Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphometric and Connectivity White Matter Abnormalities in Obsessive Compulsive Disorder

Two psychological mechanisms seem to be associated with the obsessive-compulsive cycle: (1) an emotional mechanism characterized by intense emotional arousal associated with intrusive thoughts of impending danger; (2) a cognitive mechanism exemplified by difficulties with inhibitory control. More recently, several studies found more extensive cognitive deficits in Obsessive Compulsive Disorder (OCD) beyond problems of inhibitory control and emotional regulation, namely: visual-spatial processing and memory. Thus, there is now extensive research showing that alterations of these psychological mechanisms in OCD (i.e., inhibitory control, emotional regulation, working memory, and visual spatial processing) are associated with morphological gray matter alterations in widespread brain regions. More recently, researchers have started looking at white matter abnormalities in OCD. In this article we review the research looking at white matter morphometric and structural connectivity alterations in OCD. Altogether, while some contradictory findings are still present, there is now evidence for widespread white matter morphometric and connectivity abnormalities affecting major white matter tracts (superior longitudinal fasciculus, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, cingulum bundle, semioval center, internal capsule, different regions of the corpus callosum, thalamic radiation, uncinate fasciculus and optic radiation) as well as white matter in regions adjacent to gray matter structures (superior frontal gyrus, dorsolateral prefrontal medial frontal cortex; inferior frontal gyrus, caudate, insulate cortex, parietal cortex, supramarginal and lingual gyri, and thalamus). These white matter alterations may help explaining the diversity of OCD psychological impairments in inhibitory control, emotional regulation, memory and visual spatial processing.
Climbing Performance Analysis of Radial Tire on Wet Pavement

Application of the Hydromechanics and the Momentum theorem, the driving critical velocity on wet pavement formula is established. It presents the tire-water-slope finite element modeling process. Through comparing the deformation rule of the loaded tire on horizontal ground and slope ground, analyses the tire grade ability on wet ground, and concludes the relationship between the tire load and the angle. Meanwhile compares the contact force change progress with the increased velocity when driving on the α=3°and α=8°pavement. And it estimates the critical velocity on wet slope.
Compositionality and the semantics of nominals

In this dissertation, the semantic and syntactic properties of the set of nouns which are known as agentave nominals are analyzed in the context of current concerns in computational lexical semantics. The aim of the analysis is twofold. From a theoretical perspective, it is argued that a rich enough view of lexical structure explains the polysemous behavior of agentive nouns and provides the basis for systematically capturing the semantic relation that a derived nominal bears to a correlated verbal form. From a computational perspective, it responds to the need of creating more efficient and linguistically motivated lexicons for natural language processing. Adopting the theoretical framework of Generative Lexicon theory, it is shown that, by recasting the notion of agentive nominalizations from the perspective of lexical semantics rather than morphology, the semantic interpretation of derived nominal forms is systematic and predictable. This is achieved by viewing morphological operations as instances of creative use of language that consistently derive new forms which are independently motivated in the lexicon. Thus, the correlations between lexically encoded base forms and morphologically derived forms provide a new paradigm of data showing that the distinction between different types of agentive nominals can be expressed in terms of stage-level and individual-level properties in the domain of nominals. Focusing on the role of events in the semantics of nouns, it is shown that stage-level and individual-level agentive nouns differ in the type and the quantification of their defining event. This is rejected in a variety of syntactic and semantic properties including temporal and locative modification, binding of pronouns, genericity, temporal constraints with the matrix predicate, among others. The lexical semantic account presented here strongly motivates the interest of adopting a generative lexical model of representation, which, not only makes the desired distinctions, but can be exploited in a variety of natural language processing tasks requiring linguistically-motivated solutions.
Sensemaking in a large-scale corporate fraud investigation: the challenge of communication

As part of the ‘Making Sense of Information’ project, at UCL Interaction Centre in London, we are currently performing field-studies in order to better understand the sensemaking challenges faced by lawyers and journalists. We are using our findings to explore implications for systems to support their work better. As part of our project, we have undertaken a case-study of a large corporate fraud investigation. This involved in-depth, retrospective interviews with 9 of the 30 lawyers who worked on the investigation, and provided us with 11 hours of interview data. This data has been analysed using Grounded Theory.
Parallel methods for real-time simulation of continuous systems

