Miracle-dz/UniFAR-FTUs huggingface dataset:
High-dose therapy for refractory multiple myeloma: improved prognosis with better supportive care and double transplants.

One hundred and thirty-five patients with advanced and refractory myeloma received one of three high-dose therapy regimens: melphalan at doses of 90 to 100 mg/m2 (MEL 100; 47 patients) without autotransplant; total body irradiation (TBI; 850 cGy) with either melphalan 140 mg/m2 or thiotepa 750 mg/m2 and autologous bone marrow transplant (ABMT) (< or = 30% plasma cells; 21 patients); melphalan 200 mg/m2 (MEL 200) supported by both peripheral blood stem cells (PBSC) and ABMT plus GM-CSF intended as a double-transplant program (67 patients; 42 have completed and 3 are still awaiting a second autotransplant; 5 additional patients received an allograft for their second transplant). Mortality within 2 months of therapy was 20% to 25% with MEL 100 and TBI regimens, but less than 1% with MEL 200, mainly because severe neutropenia (< 500/microL) was shortened to less than 1 week due to infusion of PBSC and use of growth factor therapy. Low beta 2-microglobulin (beta 2M) levels < or = 2.5 mg/L and MEL 200 therapy were identified as the two most important independent favorable variables associated with prolonged event-free survival (EFS) and overall survival (OS). On the basis of these two parameters, three risk groups were defined: 29 good-risk patients with low beta 2M receiving MEL 200 had the best outcome, with median durations of EFS of 37 months and projected OS of > or = 43 months; 54 intermediate-risk patients displaying one of the two favorable parameters had EFS and OS durations of 16 and 36 months, respectively; and 52 poor-risk patients with high beta 2M not receiving MEL 200 had a dismal prognosis, with EFS of 3 months and OS of 5 months (all P < .0001). Further analysis that excluded treatment as a variable identified high beta 2M and resistant relapse as the two major adverse prognostic factors, one of which was present in 80% of the 135 patients. Among these 108 high-risk patients, prognosis was improved markedly with MEL 200 because of both better supportive care (PBSC and hematopoietic growth factors) and more intensive therapy using the double-transplant approach. This study supports the concept that safer and potentially more-effective therapies can be developed in the setting of advanced and resistant disease.
A diverse group of small circular ssDNA viral genomes in human and non-human primate stools

Viral metagenomics sequencing of fecal samples from outbreaks of acute gastroenteritis from the US revealed the presence of small circular ssDNA viral genomes encoding a replication initiator protein (Rep). Viral genomes were ∼2.5 kb in length, with bi-directionally oriented Rep and capsid (Cap) encoding genes and a stem loop structure downstream of Rep. Several genomes showed evidence of recombination. By digital screening of an in-house virome database (1.04 billion reads) using BLAST, we identified closely related sequences from cases of unexplained diarrhea in France. Deep sequencing and PCR detected such genomes in 7 of 25 US (28 percent) and 14 of 21 French outbreaks (67 percent). One of eighty-five sporadic diarrhea cases in the Gambia was positive by PCR. Twenty-two complete genomes were characterized showing that viruses from patients in the same outbreaks were closely related suggesting common origins. Similar genomes were also characterized from the stools of captive chimpanzees, a gorilla, a black howler monkey, and a lemur that were more diverse than the human stool-associated genomes. The name smacovirus is proposed for this monophyletic viral clade. Possible tropism include mammalian enteric cells or ingested food components such as infected plants. No evidence of viral amplification was found in immunodeficient mice orally inoculated with smacovirus-positive stool supernatants. A role for smacoviruses in diarrhea, if any, remains to be demonstrated.
System responses to long-term drought and re-watering of two contrasting alfalfa varieties.

Systems analysis of two alfalfa varieties, Wisfal (Medicago sativa ssp. falcata var. Wisfal) and Chilean (M. sativa ssp. sativa var. Chilean), with contrasting tolerance/sensitivity to drought revealed common and divergent responses to drought stress. At a qualitative level, molecular, biochemical, and physiological responses to drought stress were similar in the two varieties, indicating that they employ the same strategies to cope with drought. However, quantitative differences in responses at all levels were revealed that may contribute to greater drought tolerance in Wisfal. These included lower stomatal density and conductance in Wisfal; delayed leaf senescence compared with Chilean; greater root growth following a drought episode, and greater accumulation of osmolytes, including raffinose and galactinol, and flavonoid antioxidants in roots and/or shoots of Wisfal. Genes encoding transcription factors and other regulatory proteins, and genes involved in the biosynthesis of osmolytes and (iso)flavonoids were differentially regulated between the two varieties and represent potential targets for improving drought tolerance in alfalfa in the future.
Local grouping for optical flow

