Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Transceiver Design Algorithms for Multiuser MISO Relay Systems With Energy Harvesting

In this paper, we investigate a multiuser multiple-input single-output relay system with simultaneous wireless information and power transfer, where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, we study the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix, and the power splitting (PS) ratios at the single-antenna receivers. The aim is to minimize the total transmission power of the BS and the RS under both signal-to-interference-plus-noise ratio and EH constraints. First, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. This AO-based approach is then extended into a robust transceiver design against norm bounded errors in channel state information (CSI), by using semidefinite relaxation and the S-procedure. Second, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO-based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed, which consists of BS beamforming vectors, optimally scaled RS permutation matrix, and receiver PS ratios. For the given CSI, the optimal latent transceiver with the lowest total power consumption is selected for transmission. We propose concave-convex procedure-based and subgradient-type iterative algorithms, respectively, to design the latent transceivers under perfect and imperfect CSI. Simulation results are presented to validate the effectiveness of all the proposed algorithms.
Defining Insulin Resistance From Hyperinsulinemic-Euglycemic Clamps

OBJECTIVE This study was designed to determine a cutoff point for identifying insulin resistance from hyperinsulinemic-euglycemic clamp studies performed at 120 mU/m2 ⋅ min in a white population and to generate equations from routinely measured clinic and blood variables for predicting clamp-derived glucose disposal rate (GDR), i.e., insulin sensitivity. RESEARCH DESIGN AND METHODS We assembled data from hyperinsulinemic-euglycemic clamps (120 mU/m2 ⋅ min insulin dose) performed at the Pennington Biomedical Research Center between 2001 and 2011. Subjects were divided into subjects with diabetes (n = 51) and subjects without diabetes (n = 116) by self-report and/or fasting glucose ≥126 mg/dL. RESULTS We found that 75% of individuals with a GDR <5.6 mg/kg fat-free mass (FFM) + 17.7 ⋅ min were truly insulin resistant. Cutoff values for GDRs normalized for body weight, body surface area, or FFM were 4.9 mg/kg ⋅ min, 212.2 mg/m2 ⋅ min, and 7.3 mg/kgFFM ⋅ min, respectively. Next, we used classification tree models to predict GDR from routinely measured clinical and biochemical variables. We found that individual insulin resistance could be estimated with good sensitivity (89%) and specificity (67%) from the homeostasis model assessment of insulin resistance (HOMA-IR) >5.9 or 2.8< HOMA-IR <5.9 with HDL <51 mg/dL. CONCLUSIONS We developed a cutoff for defining insulin resistance from hyperinsulinemic-euglycemic clamps. Moreover, we now provide classification trees for predicting insulin resistance from routinely measured clinical and biochemical markers. These findings extend the clamp from a research tool to providing a clinically meaningful message for participants in research studies, potentially providing greater opportunity for earlier recognition of insulin resistance.
Robust sketching for multiple square-root LASSO problems

Many learning tasks, such as cross-validation, parameter search, or leave-one-out analysis, involve multiple instances of similar problems, each instance sharing a large part of learning data with the others. We introduce a robust framework for solving multiple square-root LASSO problems, based on a sketch of the learning data that uses low-rank approximations. Our approach allows a dramatic reduction in computational effort, in effect reducing the number of observations from $m$ (the number of observations to start with) to $k$ (the number of singular values retained in the low-rank model), while not sacrificing---sometimes even improving---the statistical performance. Theoretical analysis, as well as numerical experiments on both synthetic and real data, illustrate the efficiency of the method in large scale applications.
Multimodal Hetero-Frequency Channel Extrapolation (M-HCE) Based CSI Reconstruction for NR FDD Massive MIMO Systems

In FDD massive MIMO (mMIMO) systems, although assisted with Rel.16 eType II feedback, Channel State Information (CSI) accuracy is limited by overhead, quantization error and user mobility. In this paper, we propose Multimodal Hetero-frequency Channel Extrapolation (M-HCE) based CSI reconstruction, where channel model with enhanced Levy-Ito decomposition path structure and Karhuen-Loeve expansion are introduced. Downlink channel is reconstructed from the multimodal transformation of uplink Sounding Reference Signal (SRS) channel and CSI feedback, by exploring both partial downlink-uplink reciprocity between hetero-frequency channels and reported downlink CSI. The performance is verified in commercial evaluation platform, which shows 23%-66% throughput gain compared with eType II CSI for user mobility 10–60 km/h and typical FDD operating bands.
Update on Diabetes Mellitus

Diabetes mellitus is a complex multi-system disorder that may be classified as autoimmune mediated type 1 diabetes, or as insulin resistance associated type 2 diabetes. In type 1 diabetes, there is selective loss of the beta cells within the endocrine islets, as a consequence of T-cell and cytokine mediated destruction of these cells, perhaps in conjunction with destruction of the peri-islet Schwann cells. In type 2 diabetes, the etiology of the resistance ranges from post-receptor defects in the insulin signaling pathway to excessive production of adipocyte derived cytokines that antagonize insulin action to mitochondrial defects that interfere with glucose disposal. Proteome based technologies are providing new insights into these defects.
A Searchable Patient Record Database for Decision Support