The authors explore the fields of digital continuous simulation and system modeling, highlighting areas in which efficiency can be improved by concentrating on how best to apply parallel processing techniques. They discuss a wide variety of parallel methods that can be applied to large-scale and complex continuous systems, forming a basis for real-time operation. A number of these techniques are being applied to the simulation of several real-world systems, including a guided-missile controller and the US Space Shuttle's main rocket engines. <<ETX>>
Mixed integer non-linear programming and Artificial Neural Network atch in competitive electricity

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illusowards iously [1–7] y mark should change regarding the operating decisions is becomi jor issue due to the changes in the market structure [9,16] eywords: ncillary services rtificial Neural Networks lectricity markets inear programming ixed integer non-linear programming ower systems trate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.
CENTRAL TIRE INFLATION: DEMONSTRATION TESTS IN THE SOUTH

Tests of prototype Central Tire Inflation (CTI) systems were conducted to quantify CTI performance, road wear, and truck vibration. The CTI systems were tested in both experimental and operational settings. Changes in the road surface that occurred during the tests could not be statistically attributed to reduced tire pressure. Vibration at the seat base, however, was significantly affected by tire pressure. In the operational tests, reduced tire pressure was associated with higher travel speeds. The implications of these findings for log transportation are discussed.
Extracting Provably Correct Rules from Artificial Neural Networks

Although connectionist learning procedures have been applied successfully to a variety of real-world scenarios, artificial neural networks have often b en criticized for exhibiting a low degree of comprehensibility. Mechanisms that automatically compile neural networks into symbolic rules offer a promising perspective to overcome this practical shortcoming of neural network repres entations. This paper describes an approach to neural network rule extraction based on Validity Interval Analysis (VI-Analysis). VI-Analysis is a generic tool for extracting symbolic knowledge from Backpropagation-style artificial neural networks. It does this by propagating whole intervals of activations through the network in both the forward and backward directions. In the context of rule extraction, these intervals are used to prove or disprove the correctness of conjectured rules . We describe techniques for generating and testing rule hypotheses, and demonstrate these using some simple classification tasks including the MONK’s benchmark problems. Rules extracted by VI-Analysis are provably correct. No assumpt ions are made about the topology of the network at hand, as well as the procedure employed for training the network.
Imbalance compensation and automatic balancing in magnetic bearing systems using the Q-parameterization theory

Rotor imbalance leads to sinusoidal disturbance forces which cause undesirable vibration. The problem is addressed using actively controlled magnetic bearing systems. First, the dynamics of the magnetic bearing are described in state space form using airgap displacement. Velocity and airgap flux as state variables. Second, the system, which is unstable in nature, is stabilized using the Q-parameterization theory. In order to compensate for the imbalance disturbance forces, the controller Q-parameter is chosen such that rejection of sinusoidal disturbances is achieved. In order to achieve automatic balancing, the imbalance is assumed as a sinusoidal noise in the measured signal, and the controller 4-parameter is chosen such that rejection of sinusoidal noise is achieved. In both cases the frequency of the sinusoidal disturbance/noise is assumed to be equal to the rotational speed. Simulation results are presented and show the robustness of the proposed controllers and that the rejection of sinusoidal disturbances is achieved. Also rotation of tile rotor around its axis of inertia is achieved.
Computation of greeks in financial markets driven by Lévy processes

This thesis is divided into 4 chapters. Chapter 1 gives a brief explanation of what the Greeks are and why they are of interest in applied financial mathematics. There is also a short summary of the first attempts at numerical methods to calculate the Greeks as well as an introduction to Levy processes. Chapter 2 starts with some relevant results from Malliavin Calculus and proceeds to derivations of general expressions for the most important Greeks using Malliavin weights. It concludes with a mathematical argument that shows how these weights can be regarded as optimal. Chapter 3 introduces stochastic volatility models followed by some more detailed analysis of a specific stochastic volatility model called the BarndorffNielsen and Shephard model. The technicalities involved in doing the necessary simulations for this model are discussed and implemented in Matlab. Chapter 4 contains a summary and outlines possible extensions to this thesis.
International Journal of Engineering