Optical flow estimation requires spatial integration, which essentially poses a grouping question: what points belong to the same motion and what do not. Classical local approaches to optical flow, such as Lucas-Kanade, use isotropic neighborhoods and have considerable difficulty near motion boundaries. In this work we utilize image-based grouping to facilitate spatial- and scale-adaptive integration. We define soft spatial support using pairwise affinities computed through intervening contour. We sample images at edges and corners, and iteratively estimate affine motion at sample points. Figure-ground organization further improves grouping and flow estimation near boundaries. We show that affinity-based spatial integration enables reliable flow estimation and avoids erroneous motion propagation from and/or across object boundaries. We demonstrate our approach on the Middlebury flow dataset.
Alternative phenotypes and sexual selection: can dichotomous handicaps honestly signal quality?

Considerable theoretical and empirical effort has been focused on the potential of continuously variable sexual traits to honestly indicate male quality, but relatively little effort has been devoted to a similar evolutionary role for dimorphic traits. Male dimorphisms, associated with conditionally expressed alternative reproductive tactics, represent extreme phenotypic plasticity. Evidence suggests that considerable heritable variation exists in the‘liabilit’ underlying many threshold traits; if this liability is correlated with the genetic quality of males, dimorphic traits have the potential to be reliable indicators. We investigated the genetic architecture of phenotypically plastic morph expression in the context of condition–dependent signalling theory. Male morph in the mite Sancassania berlesei is condition dependent:‘fighter’ armed with thickened and sharp third pairs of legs emerge from heavier nymphs than unarmoured‘scrambler’. We selected on male morph in three replicate‘fighte’ and‘scrambler’ lines and recorded a significant response to selection over seven generations; this was due to a shift in the threshold reaction norm but the lines showed no correlated response in condition. This is inconsistent with models predicting a substantial genetic correlation between condition and sexual trait expression. We discuss why dimorphic sexual traits may show more condition–independent genetic variance than continuous sexual traits.
Getting lost in space: large sample analysis of the commute distance

The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an expression that does not take into account the structure of the graph at all and that is completely meaningless as a distance function on the graph. Consequently, the use of the raw commute distance for machine learning purposes is strongly discouraged for large graphs and in high dimensions. As an alternative we introduce the amplified commute distance that corrects for the undesired large sample effects.
A dead-zone free and linearized digital PLL

This paper implements a novel digital solution to avoid the problem of dead-zone behavior in digital phase locked loop (DPLL) caused by the quantization effect of time-to-digital converter (TDC). The dead-zone behavior results in chaotic limit cycle behavior causing higher than expected in-band phase noise and strong spurious tones. This behavior is dependent on the initial phase difference between the output and reference clock which makes the DPLL performance inconsistent and unpredictable. To alleviate this problem, a noise shaped offset is added to the phase error, in the digital domain to keep the TDC active and away from the dead-zone. The proposed solution is verified by extensive simulation and using a DPLL prototype in a 0.13 μm CMOS process.
Performance of drivable path detection system of autonomous robots in rain and snow scenario

Drivable path detection is an important factor to consider for a successful development of autonomous robot which is characterized as an intelligent vehicle. Researchers using different vision-based techniques have achieved remarkable result toward drivable path detection. Regardless of this achievement, environmental noise such as rain and/or snow can cause misdetection of drivable path which can lead to autonomous robot accident. In this paper, after investigating the effects of rain and/or snow, we introduced into the drivable path detection system a filtering algorithm that addresses the detection and removal of rain and/or snow for the optimization of the system. Experiments were carried out to show the effectiveness of the filter in the system. The results show that filtering algorithm assists the autonomous driving system in navigating perfectly during rain and/or snow scenario with minimal accident.
Combining polygonal and subdivision surface approaches to modeling urban environments

The economic production and the interactive rendering of complete reconstructions of populated urban environments are technically difficult tasks. Specialized modelling tools, which exploit knowledge of the types of object being modelled by working in the application domain, can be used to create appealing virtual reconstructions quickly. At the same time, the structural information from the modeller gives valuable hints to the renderer to determine efficient interactive display strategies through the use of level-of-detail and culling techniques. Thus a modeller that knows the operator is creating houses can use this information to simplify the user interaction, guide the operator, and to create models that build in optimisations when attempting real-time rendering. In this paper we discuss the way in which polygonal and multi-resolution surface techniques can complement one another in the modelling of urban environments. We also draw more general conclusions which apply to other software systems that share the same objective.
RLEX: Saftey and Data Quality in Reinforcement Learning-based and Adaptive Systems