UNLABELLED We describe a searchable patient record database for decision support. It contains medical histories of real but pseudonymous patients with patterns of diagnosis, chosen treatment, and outcome. To be searchable, the patterns contain a feature vector (for similarity search by calculating distances) and a globally unique "pattern name" which identifies the kind of data which are represented by the feature vector. Patterns with the same pattern name are directly comparable; they represent the same kind of data. For pattern selection the database provides a growing well-structured list of initial diagnoses with associated input masks. PROCEDURE The doctor can assume that the database contains patients similar to the current patient if he finds his initial diagnosis in the list. Clicking on it opens an associated input mask which requests specific further data for finer differentiation. After input a searchable pattern group is built from the provided data, and used to search for histories of patients with similar fine diagnostics, and for the most successful treatment decisions at these patients. This information can be very valuable for deciding the treatment of the current patient. Because the database can collect patient histories from all countries, in the long run this could open access to a wealth of experience which by far exceeds the capacity of a today's doctor.
Stability for receding-horizon stochastic model predictive control

A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the SMPC approach is demonstrated using the Van de Vusse reactions.
MOMC: Multi-objective and Multi-constrained Scheduling Algorithm of Many Tasks in Hadoop

Even though scheduling in a distributed system was debated for many years, the platforms and the job types are changing everyday. This is why we need special algorithms based on new applications requirements, especially when a application is deployed in a Cloud environment. One of the most important framework used for large-scale data processing in Clouds is Hadoop and its extensions. Hadoop framework comes with default algorithms like FIFO, Fair Scheduler or Capacity Scheduler, and Hadoop on Demand. These scheduling algorithms are focused on a different and single constraint. It is hard to satisfy multiple constraints and to have a lot of objectives in the same time. After summarizing the most common schedulers, showing the need of each one in the moment it appeared on the market, this paper presents MOMC, a multi-objective and multi-constrained scheduling algorithm of many tasks in Hadoop. MOMC implementation focuses on two objectives: avoiding resource contention and having an optimal workload of the cluster, and two constraints: deadline and budget. To compare the algorithms based on different metrics, we use Scheduling Load Simulator, which is integrated in Hadoop framework and helps the developers to spend less time on testing. As killer application that generate many tasks we have chosen processing task for the Million Song Dataset, which is a set of data contains metadata for one million commercially-available songs.
Colonoscopy Key Performance Indicators in a Rural General Australian Hospital

We evaluated colonoscopic neoplastic yield (adenomas and cancers) in a rural endoscopic unit alongside several key performance indicators known to influence yield. A second aim was to compare neoplastic yield according to colonoscopic indication.Methods - We retrospectively audited 371 colonoscopies (163 male, 208 female) undertaken over six months (2007-2008) at the Mersey Hospital, Tasmania.ResultsCaecal intubation rate was 86.79% (males 90.8%, females 83.65%); bowel preparation judged as good (56.87%), fair (22.37%), poor (13.48%), not recorded (7.28%). Adenoma Detection Rate (ADR) was 11.86% (males 18.4%, females 6.7%). Inclusion of cancers - Significant Neoplastic Lesion Rate (SNL) was 15.9% (males 20.86%, females 12.02%). Mean withdrawal time to anus was 5.76 minutes with significantly higher ADR for 6-10, 11-15 and >15 minutes than 0-5 minutes (p<0.0001). SNL detection rates for major presenting indications were expressed as number needed to screen (NNS) with 95% confidence intervals (CI). Results: PR bleeding, NNS = 5, CI 3.45-7.69; Anaemia, NNS = 9.5, CI = 4-25; Abdominal Pain, NNS = 12.76, CI 5.26-33; Change in bowel habit, NNS = 6, CI 3.33-12.5; Family history colorectal cancer, NNS = 4.83, CI 3.03-8.33; Positive Faecal Occult Blood, NNS = 4.26, CI = 2.94-6.67, without significant sex differences amongst groups (p=0.5186)ConclusionsADR fell short of benchmark standards (25% males, 15% females); with slightly lower than recommended caecal intubation rate ("e90%). Adequacy of bowel preparation was comparable to reference ranges. A statistically-significant trend between rapid withdrawal times and lower ADR was found, although caution should be exercised in interpretation.
Multifractal analysis of the Greenland Ice‐Core Project climate data

Recent climatic records from the Greenland ice-core project (GRIP) ice core show that the climate proxy temperatures δ18O (18O/16O ratios) display sharp gradients and large fluctuations over all observed scales. We show that these variations are scale invariant over the range ≈400 yr to ≈40 kyr. The fluctuations corresponding to these scales are studied using multifractal analysis techniques. We estimate universal multifractal indices which characterize all the fluctuations for all the scales in this range and which are close to those obtained for turbulent temperatures at much shorter time scales. We speculate briefly on the origin of these common features intervening all over the observed range from seconds to kyr.
An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.
Differential Community Detection in Paired Biological Networks

