Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter Clustering in a High-Dimensional Multinomial Choice Model

Parameter estimation in multinomial choice models becomes infeasible when the number of alternatives is large. As the parameters are alternativespecific, the number of parameters grows with the number of alternatives. Furthermore, in many applications the explanatory variables describe clusters which enter the model as large sets of dummies. This paper develops techniques for data-driven clustering over outcome categories and explanatory dummy parameters in a multinomial probit setting. A Dirichlet process mixture encourages parameters to cluster over the choice categories or explanatory categories, which favours a more parsimonious model without imposing any model restrictions. Simulation studies show that parameter clustering can improve greatly upon standard choice models in terms of predicting categories and fitting the category probabilities for many choice alternatives. The methods are applied to a high-dimensional choice data set of holiday destinations of a Dutch household panel.
Fourier series of sums of products of Genocchi functions and their applications

Recently, Luo introduced Fourier expansions of Apostol-Bernoulli, Apostol-Euler and Genocchi polynomials and investigated some interesting identities and properties of these polynomials by using Fourier series. In this paper, we consider three types of functions given by sums of products of Genocchi functions and derive their Fourier series expansions. In addition, we will express each of them in terms of Bernoulli functions. c ©2017 All rights reserved.
Discovering semantic relations by means of unsupervised sense clustering

Electronic sense inventories are needed for Word Sense Disa mb guation in Natural Language Processing (NLP) applicati ons. However, existing sense inventories are criticized as being inadequ at for this task. The word senses proposed in these resource are listed without any information on their distinguishability and their rela tions. Furthermore, the fine granularity of the senses is not always needed in NLP applications. Nevertheless, this type of knowledge can be a utomatically extracted from textual data using machine lea rning techniques. In this paper, we show how an unsupervised sense induction me thod permits to capture two different types of semantic rela tions: first, the semantic similarity relations between the translation equivalents of polysemous words; second, the relations bet ween their senses, which are automatically identified by means of semantic clus tering. We analyze the results of this method and compare the m with the semantic descriptions provided in a typical multilingual s emantic resource.
Oral Controlled Release Drug Delivery System : A Promising Approach for the Treatment of Ulcerative Colitis

Ulcerative colitis is an idiopathic, chronic and relapsing intestinal inflammatory disorder of digestive tract which is represented by bloody diarrhoea, tenesmus, anaemia, bowel distension. In Western Europe and in the USA, ulcerative colitis has a proportion of approximately 6 to 8 cases per 100,000 peoples and an approximated prevalence of around 70 to 150 per 100,000 peoples. Proper local targeting is the major challenge in the treatment of ulcerative colitis. To minimize these challenges oral controlled drug delivery system seem to be a promising approach for colon targeting. The main motive of this article is to explore the role of oral controlled drug delivery systems in ulcerative colitis for appropriate targeting of drug entities to the colon. The increased demand of these dosage forms is because of the alertness to toxicity and ineffectiveness of oral conventional approach in the form of capsules and tablets. Generally, conventional formulations create fluctuation in drug concentration in the tissues and bloodstream with ensuing undesirable toxicity and poor efficacy. These limitations of conventional treatment such as fluctuation in drug concentration, the inappropriate drug at the target site and repetitive dosing could be overcome by the oral controlled drug delivery system. The present article contains brief review on the pathophysiology of ulcerative colitis and several formulation approaches used for colon targeting by using the oral controlled drug delivery system.
COMPARISON OF AUTONOMIC CARDIOVASCULAR FUNCTIONS USING HEART RATE VARIABILITY TEST IN YOUNG OBESE AND NON OBESE MALES AND FEMALES *

Obesity is often defined simply as a condition of abnormal or excessive fat accumulation in adipose tissue, obesity produces an increment in total blood volume and cardiac output that is caused in part by the increased metabolic demand induced by the excess body weight, since cardiovascular system have been reported to be the one of the major system reflecting the adverse effects of obesity, the present study is undertaken to assess the cardiovascular autonomic functions using Heart rate variability (HRV) in obese young Males and Females and compare the results with the controls.
FIRST RECORD OF PARACOCCUS MARGINATUS ( HEMIPTERA : PSEUDOCOCCIDAE ) , AN INVASIVE ALIEN SPECIES ON PAPAYA ( CARICA PAPAYA L . ) IN JAMMU ( J & K ) , INDIA