This paper proposes an effective multi-objective differential evolution algorithm (MDES) to solve a permutation flow shop scheduling problem (PFSSP) with the modified Dejong's learning effect. The proposed algorithm combines the basic differential evolution (DE) with local search and borrows the selection operator from NSGA-II to improve the general performance. First the problem is encoded with an appropriate rule to make the continuous nature of DE suitable for flow shop problems. Second, insert based local search is added in the initialization stage, as well as in each iteration to speed up convergence. The former guarantees that the algorithm commences with better solutions while the latter focuses the algorithm on promising areas. Third, in each generation, in order to improve diversity, two populations are introduced, current pop and advanced pop. The best solutions of each iteration are stored in the current pop, while the less than desirable solutions are added to the advanced pop. At the end of each generation, the two are combined and better individuals are selected for the next generation. The algorithm is then tested on benchmark problems to demonstrate its effectiveness and the results are discussed. Finally, a truncated version of Dejong's learning effect is proposed and MDES is used to solve the permutation flow shop with the modified learning effect.
Accounting for changing returns to experience

Abstract Returns to experience for U.S. workers have changed over the post-war period. This paper argues that a simple model goes a long way towards replicating these changes. The model features three well-known ingredients: (i) an aggregate production function with constant skill-biased technical change; (ii) cohort qualities that vary with average years of schooling; and crucially (iii) time-invariant age-efficiency profiles. The model quantitatively accounts for changes in longitudinal and cross-sectional returns to experience, as well as the differential evolution of the college wage premium for young and old workers.
Why do First-born Children Live with Parents?: Geography of the Family in Japan

In this paper, we analyze the strategic interaction between siblings who choose their residential locations in order to receive childcare assistance from their parents. Our threestage game yields a theoretical result that a first-born child lives closer to his/her parents than a second-born child, which is supported by our estimation results using Japanese micro data. We also show that the childcare assistance from parents is one of the main determinants of the residential location choice of siblings. This paper is the first to succeed in explaining the residential location choice of siblings in Japan using economic incentives.
A Secure and Efficient Direct Power Load Control Framework Based on Blockchain

Security and privacy in Direct Load Control (DLC) is a fundamental challenge in smart grids. In this paper, we propose a blockchain-based framework to increase security and privacy of DLC. We propose a method whereby participating nodes share their data with the distribution company in an anonymous and secure manner. To reduce the associated overhead for data dissemination, we propose a hash-based transaction generation method. We also outline the DLC process for managing the load in consumer site. Qualitative analysis demonstrates the security and privacy of the proposed method.
Detection of foreign words and names in written text

Tremendous research effort has gone into the field of natural language processing and understanding during the last half of the 20th century, yet it remains as one of the most challenging problems. Nevertheless, many companies have commercialized language processing applications such as Text-to-Speech (TTS) systems, domain-specific Automatic Speech Recognition (ASR) systems, Machine Translation (MT) systems, and limited-domain, speaker-dependent Speech-to-Speech (STS) prototype systems. While the quality of all natural language processing applications has improved steadily, they remain far from being perfect. Due to globalization and the widespread use of the Internet, occurrences of foreign entities—foreign words, names, locations, and events—in written text are becoming commonplace. This further complicates the automatic processing of natural language text. Identification of such foreign entities is important for several reasons. For example, TTS systems will usually fail to pronounce such entities properly in their native language as they try to sound them using the lexicon of the base language. These foreign entities also cause problems for MT programs where translation is initially done word by word. This dissertation simplifies the Naive Bayesian, N-gram based text-classification algorithm by taking its logarithm, naming it the Cumulative Frequency Addition (CFA) algorithm, and applies it to three text processing tasks: language identification, name-to-nationality identification, and detection of foreign words and names. In the language identification task CFA yields 100% accuracy on string sizes greater than 150 characters. In the name-to-nationality task, it yields 86% accuracy on a 14 country database and 96% on a 7 country database within the top three choices. Identifying the nationality, or at least the language group, of a person from his/her name, can be important not only for proper pronunciation but also for purposes of forensics studies and national security. Finally, in the task of detecting foreign words we obtain 66.9% accuracy. This is the first study to apply natural language processing techniques to the latter two tasks.
Multichannel Online Dereverberation Based on Spectral Magnitude Inverse Filtering

