Miracle-dz/UniFAR-FTUs huggingface dataset:
The Distribution of Gains from Access to Stocks

Recent market developments raise doubts regarding further spread of household stock market participation. We study, computationally and econometrically, net gains from access to stocks, and estimate the potentially changing role of their determinants across the distribution of such gains for US households. We highlight conflicting influences on net gains using a computational portfolio model, and use empirical estimates to derive differences in characteristics of potential entrants relative to marginal investors by the end of the dramatic recent expansion in the stockholder base. Findings suggest that downturns can have significant effects around the participation margin, through their influence on incomes, wealth, and employment. The role of education is found more limited than typically estimated, and confined to the low end of the gains distribution. Estimated characteristics of potential entrants relative to marginal stockholders suggest that further growth in participation poses considerable challenges, in view of more limited finances, younger age, more limited education and financial alertness, and above all significantly less self-declared willingness to assume financial risk by potential stockholders compared to marginal investors. The hurdle to financial practitioners interested in expanding the stockolder base is not estimated to be small.
Time-resolved electron microscopic analysis of the behavior of myosin heads on actin filaments after photolysis of caged ATP

The interaction between myosin subfragment 1 (S1) and actin filaments after the photolysis of P3-1-(2-nitrophenyl)ethyl ester of ATP (caged ATP) was analyzed with a newly developed freezing system using liquid helium. Actin and S1 (100 microM each) formed a ropelike double-helix characteristic of rigor in the presence of 5 mM caged ATP at room temperature. At 15 ms after photolysis, the ropelike double helix was partially disintegrated. The number of S1 attached to actin filaments gradually decreased up to 35 ms after photolysis, and no more changes were detected from 35 to 200 ms. After depletion of ATP, the ropelike double helix was reformed. Taking recent analyses of actomyosin kinetics into consideration, we concluded that most S1 observed on actin filaments at 35-200 ms are so called "weakly bound S1" (S1.ATP or S1.ADP.Pi) and that the weakly bound S1 under a rapid association- dissociation equilibrium with actin filaments can be captured by electron microscopy by means of our newly developed freezing system. This enabled us to directly compare the conformation of weakly and strongly bound S1. Within the resolution of deep-etch replica technique, there were no significant conformational differences between weakly and strongly bound S1, and neither types of S1 showed any positive cooperativity in their binding to actin filaments. Close comparison revealed that the weakly and strongly bound S1 have different angles of attachment to actin filaments. As compared to strongly bound S1, weakly bound S1 showed a significantly broader distribution of attachment angles. These results are discussed with special reference to the molecular mechanism of acto-myosin interaction in the presence of ATP.
Association relationship transformation of XML schemas to Object-Relational Databases

XML (eXtensible Markup Language) is fast emerging as the dominant standard for describing data and interchanging data between various systems and databases on the Internet. It offers the XML Schema definition language as formalism for defining the syntax and structure of XML documents provide rich facilities for defining and constraining the content of XML documents. Nevertheless, to enable efficient business application development in large-scale eCommerce environments, XML needs to have databases to keep all the data. Hence, it will inevitable be necessary to use methods to describe the XML schema in the Object Relational Database (ORDB) formats. In this paper, we present the way to transform the XML encoded format, which can be treated as a logical model, to an ORDB format. The paper first discusses the modelling of XML and why we need the transformation. Then, a number of transformation steps from the XML schema to the ORDB, with the emphasis on the transformations of association relationships, are presented. Different perspectives regarding these conceptual relationships (one-to-one relationship, oneto-many relationship and many-to-many relationship) and their transformations are mainly discussed.
Crizotinib (PF‐02341066) reverses multidrug resistance in cancer cells by inhibiting the function of P‐glycoprotein

Besides targeting the well‐known oncogenic c‐Met, crizotinib is the first oral tyrosine kinase inhibitor inhibiting anaplastic lymphoma kinase (ALK) in clinical trials for the treatment of non‐small cell lung cancer. Here, we assessed the possible reversal of multidrug resistance (MDR) by crizotinib in vitro and in vivo.
Deep Attention Recurrent Q-Network

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and "hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.
Prosodic scoring of word hypotheses graphs