The marriage of data management and machine learning is one of the most signiﬁcant trends in recent database research. Industry and academia have built systems to improve the efﬁciency of model training [3,5,7,20], studied problems in learning on normalized data [16,19], designed libraries for in-database learning [2,8, 10], explored how to apply data cleaning before learning [15]. As database researchers, it is natural to think machine learning as simply a form of high-dimensional aggregate query processing. However, unlike traditional SQL analytics, learning systems potentially create feedback loops where the learned models affect the future training data. For example, if a model recommends Pop music to European users–future data would have fewer examples of European preferences on other genres. To address such problems, we argue that learning systems need to move beyond the supervised learning paradigm and add support for Reinforcement Learning (RL) – drawing inspiration from recent successes of Google DeepMind on AlphaGo project [1]. Rather than predicting a single label for a single feature vector, RL algorithms learn stateful policies that output a sequence of predictions (called actions). As the name implies, these policies are learned through iterative trial-and-error (called exploration); optimizing an aggregate reward (utility of the predictions). In the music recommendation example, one could imagine a system that learns to recommend songs based on those songs previously heard by a user to maximize the total aggregate time spent on the platform. Such is the promise of RL, and in principle, RL subsumes all supervised learning models. However, this generality comes at a steep price. RL algorithms can spend signiﬁcant amounts of time exploring before learning a policy of value. It is further the case that many state-of-the-art RL algorithms have to store the entire execution trace of the system for learning [4]. While these algorithms do exploit parallelism [17] and GPUs [18], much of these
Knowledge and Tree-Edits in Learnable Entailment Proofs

This paper describes BIUTEE - Bar Ilan University Textual Entailment Engine. BIUTEE is a natural language inference system in which the hypothesis is proven by the text, based on linguistic- and world- knowledge resources, as well as syntactically motivated tree transformations. The main progress in BIUTEE in the last year is a new confidence model that estimates the validity of the proof found by BIUTEE.
Automated Production of Schematic Maps for Mobile Applications

The advent of high‐end miniature technology, together with the increasing availability of large scale digital geographic data products, has created a demand for techniques and methodologies that assist in the automated generation of maps specifically tailored to mobile GIS applications. This paper concerns itself with the problem of automatic generation of schematic maps. Schematic maps are diagrammatic representations based on linear abstractions of networks. In the context of mobile mapping they are seen as being a particularly useful means of displaying transportation networks. This paper describes an algorithm that automates the production of schematic maps. The algorithm makes use of the simulated annealing optimisation technique. An implementation of the algorithm is also presented, together with experimental results.
Ieee Transactions on Visualization and Computer Graphics Isa and Ibfvs: Image Space Based Visualization of Flow on Surfaces

— We present a side-by-side analysis of two recent image space approaches for the visualization of vector fields on surfaces. The two methods, Image Space Advection (ISA) and Image Based Flow Visualization for Curved Surfaces (IBFVS) generate dense representations of time-dependent vector fields with high spatio-temporal correlation. While the 3D vector fields are associated with arbitrary surfaces represented by triangular meshes, the generation and advection of texture properties is confined to image space. Fast frame rates are achieved by exploiting frame-to-frame coherency and graphics hardware. In our comparison of ISA and IBFVS we point out the strengths and weaknesses of each approach and give recommendations as to when and where they are best applied.
Construction of socio-economic status indexes for heads of rural farm families in the central agricultural zone of delta state, Nigeria

The specific objectives of the study were to collate, and validate socio-economic status indicators for heads of farm families in the agricultural zone. Stratified and multi-stage simple random sampling techniques were used in selecting the towns and respondents. Five percent (5%) of the respondents which corresponded to 147 heads of farm families were selected to constitute the sample. Data were collected by the use of interview schedule. The uniform scoring method was used to measure the variables. Data were analysed by the use of t-test and Point-Biserial correlation coefficient. A universe of 110 socio economic status indicators were collated. Sixty seven (67) socio-economic status indicators were valid. The valid socio-economic status indicators could be constructed into a scale to measure the socio-economic status of the heads of farm families in the agricultural zone. The knowledge of the socio-economic status of farm families is necessary in predicting the adoption behaviour and socio-economic well-being of the farmer.
Texture Mapping Based on Coefficient Map and Basic Texture Map

