Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Lattice-Based Information Retrieval Theory

Logic-based Information Retrieval (IR) models represent the retrieval decision as an implication d → q between a document d and a query q, where d and q are logical sentences. However, d → q is a bi- nary decision, we thus need a measure to estimate the degree to which d implies q, noted P(d → q). The main problems in the logic-based IR models are the difficulties to implement the decision algorithms and to define the uncertainty measure P as a part of the logic. In this study, we chose the Propositional Logic (PL) as the underlying framework. We propose to replace the implication d → q by the material implication d ⊃ q. However, we know that there is a mapping between PL and the lattice theory. In addition, Knuth [13] introduced the notion of degree of inclusion to quantify the ordering relations defined on lattices. There- fore, we position documents and queries on a lattice, where the ordering relation is equivalent to the material implication. In this case, the impli- cation d → q is replaced by an ordering relation between documents and queries, and the uncertainty P(d → q) is redefined using the degree of inclusion measure. This new IR model is: 1- general where it is possible to instantiate most of classical IR models depending on our lattice-based model, 2- capable to formally prove the intuition of Rijsbergen about replacing P (d → q) by P (q|d), and 3- easy to implement.
Geometrical interpretation of quantum weak measurement

We revisit the quantum weak measurement (QWM) by sketching the polarisation state dynamics on the Poincaré sphere and the associated geometric phase, which is considered the soul of QWM. Our experimental arrangement comprises a coherent laser beam as a source of pure state, two polarizers corresponding to pre- and post-selection and a tilted wave plate placed in-between them to introduce weak interaction. The pre-selected state is a linearly polarised beam that can be represented as a point on the Poincaré sphere equator and the weak interaction with the wave plate results in a small spread in S3 axis. Now, an orthogonal projection of this state leads two different geodesics on the surface of the sphere through its poles, starting from both sides of the spread to the orthogonal point. The post-selection is made by moving the projection point slightly away from the orthogonal position so that both geodesics shift to the equator which results in a rapid accumulation of geometric phase in the beam crosssection. A gradient in the linear momentum is developed as a consequence which gives amplified shift in the beam position.
High-order integral nodal discontinuous Gegenbauer-Galerkin method for solving viscous Burgers' equation

ABSTRACT We present a high-order integral nodal discontinuous Galerkin (DG) method to solve Burgers' equation. The method lays the first stone of a novel class of integral nodal DG methods exhibiting exponential convergence rates in both spatial and temporal directions; thus, producing highly accurate approximations using a significantly small number of collocation points. This useful result is proven theoretically under some mild conditions. The paper also introduces the first rigorous rounding-error analysis for the Gegenbauer integration matrices proving their stability feature. Two useful strategies were proposed to significantly reduce the errors in certain special cases and to handle problems with relatively large time domains. Extensive numerical comparisons with other competitive numerical methods manifest the superior accuracy and efficiency of the proposed numerical method. The established numerical method is so accurate in general for sufficiently smooth solutions to the extent that exact, or nearly exact solutions can be achieved using relatively small collocation points as the viscosity parameter .
Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage, and Capacity

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering the massive number of embedded devices is a key challenge of designing IoT, since batteries increase the devices’ form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In this network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle, reflection coefficient, and the PB density. The effects of the parameters on network performance are quantified.
Is the ‘Visual Fields Easy’ Application a Useful Tool to Identify Visual Field Defects in Patients Who Have Suffered a Stroke?

Aims: To determine the level of agreement between the visual Fields easy application (VFE) for iPad and a standard clinical test for assessing peripheral vision in stroke survivors. Study Design: This was a prospective cross-sectional study comparing the VFE application to the Humphrey Field Analyser (HFA) SITA Fast c30-2 program in identifying and diagnosing visual field defects post-stroke. Place and Duration of Study: The ophthalmic department at Imperial College Healthcare NHS Trust. Data collection was undertaken between January 2016 and August 2016. Methodology: A total of 50 participants with a diagnosis of stroke and a suspected visual problem were recruited to the study. Normative data was collected from 50 participants with no history of stroke or visual loss. Analysis comprised of comparing the extent of the visual field loss detected by both the VFE and HFA, and clinically assessing the results for normality. Results: Bland-Altman analysis demonstrated that with more severe visual field loss, the agreement between both modalities was found to decrease. There was a higher proportion of false negatives with the VFE compared to the HVF. The bias towards detecting more missed test locations with the VFE application compared to the HFA was 6% for the normal participants and 2% for the stroke participants. The limits of agreement between the two modalities were large; 20% and 40% for the normal and stroke participants respectively. The sensitivity of the VFE application to determine an abnormal visual field in comparison to HFA was 88% and specificity was 76% in the stroke cohort based upon a clinical impression of its findings. The majority of stroke participants (88%) found the VFE test more comfortable to perform. Conclusion: As a screening tool, the VFE application is quick and easy to administer, preferred by patients and has good sensitivity and specificity for detecting the presence of an abnormal visual field when compared to HFA. In patients with extensive visual field loss, the VFE may overestimate visual field reduction.
MiddlePolice : Fine-Grained Endpoint-Driven In-Network Traffic Control for Proactive DDoS Attack Mitigation ∗ Extended Version for MiddlePolice [ 40 ] Published in ACM CCS 2016

