Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Factors, Stock Price Paths, and Trading Volume

We examine the relation between the trading volume of a stock and the past stock price trading range. Volume is strikingly higher, in both economical and statistical terms, when the current price is above (below) the previous fifty-two week high (low). Results are robust to a wide range of controls, including returns and news events. An event study shows that volume spikes in the week when price leaves the prior trading range, then gradually returns to normal levels. The results suggest that psychological factors are pervasive and strong enough to be an important determinant of equities market volume.
European pollen-based REVEALS land-cover reconstructions for the Holocene: methodology, mapping and potentials

Abstract. Quantitative reconstructions of past land-cover are necessary for research into the processes involved in climate-human-land interactions. We present the first temporally continuous pollen-based land-cover reconstruction for Europe over the Holocene (last 11,700 cal yr BP). We describe how vegetation cover has been quantified from pollen records at a 1° × 1° spatial scale using the ‘Regional Estimates of VEgetation Abundance from Large Sites’ (REVEALS) model. REVEALS has been applied to 1128 pollen records across Europe and part of the Eastern Mediterranean-Black Sea-Caspian-Corridor (30°–75° N, 25° W–50° E) to reconstruct the cover of 31 plant taxa assigned to 12 plant functional types (PFTs) and three land-cover types (LCTs). A new synthesis of relative pollen productivities (RPPs) available for European plant taxa was performed for this reconstruction. It includes > 1 RPP values for 39 taxa, and single values for 15 taxa (total of 54 taxa). As an illustration, we present maps of the results for five taxa (Calluna vulgaris, Cerealia-t, Picea abies, Quercus deciduous and Quercus evergreen) and three LCTs (open land (OL), evergreen trees (ET) and summer-green trees (ST)) for 8 selected time windows. We discuss the reliability of the REVEALS reconstructions and issues related to the interpretation of the results in terms of landscape openness and human-induced vegetation change. We then describe the current use of this reconstruction and its future potential utility and development. The REVEALS data presented here can be downloaded from https://doi.pangaea. de/10.1594/PANGAEA.937075? format=html#download.
Estimation of Finite Population Mean and Superpopulation Parameters when the Sampling Design is Informative and Nonresponse Mechanism is Nonignorable

In this paper we study the joint treatment of not missing at random response mechanism and informative sampling for survey data. This is the most general situation in surveys and other combinations of sampling informativeness and response mechanisms can be considered as special cases. The proposed method combines two methodologies used in the analysis of sample surveys for the treatment of informative sampling and the nonignorable nonresponse mechanism. One incorporates the dependence of the first order inclusion probabilities on the study variable, while the other incorporates the dependence of the probability of nonresponse on unobserved or missing observations. The main purpose here is the estimation of finite population mean and superpopulation parameters when the sampling design is informative and nonresponse mechanism is nonignorable. Under four scenarios of sampling design and nonresponse mechanism, we obtained the method of moment estimators of finite population mean, with their biases and mean square errors. Furthermore, a four-step estimation method is introduced for the estimation of superpopulation parameters under informative sampling and nonignorable nonresponse mechanism. New relationships between moments of response, nonresponse, sample, sample-complement and population distributions were derived. Most estimators for finite population mean known from sampling surveys can be derived as a special case of the results derived in this paper.
Enhanced array design for tidal power generation

