Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Linear Time-Frequency modeling and adaptive robust target detection in highly textured monovariate SAR image

Usually, in radar imaging, the scatterers are supposed to respond the same way regardless of the angle from which they are viewed and have the same properties within the emitted spectral bandwidth. Nevertheless, new capacities in SAR imaging (large bandwidth, large angular extent) make this assumption obsolete. An original application of the Linear Time-Frequency Distributions (LTFD) in SAR imaging allows to highlight the spectral and angular diversities of these reflectors. This methodology allows to transform a monovariate SAR image onto multivariate SAR image. Robust detection schemes in Gaussian or non-Gaussian background (Adaptive Matched Filter (AMF), Adaptive Normalized Matched Filter (ANMF), Anomaly Kelly Detector) associated with classical or robust Covariance Matrix Estimates (Sample Covariance Matrix (SCM), M-estimators) can then be applied exploiting these diversities. The combined two-methodologies show their very good performance for target detection.
An analysis of price impact functions of individual trades on the London Stock Exchange

Studying price impact is important in finance and previous work examines the relationship between trade size and price impact on a number of equity markets. In this study, using recent order book data from the London Stock Exchange, we examine the price impact function for six highly-liquid stocks and novelly investigate whether the function displays time-of-day effects. The results show that price impact exhibits a power-law scaling, and that price impact is highest in the first hour of the trading day and lowest in the last ninety minutes of trading.
DOES MONETARY POLICY GENERATE RECESSIONS?

We consider two kinds of answers to the title question: Do random shifts in monetary policy account for historical recessions, and would changes in the systematic component of monetary policy have allowed reductions in inflation or output variance without substantial costs. The answer to both questions is no. We use weak identifying assumptions and include extensive discussion of these assumptions, including a completely specified dynamic stochastic equilibrium model in which our identifying assumptions can be shown to be approximately satisfied.
Exhaustion of Type I Interferon Response following an Acute Viral Infection

Viral infections often cause a period of heightened susceptibility to a secondary infection but the cause of this phenomenon is unknown. We found that a primary viral infection in mice rapidly triggers an IFN-I-dependent partial activation state in the majority of B and T lymphocytes, which reverts to a resting phenotype within 5 days. When a secondary infection with an unrelated virus occurred 5 to 9 days after the primary infection, no recurrence of marked activation of lymphocytes was observed. This was not due to an inherent inability of the previously activated cells to undergo renewed partial activation, because they responded when challenged with virus after transfer into “naive” recipients. Instead, the failure to respond optimally resided in the original host’s incapacity to mount an IFN-I response to the secondary infection during this time period. Thus, transient immunosuppression through exhaustion of IFN-I production during an acute viral infection creates a time period of enhanced susceptibility to secondary infection.
ECG Biometric Recognition Without Fiducial Detection

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric, the human heartbeat, can be used for identity recognition. Existing approaches address the problem by using electrocardiogram (ECG) data and the fiducials of the different parts of the heartrate. However, the current fiducial detection tools are inadequate for this application since the boundaries of waveforms are difficult to detect, locate and define. In this paper, an ECG biometric recognition method that does not require any waveform detections is introduced based on classification of coefficients from the discrete cosine transform (DCT) of the Autocorrelation (AC) sequence of ECG data segments. Low false negative rates, low false positive rates and a 100% subject recognition rate for healthy subjects can be achieved for parameters that are suitable for the database.
A closed-form solution to the power minimization problem over two orthogonal frequency bands under QoS and Cognitive Radio interference constraints

This paper considers a Cognitive Radio (CR) channel composed of a secondary user (SU) and a primary user (PU). An analysis of the power minimization over several orthogonal frequency bands at the SU level under the following constraints is provided: a minimum Quality of Service (QoS) constraint, maximum peak and average interference to the PU constraints. The general solution, when it exists, is a water-filling type of solution which can be computed via iterative algorithms. It turns out that, in the case of two orthogonal bands a closed-form analytical solution can be found and a complete analysis of the feasibility of these opposing constraints is presented in details. Several numerical results that sustain and give inside into the analysis are also discussed.
Associating locations from wearable cameras