1 ABSTRACT Volumetric attacks, which overwhelm the bandwidth of a destination, are amongst the most commonDDoS attacks today. One practical approach to addressing these attacks is to redirect all destination traffic (e.g., via DNS or BGP) to a third-party, DDoS-protection-asa-service provider (e.g., CloudFlare) that is well provisioned and equipped with filtering mechanisms to remove attack traffic before passing the remaining benign traffic to the destination. An alternative approach is based on the concept of network capabilities, whereby source sending rates are determined by receiver consent, in the form of capabilities enforced by the network. While both third-party scrubbing services and network capabilities can be effective at reducing unwanted traffic at an overwhelmed destination, DDoS-protection-as-a-service solutions outsource all of the scheduling decisions (e.g., fairness, priority and attack identification) to the provider, while capability-based solutions require extensive modifications to existing infrastructure to operate. In this paper we introduce MiddlePolice, which seeks to marry the deployability of DDoS-protection-as-a-service solutions with the destination-based control of network capability systems. We show that by allowing feedback from the destination to the provider, MiddlePolice can effectively enforce destination-chosen traffic control policies, while requiring no deployment from unrelated parties. Besides the above technical contributions, we also present what we learned through our industrial interviews with more than 100 interviewees from over 10 industry segments that are vulnerable to DDoS attacks. These profound discussions drive us to think what DDoS prevention really means for those who need protection, which may offer useful insight for the community to bridge the gap between academic research and industry practice.
Joint Unsupervised Learning of Deep Representations and Image Clusters Supplementary materials

This supplementary materials explain some implementation details and present additional experiments that are complementary to our main paper ”Joint Unsupervised Learning of Deep Representations and Image Clusters”. The source code for this work can be downloaded from https://github.com/jwyang/ joint-unsupervised-learning. 1. Affinity Measure for Clusters In this paper, we employ the affinity measure in [13] A(Ci, Cj) = A(Cj → Ci) +A(Ci → Cj) = 1 |Ci| 1T|Ci|WCi,CjWCj ,Ci1|Ci| + 1 |Cj |2 1T|Cj |WCj ,CiWCi,Cj1|Cj | (1) where W is the affinity matrix for samples, and WCi,Cj ∈ R|Ci|×|Cj | is the submatrix in W pointing from samples in Ci to samples in Cj , and WCj ,Ci ∈ R|Cj |×|Ci| is the one pointing from Cj to Ci. 1|Ci| and 1|Cj | are two vectors with all |Ci| and |Cj | elements be 1, respectively. Therefore, we have A(Ci, Cj) = A(Cj , Ci). According to (1), we can derive A((Cm ∪ Cn)→ Ci) = A(Cm → Ci) +A(Cn → Ci) (2) which has also been shown in [13]. Meanwhile, A(Ci → (Cm ∪ Cn)) = β1T|Cm|+|Cn|WCm∪Cn,CiWCi,Cm∪Cn1|Cm|+|Cn| = β1T|Cm|WCm,CiWCi,Cm1|Cm| + β1 T |Cn|WCn,CiWCi,Cn1|Cn| + β1T|Cm|WCm,CiWCi,Cn1|Cn| + β1 T |Cn|WCn,CiWCi,Cm1|Cm| (3) where β = 1/(|Cm|+ |Cn|). 2. Approximated Affinity Measure During agglomerative clustering, we need to re-compute the affinity between the merged cluster to all other clusters based on 2 and 3 repeatedly. It is simple to compute 2. However, to get A(Ci → (Cm∪Cn)), we need a lot of computations. These time costs become dominant and remarkable when we have a large-scale dataset. To accelerate the computations, we introduce an approximation method. At the right side of (3), we assume samples in Cm and Cn have similar affinities to Ci. This assumption is mild because the condition to merge Cm and Cn is that they are similar to each other. In this case, the ratio betweenWCi,Cm1|Cm| and WCi,Cn1|Cn| is analogy to the ratio between the number of samples in two set, i.e., WCi,Cm1|Cm| = |Cm| |Cn| WCi,Cn1|Cn| (4) Based on (4), we can re-formulate (3) to
Education and Health: The Role of Cognitive Ability