Tidal stream energy is a predictable source of renewable energy. Tidal stream turbines have been proposed as a way to extract useful energy from the tide. Many arrays of such devices will need to be installed to extract significant amounts of energy. The presence of an array of turbines within a tidal flow will impact the flowfield, as complex fluid interactions occur across multiple scales. This thesis is concerned with the behaviour of tidal turbines arrayed across channels. Experimental and analytical work is carried out to investigate array behaviour and to create new modelling tools to replicate this behaviour. Linear Momentum Actuator Disc Theory (LMADT) is employed to develop a new analytical model for a long row array of tidal turbines split into multiple smaller, co- linear row arrays. An argument of separation of scales is used to facilitate this model. It is found that increases in power extraction beyond that of a single continuous row array are possible. Experimental work is carried out on a row array of eight porous discs, simulating a short row array of tidal turbines. Disc porosity and spacing are varied to investigate thrust on the array, flow behaviour behind the array and an 'inferred' power removed from the flow. The results are compared to previously developed theoretical models. Good agreement is found with the trends of the analytical model, for example that there is a peak power coefficient which can be reached through appropriate selection of spacing and disc resistance. Differences from theory are found in the total thrust and power measurements, as well as in some aspects of the flow behaviour in the array wake. Reductions in thrust and power towards the ends of the array are also identified as 'end effects' which are not included in the analytical model. Based on these results a new semi-empirical model is proposed, using LMADT with experimental data closure. This model allows variation of the disc resistance across a row array. Values from the experimental work are used as inputs to the model, and the results compared to experimental measurements of flowspeed, thrust and power. Although agreement with experimental results is found in some areas, there are still some discrepancies between the analytical model and the experimental results. This indicates that there are additional factors that contribute to end effects on a short row array.
Theory and applications of toroidal moments in electrodynamics: their emergence, characteristics, and technological relevance

Abstract Dipole selection rules underpin much of our understanding in characterization of matter and its interaction with external radiation. However, there are several examples where these selection rules simply break down, for which a more sophisticated knowledge of matter becomes necessary. An example, which is increasingly becoming more fascinating, is macroscopic toroidization (density of toroidal dipoles), which is a direct consequence of retardation. In fact, dissimilar to the classical family of electric and magnetic multipoles, which are outcomes of the Taylor expansion of the electromagnetic potentials and sources, toroidal dipoles are obtained by the decomposition of the moment tensors. This review aims to discuss the fundamental and practical aspects of the toroidal multipolar moments in electrodynamics, from its emergence in the expansion set and the electromagnetic field associated with it, the unique characteristics of their interaction with external radiations and other moments, to the recent attempts to realize pronounced toroidal resonances in smart configurations of meta-molecules. Toroidal moments not only exhibit unique features in theory but also have promising technologically relevant applications, such as data storage, electromagnetic-induced transparency, unique magnetic responses and dichroism.
agomelatine in the treatment of depressive disorders in clinical practice: multicenter observational chr ONOs study

mBackground: CHRONOS was a large naturalistic study designed to evaluate the effectiveness and safety of agomelatine in the management of patients with major depression in routine clinical practice. Methods: Patients (n=6,276) with a moderate or severe major depressive episode without psychotic symptoms were treated initially as outpatients (80.2%) or in psychiatric facilities (19.8%) in 54 regions of the Russian Federation. Patients received a flexible-dosing regimen of agomelatine 25 mg or 50 mg once daily for 8 weeks, with frequent study visits (weeks 1, 2, 3, 4, 6, and 8). Results: Patients (mean age 44 years, 72.6% female) showed progressive improvement on the 17-item Hamilton Rating Scale for Depression (HAMD-17) total score from 22±6.9 at baseline to 4.7±4.7 at week 8 (P,0.0001). The proportion of responders (HAMD-17 decrease of $50%) was 90.1% and the proportion of remitters (HAMD-17 ,7) was 79.1% at week 8. All individual HAMD-17 item scores improved rapidly, and the change relative to baseline was significant (P,0.0001) at week 1 and at each subsequent visit in all cases. There were corresponding rapid improvements in Clinical Global Impression Severity and Improvement scores. In the subgroup of patients with more severe illness (HAMD-17 $21 at baseline; n=3,478), the proportions of responders and remitters were 92.4% and 72.8%, respectively, at week 8. Conclusion: Agomelatine was effective and well tolerated in a large sample of depressed patients in an observational treatment setting, and showed a rapid onset of benefit across all
An 18‐year climatology of hailstorm trends and related drivers across southeast Queensland, Australia