In this paper, we address a specific use-case of wearable or hand-held camera technology: indoor navigation. We explore the possibility of crowdsourcing navigational data in the form of video sequences that are captured from wearable or hand-held cameras. Without using geometric inference techniques (such as SLAM), we test video data for navigational content, and algorithms for extracting that content. We do not include tracking in this evaluation; our purpose is to explore the hypothesis that visual content, on its own, contains cues that can be mined to infer a person’s location. We test this hypothesis through estimating positional error distributions inferred during one journey with respect to other journeys along the same approximate path. The contributions of this work are threefold. First, we propose alternative methods for video feature extraction that identify candidate matches between query sequences and a database of sequences from journeys made at different times. Secondly, we suggest an evaluation methodology that estimates the error distributions in inferred position with respect to a ground truth. We assess and compare standard approaches from the field of image retrieval, such as SIFT and HOG3D, to establish associations between frames. The final contribution is a publicly available database comprising over 90,000 frames of video-sequences with positional ground-truth. The data was acquired along more than 3 km worth of indoor journeys with a hand-held device (Nexus 4) and a wearable device (Google Glass).
Patient-derived tumour xenografts for breast cancer drug discovery

Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer’s heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug–drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it.
Agglomerative clustering of a search engine query log

# $% '&( ) ) * + ! ' , * ' * ' . /0 ' 1 * . '&* ' % 2 ) *! 3 $% ) 4 ' " + $% ) * 65 7189 :, " ! ; * $4 ) @ & A B C D E . * . F ' G H ' IJ K L * M & + *) N~ z'cC g) ' &  p : q :  r Htd|#t9~,s tvuw x yFz {|;w]tS}9~ x `|; ~   C n+ U
A Feature-Based Food Recommendation on Apache Spark

With the improvement of living standards, people's demand for the quality of food is increasing. To provide a precise and efficient recommendation in this field, this paper proposes a parallel method based on food features on Apache Spark. This method finds similar users based on user evaluation on food expanded with food features, and products Top-N recommendations for the target user. We evaluate our approach by the data crawled from meishichina.com. The results show that this method can improve the accuracy and the parallelized calculation can greatly reduce the running time by adding work nodes into the cluster.
Contraction of the Southern Range Limit for Anadromous Oncorhynchus Mykiss

For many species, contraction of the geographic range limit nearest the equator is predicted by current global climate trends. Anecdotal data suggest that the southern range limit for anadromous populations of the fish Oncorhynchus mykiss has recently contracted northward; the contraction is consistent with the climate-change hypothesis, but is also consistent with the coincidental urbanization of the species’ southern range limit during the twentieth century. Distinguishing between the two mechanisms of range contraction is important in part to plan conservation measures for the species; and in part to accurately account for the impacts of climate change vs other impacts on species’ geographic distributions. Here we assess the current occurrence of anadromous O. mykiss in each coastal basin of southern California in which it occurred historically. The extent of the study is the region from Monterey Bay (the Pajaro River basin) southward to the US Border with Mexico; the grain of the study (the smallest unit for which occurrence is estimated) is the coastal basin. Two especially large basins—those of the Pajaro and Salinas rivers—were divided into sub-units. Ninety-two historic steelhead basins were identified, and occurrence was estimated in 86 of them through a combination of field reconnaissance and spot checks (snorkel surveys). The spot checks were conducted in the bestoccurring habitat of stream reaches with migration-connectivity to the ocean. The results indicated that between 58% and 65% of historic steelhead basins currently harbor O. mykiss populations. The pattern of basin-level extirpation showed a latitudinal gradient, with the highest extirpation rates in the south, confirming that a range contraction has taken place. Most of the extirpations (68%) were associated with anthropogenic barriers to fish migration (dams, flood-control structures, culverts, etc.). Historically, the probability of occurrence in a basin was correlated with basin area, but this correlation no longer holds. Barrier-associated extirpations were positively correlated with urban/agricultural development, whereas the “other” extirpations (i.e,. not associated with barriers) were positively correlated with mean annual air temperature in the basin. The results suggest that the range contraction is primarily an effect of lost migration opportunities arising from the urban/rural infrastructure; however climate and other stressors may have played a subsidiary role. Contraction of the southern range limit
Quadratic Stabilization of Discrete-Time Uncertain Nonlinear Multi-Model Systems using Piecewise A ne State-Feedback