We aim to disentangle the relative impact of (i) cognitive ability and (ii) education on health and mortality using a structural equation model suggested by Conti et al. (2010). We extend their model by allowing for a duration dependent variable (mortality), and an ordinal educational variable. Data come from a Dutch cohort born between 1937 and 1941, including detailed measures of cognitive ability and family background in the final grade of primary school. The data are linked to the mortality register 1995-2011, such that we observe mortality between ages 55 and 75. The results suggest that at least half of the unconditional survival differences between educational groups are due to a 'selection effect', primarily on the basis of cognitive ability. Conditional survival differences across those having finished just primary school and those entering secondary education are still substantial, and amount to a 4 years gain in life expectancy, on average.
Measurement of the 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>H</mml:mi></mml:math>
 to 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:math>
 branching fraction at a 350 GeV and 3 TeV CLIC

In this paper we investigate the prospects for measuring the branching fraction of the Standard Model Higgs boson decay into a pair of $Z$ bosons at the future Compact Linear Collider (CLIC) at 350 GeV and 3 TeV centre-of-mass energies. Studies are performed using a detailed simulation of the detector for CLIC, taking into consideration all relevant physics and beam-induced background processes. It is shown that the product of the Higgs production cross-section and the branching fraction BR(${H\rightarrow\thinspace ZZ^\ast}$) can be measured with a relative statistical uncertainty of 20% (3.0%) at a centre-of-mass energy of 350 GeV (3 TeV) using semileptonic final states, assuming an integrated luminosity of 1 ab$^{-1}$ (5 ab$^{-1}$).
PSO-based Motion Fuzzy Controller Design for Mobile Robots

A motion control structure with a distance fuzzy controller and an angle fuzzy controller is proposed to determine velocities of the left-wheeled motor and right-wheeled motor of the two-wheeled mobile robot. A PSO-based method is proposed to automatically determine appropriate membership functions of these two fuzzy systems so that the controlled robot can move to any desired position effectively in a two-dimensional space. A ratio coefficient coding method and a variable fitness function are considered in the proposed PSO-based fuzzy controller design method so that the proposed PSO-based method with a good searching efficiency and the selected fuzzy controller with a good control performance.
ISSN 2348 – 0319 International Journal of Innovative and Applied Research (2016), Volume 4, Issue (1): 64- 76

1 Mehtab Singh and 2 Dr. Ram Niwas 1. Research scholar, Education Department, Guru Kashi University, Talwandi Sabo. 2. Assistant Professor (Deputy Dean), Guru Kashi University, Talwandi Sabo. Abstract: The present study is aimed to find out the role of parenting in formation of alcohol and drug attitude among secondary school students of district Faridkot of Punjab state. The data was collected from 300 randomly selected respondents from 10 secondary schools by using standardized questionnaire related to alcohol and drug attitude and parenting. Findings indicate boys were more inclined towards alcohol and drugs as compare to girls. Further, urban students showed more positive attitude towards drugs than rural. Also, the researchers found minutely negative coefficient of correlation between parenting and alcohol and drugs attitude which may conclude as inefficient parenting may leads to positive attitude towards alcohol and drugs.
Stream 2 D convolutions Foreground Stream 3 D convolutions Noise 100 dim Mask Foreground Background

We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene’s foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.
Nowcasting the Bitcoin Market with Twitter Signals

This paper analyzes correlations and causalities between Bitcoin market indicators and Twitter posts containing emotional signals on Bitcoin. Within a timeframe of 104 days (November 23rd 2013 - March 7th 2014), about 160,000 Twitter posts containing "bitcoin" and a positive, negative or uncertainty related term were collected and further analyzed. For instance, the terms "happy", "love", "fun", "good", "bad", "sad" and "unhappy" represent positive and negative emotional signals, while "hope", "fear" and "worry" are considered as indicators of uncertainty. The static (daily) Pearson correlation results show a significant positive correlation between emotional tweets and the close price, trading volume and intraday price spread of Bitcoin. However, a dynamic Granger causality analysis does not confirm a statistically significant effect of emotional Tweets on Bitcoin market values. To the contrary, the analyzed data shows that a higher Bitcoin trading volume Granger causes more signals of uncertainty within a 24 to 72-hour timeframe. This result leads to the interpretation that emotional sentiments rather mirror the market than that they make it predictable. Finally, the conclusion of this paper is that the microblogging platform Twitter is Bitcoin's virtual trading floor, emotionally reflecting its trading dynamics.
Computational Statistics and Data Analysis Using random subspace method for prediction and variable importance assessment in linear regression