The papaya mealybug, Paracoccus marginatus, an invasive alien species found to heavily infest papaya (Carica papaya), the major host. Paracoccus marginatus (Hemiptera: Pseudococcidae), was first described by Williams and Granara de Willink in 1992. It is a polyphagous species native to Mexico and Other Central American Countries. It is also recorded from many countries of the Caribbean region, causing huge economic losses to papayas since 1994. Paracoccus marginatus is a small hemipteran that attacks several genera of plants (more than 40 species), including economically important fruits, vegetables and ornamentals. It potentially posed a serious threat to numerous agricultural crops. Integrated pest Management (IPM) was identified as a key component in a management strategy for the papaya mealybug. Paracoccus marginatus damaging papaya in Jammu region is reported for the first time. Besides it, the fundamental biological data of Paracoccus marginatus, its existing host plants and its distribution in India and around the world was described.
Progression in Multiple Representations : Supporting students ’ learning with multiple representations in a dynamic simulation-based learning environment

Relating multiple representations and translating between them is important to acquire deeper knowledge about a domain. To relate representations, learners have to mentally search for similarities and differences. To translate between representations, learners need to interpret the effects that changes in one representation have on corresponding representations. The question is how presenting representations may improve or hinder the processes of relation and translation. In this study we examined the effect of sequencing dynamic representations on learning outcomes. Two versions of the same simulation-based learning environment, that of the physics topic of moments, were compared: a learning environment providing the representations step-by-step (experimental condition) and a learning environment providing all representations at once (control condition). The subjects were 120 students from secondary vocational education (aged 15 to 21). Overall, we found the subjects learned from working in the learning environment; the post-test scores on the domain and understanding items were significantly better than the pre-test scores. This was true for both the subjects with and without prior knowledge on the domain. Moreover, the subjects with prior knowledge scored significantly better on both the pre-test and the post-test compared to the subjects without prior knowledge. Despite our expectations, no differences were found between the two experimental conditions. The subjects learned equally well regardless of the way in which the representations were presented. Also, the extent to which the subjects’ experienced complexity of both the topic and the learning environment did not differ between the experimental conditions.
Wireless communication and SNS (Social Networking Service) linkage pattern method

The invention discloses a wireless communication and SNS (Social Networking Service) linkage pattern method in the technical field of communication. The method comprises the steps that wireless network equipment is used for transmitting a wireless network signal; each set of communication equipment is connected to a wireless signal to initiate a network connecting request; a wireless network server page is accessed; a server pushes an SNS account adding request or an EMAIL registration request; the Internet is accessed for browsing through account adding or registration confirmation. By using the pattern provided by the invention, for enterprises or individuals which require for SNS marketing, a great number of access users is continuously accumulated through wireless network service, the problem that the SNS account concerning users are few is solved, various patterns of advertisement are transmitted through an SNS platform and information can be directly pushed in a wireless network coverage area.
Energy Constrained Resource Scheduling for Cloud Environment

Cloud Computing is used to access computing resources owned and operated by a third-party provider. It is internet-based computing to share resources, software and information. The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In existing system, dynamic server consolidation through live migration is an efficient way towards energy conservation in cloud data centers. It keeps the number of power-on systems as low as possible which is achieved by implementing PABFD algorithm with the help of MMT. The main drawback is that when the systems are in power off state will also increases power consumption when starts immediately. In proposed system, Power management strategies have been proposed for enterprise servers based on Dynamic Voltage and Frequency Scaling (DVFS). DVFS allows the server to transition the processor from highpower states to low-power states. The processors are assigned to deep sleep to reduce energy consumption. In deep sleep the server can be configured to use Direct Memory Access (DMA) to place incoming packets into memory buffers for processing in the active state. Request grouping will group received requests into batches and put the processor into sleep between the batches. The Virtual grouping scheme is enhanced to manage resources with load balancing mechanism. The system is improved with optimization mechanism to manage relative response time. Resource levels and application requirements are integrated in the allocation process. The system is adopted to support Dynamic Random Access Memory (DRAM) and Dual in-line Memory Module (DIMM) components.
How Alternative Are Private Markets ? 1

