV) develop a meningoencephalitis characterized by high IFN-γ and TNF-α levels and mild T cell infiltration. Neutralization of the TNF-α using mAb was associated with lower chemokine expression, reduced T cell infiltration, and lower levels of IFN-γ, and TNF-α in the CNS and led to 100% survival. Moreover, treatment with Abs to TNF-α improved mobility and increased survival even after the mice developed bilateral hind limb paralysis. Of note, animals treated with anti-TNF-α Abs alone did not clear the virus despite generating Abs to TCRV. Direct activation of the innate immune response using CpG oligodeoxynucleotides in combination with anti-TNF-α Abs resulted in 100% survival and complete viral clearance. To our knowledge, this is the first demonstration of the use of innate immune modulators plus Abs to TNF-α as therapeutics for a lethal neurotropic viral infection.
Cross-infection with Serratia marcescens

Cross-infection in a urological unit due to Serratia marcescens is reported. The bacteriology of the organism and its mode of spread are described. It is suggested that Serratia marcescens may be a more virulent organism than is generally believed, especially in situations in which there is an excess of mucus.
Coupled lighting direction and shape estimation from single images

This paper presents a new method for the simultaneous estimation of lighting direction and shape from shading. The method estimates the shape and the lighting direction using a two step iterative process. We assume an initial (possibly incorrect) estimate of the lighting position. A stiff deformable model is then fitted to the image, assuming this lighting position. Next, a least-squares estimate of the lighting position is derived from the model using the Levenberg-Marquart method. The two steps-model fitting and lighting-position estimation-are iterated. Once the light direction has converged to a stable solution the deformable model stiffness is lowered and the model fits accurately given the lighting model. In addition, we show how the method can be used with either orthographic or perspective projection assumptions. In a variety of experiments on real and synthetic data, the method is robust to errors both to the initial light position and shape estimates.
Recruiting Strategy and 24-Hour Biomonitoring of Paraquat in Agricultural Workers

ABSTRACT The objectives of this study were to recruit agricultural workers in Costa Rica to participate in a 24-hour urine collection for paraquat exposure assessment and to compare the 24-hour sampling to end-of-shift sampling. The authors recruited 187 handlers and 54 nonhandlers from coffee, banana, and palm oil plantations. The completeness of 24-hour urine samples collected (a total of 393 samples) was confirmed by questionnaire and urinary creatinine level. For a subset of 12 samples, the absorbed paraquat level was determined in 24-hours and end-of-shift spot urine samples. The participation rate for handlers was ∼90%. The completeness of 24-hour urine collections was verified as the overall average of creatinine levels from 393 urines (1.11 ± 0.50 g/L). A total of 92.4% to 96.7% of urine samples were considered within the acceptable range of urinary creatinine, whereas 94.7% of the samples were described as “complete” from the questionnaire. Measured creatinine correlated well to predicted values (r = .327, p = .0024, 95% CI .12–.51). Detected paraquat levels in spot urine samples had a sensitivity of 96.9% at the high specificity of 100% compared to 24-hour urine samples as the gold standard. There was a significant (p < .0001) correlation between spot and 24-hour urine paraquat levels (r = .7825, 95% CI .61–.88). The recruiting strategy was successful in getting 24-hour urine samples from a farm worker population. Comparison between the paraquat levels in spot and 24-hour urine samples demonstrated that for this compound, end-of-shift spot urine samples would be an appropriate substitute for 24-hour collections.
Summarization scheme based on near-duplicate analysis

This paper presents our approach to select relevant sequences from raw videos in order to generate summaries to Trecvid 2008 BBC Rush Task. Our system is composed of two major steps: First, the system detects "semantic" shot boundaries and keeps only non-redundant shots; then, the system estimates average motion for each shot, as a criterion of amount of information, to better share out the duration of the summary between remaining shots. The first step is based on a fast near-duplicate retrieval using Locality Sensitive Hashing (LSH) which provides results in few seconds (if we do not take into account decoding and encoding processes). The evaluation of Trecvid shows very promising results, since we ranked 17th over 43 runs, regarding redundancy measure (RE), and 18th for object and event inclusion (IN). These balanced results (most of best teams for the first criterion are among the latest for the second one) show that our method offers a quite good trade-off between false negatives (IN) and false positives (RE).
Local Regularization in the Inverse Problem of Electrocardiography

The inverse problem of ECG is the task of cardiac source reconstruction from the measured body surface potential maps (BSPM). It is ill-posed and therefore requires regularization, which is usually applied uniformly to the whole heart geometry. In order to improve the solution quality and localize potentials extrema we propose a local regularization method: the weighting is done iteratively according to the solution spatial content. The performed test showed the ability of the new method to overcome oversmoothing and to better reconstruct strong solution gradients.
To Empower or Not to Empower Your Sales Force? An Empirical Examination of the Influence of Leadership Empowerment Behavior on Customer Satisfaction and Performance

This research focuses on the impact of leadership empowerment behavior (LEB) on customer service satisfaction and sales performance, as mediated by salespeople's self-efficacy and adaptability. Moreover, the authors propose an interactive relationship whereby LEB will be differentially effective as a function of employees' empowerment readiness. The authors' hypotheses are tested using survey data from a sample of 231 salespeople in the pharmaceutical field, along with external ratings of satisfaction from 864 customers and archival sales performance information. Results indicated that contrary to popular belief, employees with low levels of product/industry knowledge and low experience benefit the most from leadership behaviors that are empowering, whereas high-knowledge and experienced employees reap no clear benefit. The authors conclude with directions for future research and application.
Envelope simulation by SPICE-compatible models of linear electric circuits driven by modulated signals

SPICE-compatible equivalent circuits were developed to facilitate the analysis and envelope simulation of electric circuits driven by modulated signals. The circuits are based on a novel complex phasor-domain transformation. The proposed method facilitates simulation of any general linear circuit driven by a modulated signal such as amplitude modulation, frequency modulation, or phase modulation. Simulation time by the proposed envelope simulation is much faster than the full cycle-by-cycle simulation of the original circuit and excitation.