Motivation Biological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures. Methods The task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast two-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. We then apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities. Results Our proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different power-law exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding network-inferred bio-markers/pathways associated with a trait of interest. Conclusion The proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available at https://sites.google. com/site/ raghvendramallmlresearcher/codes.
Mean Exposure Fractions of Human Body Solar UV Exposure Patterns for Application in Different Ambient Climates

In this research, the erythemally effective UV measured using miniaturized polysulphone dosimeters to over 1250 individual body sites and collected over a 4‐year period is presented relative to the total exposed skin surface area (SSA) of a life‐size manikin model. A new term is also introduced, the mean exposure fraction (MEF). The MEF is used to weight modeled or measured horizontal plane UV exposures to the total unprotected SSA of an individual and is defined as the ratio of exposure per unit area received by the unprotected skin surfaces of the body relative to the exposure received on a horizontal plane. The MEF has been calculated for a range of solar zenith angles (SZA) to provide a sunburning energy data set weighted to the actual SSA of a typically clothed individual. For this research, the MEF was determined as 0.15, 0.26 and 0.41 in the SZA ranges 0°–30°, 30°–50° and 50°–80° providing information that can be used in a variety of different ambient, latitudinal and seasonal climates where total human body UV exposure information is not available.
Assessing safety and efficacy of sofosbuvir for the treatment of hepatitis C

Introduction: IFN-free regimens with direct antiviral agents (DAAs) against hepatitis C virus (HCV) are likely to greatly expand patients’ access and response to hepatitis C therapy, while safety and tolerability of treatments seem substantially improved. Sofosbuvir (SOF), a NS5B nucleotide polymerase inhibitor with pan-genotypic activity and a high-barrier to resistance, has been approved by FDA and EMA in an all oral combination therapy for chronic hepatitis C with ribavirin (RBV) alone, or in combination with either pegylated interferon/RBV or other DAAs. Areas covered: This paper provides an overview of SOF-based therapy in chronic hepatitis C as it emerges from the published clinical trials. Data on special populations are included (i.e., decompensated patients, patients on liver transplant waiting lists, patients with renal impairment). The data has been analyzed according to the different HCV-genotypes and comprehensively covers both safety and efficacy treatment profiles. Expert opinion: Clinical trials have highlighted the safety and efficacy of SOF-based regimens, leading to the rapid approval of this therapy and its incorporation in the recommendations of the international societies on treatment of HCV infection. However, additional data are still needed to optimize both combination therapies’ efficacy and duration in some categories of patients who have been under-represented in the registration trials.
Dynamic Query Expansion based on User's Real Time Implicit Feedback

Majority of the queries submitted to search engines are short and under-specified. Query expansion is a commonly used technique to address this issue. However, existing query expansion frameworks have an inherent problem of poor coherence between expansion terms and user’s search goal. User’s search goal, even for the same query, may be different at different instances. This often leads to poor retrieval performance. In many instances, user’s current search is influenced by his/her recent searches. In this paper, we study a framework which explores user’s implicit feedback provided at the time of search to determine user’s search context. We then incorporate the proposed framework with query expansion to identify relevant query expansion terms. From extensive experiments, it is evident that the proposed framework can capture the dynamics of user’s search and adapt query expansion accordingly.
Psychiatric Epidemiology

In this paper, some of the basic concepts of epidemiology are presented. Their applicability to psychiatry is outlined, and the definitions of terms are given. Various methods of epidemiological investigation are discussed. The background for the development of recent prevalence studies, including advances in diagnostic methods and case ascertainment, are given in some detail, and some recent results are presented. Additional areas of investigation are discussed more briefly.
Rule-Based Translation of Spanish Verb-Noun Combinations into Basque

This paper presents a method to improve the translation of Verb-Noun Combinations (VNCs) in a rule-based Machine Translation (MT) system for Spanish-Basque. Linguistic information about a set of VNCs is gathered from the public database Konbitzul, and it is integrated into the MT system, leading to an improvement in BLEU, NIST and TER scores, as well as the results being evidently better according to human evaluators.
Some basic issues in teletaction

Teletaction is the transmission of cutaneous information from a remote tactile sensor to an operator's skin, typically the finger tips. Ideally, one would like a realistic sensation of directly touching an object with one's own finger and sense properties such as local shape, hardness, or texture. Teletaction or tactile feedback is one component of haptic feedback, the other component being force or kinesthetic feedback. This paper considers design issues for teletaction systems, particularly sampling density, aliasing, and the limitations of using an array of 1-DOF actuators to approximate a continuous stress distribution on the human finger.
Automatic query taxonomy generation for information retrieval applications

It is crucial for information retrieval systems to learn more about what users search for in order to fulfil the intent of searches. This paper introduces query taxonomy generation, which attempts to organise users’ queries into a hierarchical structure of topic classes. Such a query taxonomy provides a basis for the in‐depth analysis of users’ queries on a larger scale and can benefit many information retrieval systems. The proposed approach to this problem consists of two computational processes: hierarchical query clustering to generate a query taxonomy from scratch, and query categorisation to place newly‐arrived queries into the taxonomy. The results of the preliminary experiment have shown the potential of the proposed approach in generating taxonomies for queries, which may be useful in various Web information retrieval applications.