Prosodic boundary detection is important to disam biguate parsing especially in spontaneous speech where elliptic sentences occur frequently Word graphs are an e cient interface between word recognition and parser Prosodic classi cation of word chains has been published earlier The adjustments necessary for applying these classi cation techniques to word graphs are discussed in this paper When classifying a word hypothesis a set of context words has to be determined appropriately A method has been developed to use stochastic language models for prosodic classi cation This as well has been adopted for the use on word graphs We also improved the set of acoustic prosodic features with which the recog nition errors were reduced by about on the read speech we were working on previously now achieving error rate for boundary classes and for accent classes Moving to spontaneous speech the recognition er ror increases signi cantly e g for a class boundary task We show that even on word graphs the combina tion of language models which model a larger context with acoustic prosodic classi ers reduces the recognition error by up to
PERFORMANCE EVALUATION OF ZIGBEE TRANSCEIVER FOR WIRELESS BODY SENSOR SYSTEM

A cost effective impedance measurement system and a low cost transceiver device has been presented for wireless body sensor systems.The proposed device has an analog front end to measure bioimpedance and ZigBee device which provides reliable wireless communication.Bioelectric Impedance measurement enables to characterize the state of tissues.Tetrapolar method is an advance method for measuring impedance since it is a very easy and simple method for practical implementation.The principle of modified tetrapolar method and its wireless transimission through zigbee has been investigated here.Different modulation technique has been applied and it has been found that MSK based transceiver is an efficient one since it has low bit error rate and it produce constant envelope carrier signals which have no amplitude and phase varations,hence it will be a more power saving technique.
Portfolio Optimization under Local-Stochastic Volatility: Coefficient Taylor Series Approximations and Implied Sharpe Ratio

We study the finite horizon Merton portfolio optimization problem in a general local-stochastic volatility setting. Using model coefficient expansion techniques, we derive approximations for both the value function and the optimal investment strategy. We also analyze the “implied Sharpe ratio” and derive a series approximation for this quantity. The zeroth order approximation of the value function and optimal investment strategy correspond to those obtained by [Merton, Rev. Econ. Statist. , 51, pp. 247--257] when the risky asset follows a geometric Brownian motion. The first order correction of the value function can, for general utility functions, be expressed as a differential operator acting on the zeroth order term. For power utility functions, higher order terms can also be computed as a differential operator acting on the zeroth order term. While our approximations are derived formally, we give a rigorous accuracy bound for the higher order approximations in this case in pure stochastic volatility mo.
MIS Profit from Customer Data by Identifying Strategic Opportunities Profit from Customer Data by Identifying Strategic Opportunities and Adopting the "Born Digital" Approach and Adopting the "Born Digital" Approach

We present a framework that maps the four data-driven strategies—Minimize Costs, Reward Loyalty, Personalize Interactions, and Acquire Customers—that a firm can enact to extract value from its customer data. The four strategies are distinguished by the potential repurchase frequency and the customizability of a firm’s products or services. We describe each of the strategies and provide in-depth examples from companies in the hospitality industry. By positioning themselves within the four-strategy framework, firms in a wide range of industries will be able to envision how they can adopt the most appropriate strategy (or strategies) for exploiting customer data to improve profitability. We also discuss the importance of “born digital” data, whereby data is captured in digital form, not digitized through scanning or manually input. A proactive born digital approach enables firms to better exploit opportunities for extracting business value from customer data.
Abundance and distribution of planktonic Archaea and Bacteria in the waters west of the Antarctic Peninsula

Polyribonucleotide probes targeting planktonic archaeal (Group I and II) and bacterial rRNA revealed that Archaea comprised a significant fraction of total prokaryote cell abundance in the marine waters west of the Antarctic Peninsula. Determinations of Archaea and Bacteria cell abundances were made during two research cruises to the Palmer Long‐Term Ecological Research region during the austral winter and summer of 1999. During the austral summer, surface water abundances of Group I (GI) Archaea were generally low, averaging 4.7 x 103 cells ml−1 and accounting for 1% of the total picoplankton assemblage. The abundance of GI Archaea increased significantly with depth, averaging 2.1 X 104 cells ml−1 and comprising 9–39% of the total picoplankton abundance in the meso‐ (150–1,000 m) and bathypelagic (1,000–3,500 m) circumpolar deep water (CDW). Relative to summertime distributions, GI cells were more evenly distributed throughout the water column during the winter, averaging 10% of the picoplankton in the surface waters and 13% in the CDW. Surface water GI abundance increased 44% between the summer and winter, coincident with a fivefold decrease in GI abundance in the deeper waters. The abundance of Group II (GII) Archaea was persistently <2% of the total picoplankton throughout the water column in both summer and winter. Bacterial abundance was greatest in the upper water column (0–100 m) during the summer, averaging 3.9 x 105 cells ml−1 and comprised 89% of the total picoplankton assemblage. Generally, GI Archaea varied seasonally in the deeper waters, whereas bacterial abundance varied more in the upper waters. The observed variability in bacterial and archaeal abundance suggests that these two groups of marine picoplankton are dynamic components of Southern Ocean microbial food webs.
The Effect of Augmented Reality Training on Percutaneous Needle Placement in Spinal Facet Joint Injections