We present a new and flexible methodology to build factors in illiquid markets, from an unbalanced panel of smoothed asset returns. We apply this methodology to a large and unique panel of private market funds. We build a set of eight private factors that capture nearly 60% of the variation in private market returns. Four of these factors command a risk premium above 3%, but half of the variation in their returns is explained by standard listed equity factors. Exposure to these factors can be gained by forming a portfolio with given fund characteristics that include region of investment, industry focus, investment strategy, and fund size. We thank Burgiss for supplying the data, and especially James Bachman for feedback and data guidance, and Wendy Hu for patiently executing all of our codes and sending us the anonymized output. We thank Pierre CollinDufresne, Francesca Cornelli, Thierry Foucault, Andras Fulop, René Garcia, Amit Goyal (discussant), Emmanuel Guerre, Michel Habib, Emmanuel Jurczenko (discussant), Kevin Kaiser, Tarun Ramadorai, Sergei Sarkissian (discussant), Berk Sensoy (discussant), seminar participants at Amsterdam U. , Bank of Portugal, Bath U. , ESSEC, INSEAD, HEC Paris, Liverpool U. , London School of Economics, Paris Dauphine U. , and Zurich U. , participants to the HF&PE Paris-Dauphine Conference, the ESSEC Amundi Workshop, the 7th Luxembourg Asset Management Summit, the 2018 PERC Symposium, the 6th ESSEC Empirical Finance Workshop, the 2019 SFS Cavalcade and the Glion Private Capital Conference. We are also grateful to the Q-Group for the 2018 Jack Treynor Prize. The authors have consulted to various financial institutions. In particular, Ludovic Phalippou has been an advisor on private market research for the Blackrock Investment Institute. None of the authors have any conflicts of interest to report. 2 Yale School of Management International Center for Finance; National Bureau of Economic Research (NBER), 165 Whitney Ave., P.O. Box 208200, New Haven, CT 06520-8200, United States; tel: (+1)2034325950; Email: william.goetzmann@yale.edu. 3 Corresponding author. ESSEC Business School and Centre for Economic Policy Research (CEPR), 3 avenue Bernard Hirsch, 95021 Cergy-Pontoise, France; tel: (+33)1 3443 3270; Email: elise.gourier@essec.edu. 4 University of Oxford Said Business School, Park End Street, Oxford, OX1 1HP, United Kingdom; tel: (+44) 1865 288 719; Email: ludovic.phalippou@sbs.ox. ac.uk.
Performance Analysis of different Discrete Time and Discrete Amplitude Representation Techniques used for Encoding the Speech Waveform Digitally

Abstract —This study is mainly based on comparison between different source coding techniques on the basis of their signal to quantization noise ratio.On the basis of the observation some perceptual and finer adjustments were done to find optimized parameters for different techniques. The main problem for a uniformly quantizer was its deteriorated performance when the amplitude of signal exceeds the dynamic range of quantizer which in turn leads to signal dependent noise components leading to less SNR. So to rectify this we go for compression of signal that is to be presented to the quantizer. Also to increase the performance we introduced a concept of adaption in step size of the quantizer which is based on the input signal. Along with this, two bandwidth reducing techniques namely differential pulse code modulation (DPCM) and Adaptive Pulse code modulation (ADPCM) techniques were compared on the basis of Signal to noise ratio and it was found that for low signaling rate ADPCM was best but after an instant Pulse code modulation technique outperforms the other three with respect to signal to noise ratio. Lastly ,we found that when we observe the coding techniques involving one bit quantizer the SNR was largely depending on the amount of fluctuations in input speech signal for optimized delta modulation scheme whereas in case of adaptive delta modulation scheme it mainly depended on the initial amplitude of speech signal.
A Survey on Trust Management Mechanism for Internet of Things

The Internet is populated with billions of electronic contraptions that have turned into a piece of our texture. Trust administration assumes an essential part in IoT for dependable information combination and reliable information, qualified administrations with setting – mindfulness, and improved client protection and data security.In network arrangement reliable information handling in remote sensor systems is a quickly rising examination theme. In remote sensor arrange calculation is regularly considerably less vitality devouring than correspondence. Reliability of sensor information is most critical part when detecting undertaking done in remote sensor arrange. In this paper we discuss about the trust management mechanism, wireless sensor network, Internet of Things architecture, and also give the literature survey of some papers. Keywords—Internet of Things, Trust Management Mechanism, Wireless sensor Network, Trustworthy sensor data __________________________________________________*****_________________________________________________
Reinforcement Learning for Multi-Scale Molecular Modeling