Arandomsubsetmethod(RSM)withanewweightingschemeisproposedandinvestigated for linear regression with a large number of features. Weights of variables are defined as averages of squared values of pertaining t-statistics over fitted models with randomly chosen features. It is argued that such weighting is advisable as it incorporates two factors: a measure of importance of the variable within the considered model and a measure of goodness-of-fit of the model itself. Asymptotic weights assigned by such a scheme are determined as well as assumptions under which the method leads to consistent choice of significant variables in the model. Numerical experiments indicate that the proposed method behaves promisingly when its prediction errors are compared with errors of penalty-based methods such as the lasso and it has much smaller false discovery rate than the other methods considered.
Computational Statistics and Data Analysis Introduction to Face Recognition and Evaluation of Algorithm Performance

The field of biometric face recognition blends methods from computer science, engineering and statistics, however statistical reasoning has been applied predominantly in the design of recognition algorithms. A new opportunity for the application of statistical methods is driven by growing interest in biometric performance evaluation. Methods for performance evaluation seek to identify, compare and interpret how characteristics of subjects, the environment and images are associated with the performance of recognition algorithms. Some central topics in face recognition are reviewed for background and several examples of recognition algorithms are given. One approach to the evaluation problem is then illustrated with a generalized linear mixed model analysis of the Good, Bad, and Ugly Face Challenge, a pre-eminent face recognition dataset used to test state-of-the-art still-image face recognition algorithms. Findings include that (i) between-subject variation is the dominant source of verification heterogeneity when algorithm performance is good, and (ii) many covariate effects on verification performance are 'universal' across easy, medium and hard verification tasks. Although the design and evaluation of face recognition algorithms draw upon some familiar statistical ideas in multivariate statistics, dimension reduction, classification, clustering, binary response data, generalized linear models and random effects, the field also presents some unique features and challenges. Opportunities abound for innovative statistical work in this new field.
The waiting game: investigating the neurobiological transition from acute to persistent pain in adolescent rats

Abstract Persistent postsurgical pain affects 20% of youth undergoing a surgical procedure, with females exhibiting increased prevalence of chronic pain compared with males. This study sought to examine the sexually-dimorphic neurobiological changes underlying the transition from acute to persistent pain following surgery in adolescence. Male and female Sprague Dawley rats were randomly allocated to a sham or injury (plantar-incision surgery) condition and assessed for pain sensitivity while also undergoing magnetic resonance imaging at both an acute and chronic timepoint within adolescence. We found that injury resulted in persistent pain in both sexes, with females displaying most significant sensitivity. Injury resulted in significant gray matter density increases in brain areas including the cerebellum, caudate putamen/insula, and amygdala and decreases in the hippocampus, hypothalamus, nucleus accumbens, and lateral septal nucleus. Gray matter density changes in the hippocampus and lateral septal nucleus were driven by male rats whereas changes in the amygdala and caudate putamen/insula were driven by female rats. Overall, our results indicate persistent behavioral and neurobiological changes following surgery in adolescence, with sexually-dimorphic and age-specific outcomes, highlighting the importance of studying both sexes and adolescents, rather than extrapolating from male adult literature.
Statistical Mechanics of Self-Gravitating Systems in General Relativity JURY

The statistical mechanics of self-gravitating systems constitutes one of the most fascinating and puzzling fields of research. Due to the long-range nature of the gravitational force, the usual notion of statistical equilibrium is modified, making of this study an out-of-equilibrium problem. As a consequence, these systems exhibit some peculiar features such as the occurrence of phase transitions associated with a gravitational collapse. The work presented in this thesis aims at providing a detailed description of the phase transitions in a general relativistic framework by considering, in particular, the case of self-gravitating fermions. The thesis is conceptually divided in three parts, according to the degeneracy level of the system. We firstly focus our attention on the case of degenerate fermions (T = 0), by studying in detail the gravitational equilibrium. Successively, considering the high temperature limit (T >> 1), we show the existence of two kinds of gravitational collapse in the series of equilibria. Finally, we explore the general case, by illustrating the occurrence of the gravitational phase transitions, in both microcanonical and canonical ensembles.