The purpose of this study was to determine if augmented reality image overlay and laser guidance systems can assist medical trainees in learning the correct placement of a needle for percutaneous facet joint injection. The Perk Station training suite was used to conduct and record the needle insertion procedures. A total of 40 volunteers were randomized into two groups of 20. 1) The Overlay group received a training session that consisted of four insertions with image and laser guidance, followed by two insertions with laser overlay only. 2) The Control group received a training session of six classical freehand insertions. Both groups then conducted two freehand insertions. The movement of the needle was tracked during the series of insertions. The final insertion procedure was assessed to determine if there was a benefit to the overlay method compared to the freehand insertions. The Overlay group had a better success rate (83.3% versus 68.4%, p = 0.002), and potential for less tissue damage as measured by the amount of needle movement inside the phantom (3077.6 mm2 versus 5607.9 mm2, p = 0.01). These results suggest that an augmented reality overlay guidance system can assist medical trainees in acquiring technical competence in a percutaneous needle insertion procedure.
Bayesian Learning of Generalized Board Positions for Improved Move Prediction in Computer Go

Computer Go presents a challenging problem for machine learning agents. With the number of possible board states estimated to be larger than the number of hydrogen atoms in the universe, learning effective policies or board evaluation functions is extremely difficult. In this paper we describe Cortigo, a system that efficiently and autonomously learns useful generalizations for large state-space classification problems such as Go. Cortigo uses a hierarchical generative model loosely related to the human visual cortex to recognize Go board positions well enough to suggest promising next moves. We begin by briefly describing and providing motivation for research in the computer Go domain. We describe Cortigo’s ability to learn predictive models based on large subsets of the Go board and demonstrate how using Cortigo’s learned models as additive knowledge in a state-of-the-art computer Go player (Fuego) significantly improves its playing strength.
Nested Parallelism on GPU: Exploring Parallelization Templates for Irregular Loops and Recursive Computations

The effective deployment of applications exhibiting irregular nested parallelism on GPUs is still an open problem. A naïve mapping of irregular code onto the GPU hardware often leads to resource underutilization and, thereby, limited performance. In this work, we focus on two computational patterns exhibiting nested parallelism: irregular nested loops and parallel recursive computations. In particular, we focus on recursive algorithms operating on trees and graphs. We propose different parallelization templates aimed to increase the GPU utilization of these codes. Specifically, we investigate mechanisms to effectively distribute irregular work to streaming multiprocessors and GPU cores. Some of our parallelization templates rely on dynamic parallelism, a feature recently introduced by Nvidia in their Kepler GPUs and announced as part of the Open CL 2.0 standard. We propose mechanisms to maximize the work performed by nested kernels and minimize the overhead due to their invocation. Our results show that the use of our parallelization templates on applications with irregular nested loops can lead to a 2-6x speedup over baseline GPU codes that do not include load balancing mechanisms. The use of nested parallelism-based parallelization templates on recursive tree traversal algorithms can lead to substantial speedups (up to 15-24x) over optimized CPU implementations. However, the benefits of nested parallelism are still unclear in the presence of recursive applications operating on graphs, especially when recursive code variants require expensive synchronization. In these cases, a flat parallelization of iterative versions of the considered algorithms may be preferable.
Functional Inactivation of EBV-Specific T-Lymphocytes in Nasopharyngeal Carcinoma: Implications for Tumor Immunotherapy

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) associated malignancy with high prevalence in Southern Chinese. In order to assess whether defects of EBV-specific immunity may contribute to the tumor, the phenotype and function of circulating T-cells and tumor infiltrating lymphocytes (TILs) were investigated in untreated NPC patients. Circulating naïve CD3+CD45RA+ and CD4+CD25− cells were decreased, while activated CD4+CD25+ T-cells and CD3−CD16+ NK-cells were increased in patients compared to healthy donors. The frequency of T-cells recognizing seven HLA-A2 restricted epitopes in LMP1 and LMP2 was lower in the patients and remained low after stimulation with autologous EBV-carrying cells. TILs expanded in low doses of IL-2 exhibited an increase of CD3+CD4+, CD3+CD45RO+ and CD4+CD25+ cells and 2 to 5 fold higher frequency of LMP1 and LMP2 tetramer positive cells compared to peripheral blood. EBV-specific cytotoxicity could be reactivated from the blood of most patients, whereas the TILs lacked cytotoxic activity and failed to produce IFNγ upon specific stimulation. Thus, EBV-specific rejection responses appear to be functionally inactivated at the tumor site in NPC.
Enhanced Cognitive Effects of Demethoxycurcumin, a Natural Derivative of Curcumin on Scopolamine-Induced Memory Impairment in Mice