The southeast Queensland (SEQ) region of Australia is recognised for frequent thunderstorms documented through numerous studies including high‐impact severe thunderstorm cases which have caused insured losses exceeding $1 billion AUD. Despite a modest body of scientific literature, basic questions regarding the role of climate, synoptic and local‐scale (<10 km) processes affecting the variability of thunderstorms still remain. In an effort to advance these questions as part of the Coastal Convective Interactions Experiment (CCIE), this study integrates multiple datasets across an 18‐year period (July 1997–June 2015) to provide a mesoscale climatological analysis of the SEQ hailstorms and associated environmental conditions. On a multi‐year time‐scale, the relationship between the El Niño Southern Oscillation and hailstorm frequency is consistent with previous studies. On synoptic scales, a southeasterly change situation coupled with a sea breeze was found to provide the most favourable environment for hailstorms, particularly for southwest SEQ. On the local scale, hail development within convective cells was found to be most frequent within the inland limb of the maritime air mass on sea‐breeze days. This observation suggests the sea‐breeze air masses may become favourable for convection after sufficient modification during inland propagation.
Legendre Polynomials Approach for Bi-Level Linear Fractional Programming Problem

This paper presents a solution method of bi-level linear fractional programming problem (BLLFPP) using of Legendre polynomials. The Legendre polynomial is a series expansion that a representation of a function f(x) which is finite and single- valued in the interval (a, b). Levels are classified as upper level and lower level, and they are weighted with respect to their classes before Legendre polynomials approach unified levels by using their weights. Thus, the problem is reduced to a single objective. Numerical example is provided to clarify the efficiency and feasibility of the suggested approach.
Scattering from an object behind multi-layer periodic gratings of cylindrical shapes

Abstract The problem of electromagnetic scattering from a perfect electric conductor (PEC) object of arbitrary cross-section behind multilayer of periodic gratings of circular cylinders is solved by a hybrid method combining the Method of Moments (MoM), decomposition, and the Physical Optics methods. The periodic grating’s periodic cell consists of multiple layers of lossy-dielectric scattering elements situated in free space. The PEC object under the periodic gratings assumed to be electrically large. A set of electric-field integral equations (EFIEs) for the perturbation currents both on the portion of the PEC object and the periodic gratings most strongly interacting with each other obtained using the surface equivalence principle and a decomposition method. Then, MoM is used to solve the EFIEs in the frequency domain to obtain the scattered electric field.
S 3 CMTF : Fast , Accurate , and Scalable Method for Sparse Coupled Matrix-Tensor Factorization

How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for the purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose SCMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not support sparse tensors or do not model complicated relationships between factors, SCMTF provides sparse Tucker factorization by carefully deriving gradient update rules. We also show that lock-free parallel SGD is useful for SCMTF in multi-core shared memory systems. SCMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that SCMTF is the fastest, outperforming existing methods. Experimental results show that SCMTF is up to 989× faster than existing methods while providing the best accuracy. SCMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply SCMTF to Yelp recommendation tensor data coupled with 3 additional matrices to discover interesting patterns.
Enabling Quality Control for Entity Resolution: A Human and Machine Cooperative Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperative (HUMO) framework for entity resolution (ER), which divides an ER workload between machine and human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternative in quality control.
tau-FPL: Tolerance-Constrained Learning in Linear Time

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed tau-FPL over the existing approaches.
Observer Design for a Class of Discrete-Time Takagi-Sugeno Implicit Models Subject to Unknown Inputs

This paper investigates the problem of the fuzzy unknown inputs observer (FUIO) design for a class of discrete-time Takagi-Sugeno implicit models (DTSIMs) with unmeasurable premise variables which satisfying Lipschitz conditions. The unknown inputs (UIs) affect both state and output of the model. The idea of the proposed result is based on the separation between dynamic and static equations in the considered DTSIM. First, the method used to separate dynamic equations from static equations is developed. Next, based on the augmented system structure which contains the dynamic equations and the unknown inputs, a new observer design in explicit structure to estimate simultaneously the system state and the unknown inputs is established. The convergence of the state estimation error of the augmented system is studied by using the Lyapunov theory and the gain matrix of the FUIO is obtained by solving only one linear matrix inequality (LMI). At last, an illustrative example is given to show the effectiveness of the proposed technique.
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy

Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing every pixel of every frame assuming white-box policy access, in this paper we take a more restrictive view towards adversary generation - with the goal of unveiling the limits of a model's vulnerability. In particular, we explore minimalistic attacks by defining three key settings: (1) black-box policy access: where the attacker only has access to the input (state) and output (action probability) of an RL policy; (2) fractional-state adversary: where only several pixels are perturbed, with the extreme case being a single-pixel adversary; and (3) tactically-chanced attack: where only significant frames are tactically chosen to be attacked. We formulate the adversarial attack by accommodating the three key settings and explore their potency on six Atari games by examining four fully trained state-of-the-art policies. In Breakout, for example, we surprisingly find that: (i) all policies showcase significant performance degradation by merely modifying 0.01% of the input state, and (ii) the policy trained by DQN is totally deceived by perturbation to only 1% frames.
Allocation de ressources : optimisation des symboles pilotes et de la voie de retour

Dans cette these, nous proposons d'optimiser la longueur de la sequence d'apprentissage quand un detecteur base sur le critere Maximum a posteriori (MAP) est utilise. Nous considerons une transmission mono-antenne sur un canal selectif en frequence et une transmission multi-antennes (MIMO: Multiple-Input Multiple-Output) sur un canal non selectif en frequence. Nous etudions le cas d'un recepteur non iteratif et le cas d'un turbo-detecteur compose d'un detecteur MAP d'un decodeur MAP. La longueur optimale de la sequence d'apprentissage est trouvee en maximisant un Rapport Signal a Bruit (RSB) utile qui tient compte de la perte en termes de debit due a l'utilisation des symboles pilotes. Nous considerons ensuite un systeme OFDM (Orthogonal Frequency Division Multiplexing) mono-antenne. Nous considerons le cas ou le mode FDD (Frequency Division Duplexing) est utilise. Nous etudions le probleme de l'optimisation conjointe de la voie de retour et de l'allocation des puissances. Nous proposons aussi des algorithmes d'allocation de la modulation a faible complexite utilisant une quantite limitee de ressources sur la voie de retour pour un systeme OFDM. Nous mettons en evidence deux methodes pour utiliser la voie de retour et donnons une condition analytique permettant de choisir entre ces deux methodes.
SHREC’08 entry: Registration and retrieval of 3D faces using a Point Distribution Model

We present a 3D face retrieval approach based on the fitting of a 3D Point Distribution Model (PDM). The model is fitted on a face without relying on texture, pose or orientation information. The retrieval is based on fine registration obtained using anthropometric knowledge and facial landmarks that are localized using the model.
Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Runtime Analyses for Using Fairness in Evolutionary Multi-objective Optimization Runtime Analyses for Using Fairness in Evolutionary Multi-objective Optimization

It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced in [Laumanns, Thiele, Zitzler, IEEE TEC 2004]. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations, where the right mechanism can speed up the optimization process significantly. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically.
Causal structure-based root cause analysis of outliers

Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify “root causes” of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each variable to the target outlier score, which explains to what extent each variable is a “root cause” of the target outlier. We study the empirical performance of the method through simulations and present a real-world case study identifying “root causes” of extreme river flows.
Advances in Neural Information Processing Systems 8 ( NIPS * 95 )

Visual cognition depends critically on the ability to make rapid eye movements known as saccades that orient the fovea over targets of interest in a visual scene. Saccades are known to be ballistic: the pattern of muscle activation for foveating a prespecified target location is computed prior to the movement and visual feedback is precluded. Despite these distinctive properties, there has been no general model of the saccadic targeting strategy employed by the human visual system during visual search in natural scenes. This paper proposes a model for saccadic targeting that uses iconic scene representations derived from oriented spatial filters at multiple scales. Visual search proceeds in a coarse-to-fine fashion with the largest scale filter responses being compared first. The model was empirically tested by comparing its performance with actual eye movement data from human subjects in a natural visual search task; preliminary results indicate substantial agreement between eye movements predicted by the model and those recorded from human subjects.
NUMERICAL SOLUTION OF TRANSITIONAL FLOWS USING EARSM WITH ALGEBRAIC MODEL OF TRANSITION

The contribution deals with the numerical simulation of laminar-turbulent transition using algebraic model based on computation of the intermittency coefficient. Algebraic model of transition is implemented to the in-house software for simulation of 2D compressible turbulent flows using the Reynolds averaged Navier-Stokes equations (RANS) closed by the explicit algebraic Reynolds stress model (EARSM). The numerical solution is obtained by the finite volume method based on the HLLC scheme and explicit TVD Runge-Kutta method. The proposed method is validated on the ERCOFTAC T3 test cases.