In this paper a method for nonlinear robust stabilization based on solving a bilinear matrix inequality (BMI) feasibility problem is developed. Robustness against model uncertainty is handled. In different non-overlapping regions of the state-space known as clusters the plant is assumed to be an element in a polytope which vertices (local models) are a ne systems. In the clusters containing the origin in their closure, the local models are restricted to being linear systems. The clusters cover the region of interest in the state-space. An a ne state-feedback is associated with each cluster. By utilizing the a nity of the local models and the state-feedback, a set of linear matrix inequalities (LMIs) combined with a single nonconvex BMI are obtained which, if feasible, guarantee quadratic stability of the origin of the closed-loop. The feasibility problem is attacked by a branch-andbound based global approach. If the feasibility check is successful, the Liapunov matrix and the piecewise a ne state-feedback are given directly by the feasible solution. Control constraints are shown to be representable by LMIs or BMIs, and an application of the control design method to robustify constrained nonlinear model predictive control is presented. Also, the control design method is applied to a simple example.
Feature point extraction based on contour detection and corner detection for weld seam

In an arc welding robot system with vision sensor, the extraction precision of the weld seam position has an important effect on welding quality. However, due to disturbances such as camera resolution, scanning posture, reflections, etc., it is a great challenge to identify weld seam accurately. In this paper, weld seam is considered as a special curve which consists of feature points, based on that, an effective method is proposed to extract the feature points by using contour detection and corner detection. Experiment results show that feature points can be extracted successfully by using this method, so that the robot can weld the work part along the feature points detected. The limitation of this method is that the detected feature points are sometimes inconsistent with the weld center points, which may affect the welding quality.
Gradient Intensity Selectivity for Scalar Image Restoration using PDE

This book is the practical guide about designing, building and programming a modern robotics system. It presents a collection of wide range research results from the robotics community. Various aspects of current research in robotics area are explored and discussed. In particular, the major part of the book is devoted to robotic vision systems, robot navigation, human-robot interaction and adaptive learning systems. Different approaches include theorems and formal proofs for these systems are provided. Some advanced mathematics will be involved. This book is aimed at students and researchers who have an avid interest in this area will appreciate this user-friendly presentation of a wealth of robotics topics.
Natural draining model on sandy clay loam soil above impermeable layer in mining over burden disposal

Drawdown of groundwater table on mining over-burden disposal field had known will increase the disposal slope stability due to reduction on its inside effective stress. In fact, the drawdown has been happened because of the disposal drainage system and natural draining in dry condition. This paper would like to emphasize influence of natural sun-shining on the drawdown refers to disposal hydraulic parameter through negative-infiltration approach. A role model was running on the disposal layer with sandy clay loam as a study case. Final calculation resulted that it was needed 20 days left for declining the groundwater down to 2.25 meters. This conceptual model was expected to be an option of decision reference for mining operational to minimize some unnecessary vertical and horizontal drains.
Precipitation characteristics over East Asia in early summer: effects of the subtropical jet and lower-tropospheric convective instability.

AbstractA quasi-stationary front, called the baiu front, often appears during the early-summer rainy season in East Asia (baiu in Japan). The present study examines how precipitation characteristics during the baiu season are determined by the large-scale environment, using satellite observation three-dimensional precipitation data. Emphasis is placed on the effect of subtropical jet (STJ) and lower-tropospheric convective instability (LCI). A rainband appears together with a deep moisture convergence to the south of the STJ. Two types of mesoscale rainfall events (REs; contiguous rainfall areas), which are grouped by the stratiform precipitation ratio (SPR; stratiform precipitation over total precipitation), are identified: moderately stratiform REs (SPR of 0%–80%) representing tropical organized precipitation systems and highly stratiform REs (SPR of 80%–100%) representing midlatitude precipitation systems associated with extratropical cyclones. As the STJ becomes strong, rainfall from both types of meso.
An Unsupervised, Dual-Network Connectionist Model of Rule Emergence in Category Learning

We develop an unsupervised “dual-network” connectionist model of category learning in which rules gradually emerge from a standard Kohonen network. The architecture is based on the interaction of a statistical-learning (Kohonen) network and a competitive-learning rule network. The rules that emerge in the rule network are weightings of individual features according to their importance for categorisation. Once the combined system has learned a particular rule, it de-emphasizes those features that are not sufficient for categorisation, thus allowing correct classification of novel, but atypical, stimuli, for which a standard Kohonen network fails. We explain the principles and architectural details of the model and show how it works correctly for stimuli that are misclassified by a standard Kohonen network.
Motion control of a robot fish based on CPG