This paper addresses the problem of multichannel online dereverberation. The proposed method is carried out in the short-time Fourier transform (STFT) domain, and for each frequency band independently. In the STFT domain, the time-domain room impulse response is approximately represented by the convolutive transfer function (CTF). The multichannel CTFs are adaptively identified based on the cross-relation method, and using the recursive least square criterion. Instead of the complex-valued CTF convolution model, we use a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude, which is just a coarse approximation of the former model, but is shown to be more robust against the CTF perturbations. Based on this nonnegative model, we propose an online STFT magnitude inverse filtering method. The inverse filters of the CTF magnitude are formulated based on the multiple-input/output inverse theorem, and adaptively estimated based on the gradient descent criterion. Finally, the inverse filtering is applied to the STFT magnitude of the microphone signals, obtaining an estimate of the STFT magnitude of the source signal. Experiments regarding both speech enhancement and automatic speech recognition are conducted, which demonstrate that the proposed method can effectively suppress reverberation, even for the difficult case of a moving speaker.
Persian phonemes recognition using PPNet

In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian Consonant-Vowel Combination (PCVC) speech dataset. Nowadays, deep neural networks play a crucial role in classification tasks. However, the best results in speech recognition are not yet as perfect as human recognition rate. Deep learning techniques show outstanding performance over many other classification tasks like image classification, document classification, etc. Furthermore, the performance is sometimes better than a human. The reason why automatic speech recognition (ASR) systems are not as qualified as the human speech recognition system, mostly depends on features of data which is fed to deep neural networks. Methods: In this research, firstly, the sound samples are cut for the exact extraction of phoneme sounds in 50ms samples. Then, phonemes are divided into 30 groups, containing 23 consonants, 6 vowels, and a silence phoneme. Results: The short-time Fourier transform (STFT) is conducted on them, and the results are given to PPNet (A new deep convolutional neural network architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on separated Persian phonemes (Like in PCVC speech dataset). Conclusion: This method can be used not only for recognizing mono-phonemes but also it can be adopted as an input to the selection of the best words in speech transcription.
SFA: Small Faces Attention Face Detector

Tremendous strides have been made in face detection thanks to convolutional neural network. However, the performance of previous face detectors deteriorates dramatically as the face scale shrinks. In this paper, we propose a novel scale-invariant face detector, named Small Faces Attention (SFA) face detector, for better detecting small faces. We first present multi-branch face detection architecture which pays more attention to faces with small scale. Then, feature maps of neighbouring branches is fused so that the features coming from large scale can auxiliary detect hard faces with small scale. Finally, we simultaneously adopt multi-scale training and testing to make our model robust towards various scale. Comprehensive experiments show that SFA significantly improves face detection performance, especially on small faces. Our method achieves promising detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets, with competitive runtime speed. Both our code and model will be available at https://github.com/shiluo1990/SFA.
A Novel WLAN Client Puzzle against DoS Attack Based on Pattern Matching

Despite the popularity of 802.11 based networks, they suffer several types of DoS attack, launched by an attacker whose aim is to make an access point (AP) unavailable to legitimate users. One of the most common DoS attacks on 802.11 based networks is to deplete the resources of the AP. A serious situation like this can occur when the AP receives a burst of connection requests. This paper addresses this common DoS attack and proposes a lightweight puzzle, based on pattern-matching. Using a pattern-matching technique, this model adequately resists resource-depletion attacks in terms of both puzzle generation and solution verification. Using a sensible series of contextual comparisons, the outcomes were modelled by a simulator, and the security definition and proofs are verified, among other results.
Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics

Social media has gained an immense popularity over the last decade. People tend to express opinions about their daily encounters on social media freely. These daily encounters include the places they travelled, hotels or restaurants they have tried and aspects related to tourism in general. Since people usually express their true experiences on social media, the expressed opinions contain valuable information that can be used to generate business value and aid in decision-making processes. Due to the large volume of data, it is not a feasible task to manually go through each and every item and extract the information. Hence, we propose a social media analytics platform which has the capability to identify discussion pathways and aspects with their corresponding sentiment and deeper emotions using machine learning techniques and a visualization tool which shows the extracted insights in a comprehensible and concise manner. Identified topic pathways and aspects will give a decision maker some insight into what are the most discussed topics about the entity whereas associated sentiments and emotions will help to identify the feedback.