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to take advantage of both strategies. In this approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.
OPTICAL BISTABILITY WITH A PLASMONIC CHALCOGEN-BASED WAVEGUIDE STRUCTURE

We analyzed theoretically and experimentally the coupling of Ar laser radiation (502 nm wavelength) as TM modes into a planar waveguide containing metallic (Au) and a chalcogenic (As2S3) thin layers by using Kretschmann-Raether configuration. The resonant coupling of the radiation to the waveguide is calculated to be possible when the incidence angle to the coupling prism base is 58 degrees. This is demonstrated to be in good agreement to our experiments. Subsequent calculations indicate that, for an incidence angle slightly above the resonance coupling angle, the increase of the incident laser intensity leads to a rapid decay of the reflectivity of the waveguide structure. The decrease of the incident laser radiation leads to the increase of the reflectivity back to the original value on a slightly different path, resulting in a hysteresis like curve. This hysteresis curve is mostly due to the “memory” of the refractive index changes of the chalcogenic layer during the intensity increase phase.
Improving the Performance of Entities in the Mining Industry by Optimizing Green Business Processes and Emission Inventories

Due to environmental considerations, environmental sustainability has become the main target of contemporary organizations, which has a direct influence on increasing their performance. The purpose of this study was to present the efficiency of green business process optimization for the performances of mining entities. Quantitative research was carried out on a sample of 209 people in an economic entity in the mining industry. The results of the study indicated real possibilities to achieve the objectives set in the research undertaken. Using business process management, the authors examined how green business processes can be optimized in a Romanian mining entity. The main results determined the degree of pollution from suspended and sedimentary dust particles due to coal production from the mining entity that was studied. Moreover, the present research proved that certain key environmental indicators underlie the performance and optimization of green business processes. The practical implications of this study are to respect and continually improve the management of the processes of activities, to reduce the costs of depollution and increase the performances.
Nonlinear Motion Tracking by Deep Learning Architecture

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.
The Effect of Dividend Policy on Corporate Profitability : An Empirical Study on Beverage , Food and Tobacco Industry in Sri Lanka

The purpose of the study is to examine the relationship between dividend pay-out and corporate profitability. The study conducted as a panel sectional survey and exploited the sample frame of the Beverage, Food & Tobacco (BFT) industry in Sri Lanka. 10 companies were taken from the listed companies in Colombo Stock Exchange (CSE) under the Beverage, Food & Tobacco (BFT) industry in Sri Lanka. Secondary data for the period of 2012 to 2015 was used for this study based on financial statement. The objective of this study is to find out the impact of dividend payout on corporate profitability in Sri Lanka. The dividend policy measured using the dividend payout ratio, while return on equity (ROE) used as a measure of profitability. The study concluded that there is a positive significant relationship between dividend policy and the profitability in Beverage, Food & Tobacco (BFT) industry in Sri Lanka. The time horizon for this study is limited when compared with other studies.
Storage and Development of Digital Image Negatives in a Distributed File System

Steady innovations in traditional computing hardware have had a profound effect on other consumer electronic devices in use today; as processing becomes cheaper and storage capacities become larger, users are producing data at an unprecedented rate. To cope with these new storage demands, distributed and cloud-based systems are increasingly popular due to their scalable, parallel nature. In this paper, we investigate the use of cloud computing technology to provide storage and processing capabilities for extremely large image-based datasets and photographic editing workflows, and demonstrate the performance gains provided by such a system.
Enabling Data Integrity Protection in Regenerating-Coding-Based Cloud Storage

To protect outsourced data in cloud storage against corruptions, enabling integrity protection, fault tolerance, and efficient recovery for cloud storage becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving the intrinsic properties of fault tolerance and repair traffic saving. Our DIP scheme is designed under a Byzantine adversarial model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for the performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage test bed under different parameter choices. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.