Texture mapping may give the impression of geometric details in a model using an image. But texture always was captured under special light condition. If the lighting in virtual environment is different from the texture image, the result of rendering will be incorrect and unrealistic. This paper proposes an image-based method that requires basic texture map and coefficient maps to interpolate light effective. This method uses a quadratic multinomial to fit the reflection model. The coefficients of quadratic multinomial will be gained from BTFs and are stored as coefficient maps. A picture is taken under wellproportioned environment light as a basic texture map, which the chromaticity is saved. The method can reconstruct the surface color under varying lighting conditions and represent the variation in surface color for each texel independently. Coefficient map and basic texture map make texture mapping become more realistic, simple and convenient.
CRLH TL-unit cell loaded tri-band circularly polarized crossed dipole antenna

In this paper, two unit cells of a via-less Composite Right/Left handed Transmission Line (CRLH TL) are loaded on the arms of a reference double sided printed dipole antenna to achieve tri-band operation. A pair of CRLH TL loaded tri-band dipoles are crossed through a vacant printed ring to obtain a tri-band circularly polarized (CP) operation. A cavity backed reflector is used with the tri-band CP crossed dipole to get high gain with unidirectional pattern (front to back lobe ratio > 25 dB) and wider CP operating bandwidth in all the three resonant frequency bands. The measurement results on the fabricated prototype confirm that the proposed tri-band cavity backed CP crossed dipole antenna exhibits CP operating bandwidth (S11 <; -10 dB and Axial Ratio <; 3 dB) of 1.22%, 3.69% and 10.78% centered at 1.64 GHz, 2.17 GHz and 2.69 GHz respectively.
Cognitive processes involved in smooth pursuit eye movements: behavioral evidence, neural substrate and clinical correlation

Smooth-pursuit eye movements allow primates to track moving objects. Efficient pursuit requires appropriate target selection and predictive compensation for inherent processing delays. Prediction depends on expectation of future object motion, storage of motion information and use of extra-retinal mechanisms in addition to visual feedback. We present behavioral evidence of how cognitive processes are involved in predictive pursuit in normal humans and then describe neuronal responses in monkeys and behavioral responses in patients using a new technique to test these cognitive controls. The new technique examines the neural substrate of working memory and movement preparation for predictive pursuit by using a memory-based task in macaque monkeys trained to pursue (go) or not pursue (no-go) according to a go/no-go cue, in a direction based on memory of a previously presented visual motion display. Single-unit task-related neuronal activity was examined in medial superior temporal cortex (MST), supplementary eye fields (SEF), caudal frontal eye fields (FEF), cerebellar dorsal vermis lobules VI–VII, caudal fastigial nuclei (cFN), and floccular region. Neuronal activity reflecting working memory of visual motion direction and go/no-go selection was found predominantly in SEF, cerebellar dorsal vermis and cFN, whereas movement preparation related signals were found predominantly in caudal FEF and the same cerebellar areas. Chemical inactivation produced effects consistent with differences in signals represented in each area. When applied to patients with Parkinson's disease (PD), the task revealed deficits in movement preparation but not working memory. In contrast, patients with frontal cortical or cerebellar dysfunction had high error rates, suggesting impaired working memory. We show how neuronal activity may be explained by models of retinal and extra-retinal interaction in target selection and predictive control and thus aid understanding of underlying pathophysiology.
Fuzzy Heterogeneous Neurons for ImpreciseClassi cation

In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and diierent neural archi-tectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classiication and diagnostic problems are here illustrated by experiments with data from a real world domain in the eld of environmental studies. These experiments show that such neurons can both learn and classify complex data very eeectively in the presence of uncertain information.
Massive MIMO Channel-Aware Decision Fusion

In this paper, we provide a study of channel-aware decision fusion (DF) over a “virtual” multiple-input multiple-output (MIMO) channel in the large-array regime at the DF center (DFC). The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the DFC. The aim is the development of (widely) linear fusion rules, as opposed to the unsuitable optimum log-likelihood ratio (LLR). The proposed rules can effectively benefit from performance improvement via a large array, differently from existing suboptimal alternatives. Performance evaluation, along with theoretical achievable performance and complexity analysis, is presented. Simulation results are provided to confirm the findings. Analogies and differences with uplink communication in a multiuser (massive) MIMO scenario are underlined.