In this paper, we developed a robot fish consisting of a fish body, a two-linked fish tail and two 2-DOF pectoral fins. In order to make robot fish detect obstacles and perceive its postures, three infrared sensors and a gyro are fixed on the fish body. And an artificial CPG controller consists of four neural oscillators with sensory information feedback have been designed. This controller can generate rhythm instructions that can drive pectoral fins and tail fin flapping repeatedly. According to the relationship between motion patterns and parameters of CPG model, two feedback strategies are designed to control pitching and turning locomotion respectively. Simulations we have conducted show the applicability and validity of CPG model and feedback strategies
Cutting Edge: IL-26 Signals through a Novel Receptor Complex Composed of IL-20 Receptor 1 and IL-10 Receptor 2 1

The receptor for IL-26 (AK155), a cytokine of the IL-10 family, has not previously been defined. We demonstrate that the active receptor complex for IL-26 is a heterodimer composed of two receptor proteins: IL-20R1 and IL-10R2. Signaling through the IL-26R results in activation of STAT1 and STAT3 which can be blocked by neutralizing Abs against IL-20R1 or IL-10R2. IL-10R2 is broadly expressed on a wide variety of tissues, whereas only a limited number of tissues express IL-20R1. Therefore, the ability to respond to IL-26 is restricted by the expression of IL-20R1. IL-10, IL-19, IL-20, IL-22, and IL-24 fail to signal through the combination of IL-10R2 and IL-20R1 proteins, demonstrating that this receptor combination is unique and specific for IL-26.
Validation of Transcranial Doppler With Computed Tomography Angiography in Acute Cerebral Ischemia

Background and Purpose— Both transcranial Doppler (TCD) and spiral computed tomography angiography (CTA) are used for noninvasive vascular assessment tools in acute stroke. We aimed to evaluate the diagnostic accuracy of TCD against CTA in patients with acute cerebral ischemia. Methods— Consecutive patients presenting to the Emergency Department with symptoms of acute (<24 hours) cerebral ischemia underwent emergent high-resolution brain CTA with a multidetector helical scanner. TCD was performed at bedside with a standardized, fast-track insonation protocol before or shortly (<2 hours) after completion of the CTA. Previously published diagnostic criteria were prospectively applied for TCD interpretation independent of angiographic findings. Results— A total of 132 patients (74 men, mean±SD age 63±15 years) underwent emergent neurovascular assessment with brain CTA and TCD. Compared with CTA, TCD showed 34 true-positive, 9 false-negative, 5 false-positive, and 84 true-negative studies (sensitivity 79.1%, specificity 94.3%, positive predictive value 87.2%, negative predictive value 90.3%, and accuracy 89.4%). In 9 cases (7%), TCD showed findings complementary to the CTA (real-time embolization, collateralization of flow with extracranial internal carotid artery disease, alternating flow signals indicative of steal phenomenon). Conclusions— Bedside TCD examination yields satisfactory agreement with urgent brain CTA in the evaluation of patients with acute cerebral ischemia. TCD can provide real-time flow findings that are complementary to information provided by CTA.
Science on the net: an analysis of the websites of the European public research institutions

This article introduces a study on the websites of everal European public research institutions aimin g at identifying the science communication model chosen and implemented online with the purpose of reaching different target publics. The analytical a pproach takes into account a number of indicators: from the institutional identity to the scientific f eatures, from the interactive services to the internationalisation level, in order to evaluate wh et er the web provides an added value in the adopte communication model and in building a relation with the users. Lights and shades emerge from this study in which good practices side examples of a mu ch weaker science communication approach, outlining a general context where a public research institution website has been still used as a presentation tool and its interactive opportunities have not been capitalised.
Robust Least Squares Method for Sporadic E Ionospheric Clutter Mitigation in High Frequency Surface Wave Radar

The unwanted ionospheric clutter has proved to be the greatest impediment to achieving consistently good performance for high-frequency surface wave radar (HFSWR), A robust least squares algorithm based on the dual-polar receiver was proposed in this paper for sporadic E ionospheric clutter mitigation. The horizontal auxiliary channel was modified to get the best performance of clutter mitigation in the worst case error circumstance under the limit of maximum error bound. The optimal weight coefficient was solved efficiently by converting the min-max problem into a minimization problem. The experimental result shows the effectiveness of the proposed method