In the present study, we examined the ameliorating effects of demethoxycurcumin (DMC) on memory impairment induced by scopolamine using passive avoidance and Morris water maze tests in mice. Moreover, to determine the neurobiological effects underlying the ameliorating effects of the DMC, choline acetyltransferase (ChAT) immunoreactivity was evaluated in mice exposed to scopolamine. Our results demonstrated that chronic oral administration (28 days) of DMC (10 mg/kg) improved scopolamine-induced learning impairment in the passive avoidance task and memory impairment in the Morris water maze. Moreover, Choline acetyltransferase (ChAT) activity in the DMC-treated group was significantly increased to 33.03% compared with the control group. Our present finding suggests that DMC ameliorates memory impairments induced by scopolamine treatment through reversing the reduction of hippocampal ChAT expression in mice.
eodynamical modeling and multiscale seismic expression of thermochemical eterogeneity and phase transitions in the lowermost mantle

The D′′ region at the base of the mantle is characterized by seismologically inferred 3D heterogeneity, including multiple interfaces, localized low velocity zones, and anisotropy. The occurrence of the postperovskite (PPV) phase transition with a steep Clapeyron slope of 11.5–13 MPa/K, close to the core–mantle boundary, is a prime candidate for explaining observed seismic layering in the D′′. To examine the effect of the PPV phase transition on seismic structure we have carried out finite-element simulations with high-resolution (up to 3 km) in a cylindrical geometry. The rheology of the mantle has both Newtonian diffusion and non-Newtonian components, with a much greater propensity to non-Newtonian for PPV. From the temperature output we computed the 2D variations in shear wavespeed using a seismic equation of state based on mineral physics data. We then use a wave-packet decomposition of the wavespeed
Approximations for end-to-end delay analysis in OBS networks with light load

We provide an analysis of end-to-end delay in OBS (optical burst switching) networks and a large deviations approximation. The analysis is based on an exponential approximation of the OBS router blocking time and on the assumption of Poisson arrivals in routers along the path from source to destination. On the other hand, a light load assumption is performed, namely, waiting time is mainly due to the residual life of the output wavelengths and not to buffering.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Short and Long Run Determinants of Private Investment in Argentina

This study provides an empirical analysis of the macroeconomic factors that can potentially affect investment decisions in Argentina in a short, medium and long run perspective. Both the theory and the empirical literature are reviewed in order to identify a private investment function for the last three decades (1970–2000). The results suggest that investment decisions seem to be determined, in the short run, by shocks in returns (exchange rate, trade liberalization) and in aggregate demand. Besides, there is evidence of a “crowding-out” effect of public investment. In the long run, the capital accumulation path seems to be closely dependent on both well-developed financial and credit markets and on perspectives of fiscal sustainability.
Visual cortex neurons in monkey and cat: Effect of contrast on the spatial and temporal phase transfer functions

Abstract The responses of simple cells (recorded from within the striate visual cortex) were measured as a function of the contrast and the frequency of sine-wave grating patterns in order to explore the effect of contrast on the spatial and temporal phase transfer functions and on the spatiotemporal receptive field. In general, as the contrast increased, the phase of the response advanced by approximately 45 ms (approximately one-quarter of a cycle for frequencies near 5 Hz), although the exact value varied from cell to cell. The dynamics of this phase-advance were similar to the dynamics of the amplitude: the amplitude and the phase increased in an accelerating fashion at lower contrasts and then saturated at higher contrasts. Further, the gain for both the amplitude and the phase appeared to be governed by the magnitude of the contrast rather than the magnitude of the response. For the spatial phase transfer function, variations in contrast had little or no systematic effect; all of the phase responses clustered around a single straight line, with a common slope and intercept. This implies that the phase-advance was not due to a change in the spatial properties of the neuron; it also implies that the phase-advance was not systematically related to the magnitude of the response amplitude. On the other hand, for the temporal phase transfer function, the phase responses fell on five straight lines, related to the five steps in contrast. As the contrast increased, the phase responses advanced such that both the slope and the intercept were affected. This implies that the phase-advance was a result of contrast-induced changes in both the response latency and the shape/symmetry of the temporal receptive field.