Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Telmisartan or Losartan for Treatment of Nonalcoholic Fatty Liver Disease: Fatty Liver Protection Trial by Telmisartan or Losartan Study (FANTASY)

Aim. This study compared the effects of telmisartan and losartan on nonalcoholic fatty liver disease (NAFLD) and biochemical markers of insulin resistance in hypertensive NAFLD patients with type 2 diabetes mellitus. Methods. This was a randomized, open-label, parallel-group comparison of therapy with telmisartan or losartan. Nineteen hypertensive NAFLD patients with type 2 diabetes were randomly assigned to receive telmisartan at a dose of 20 mg once a day (n = 12) or losartan at a dose of 50 mg once a day (n = 7) for 12 months. Body fat area as determined by CT scanning and hepatic fat content based on the liver-to-spleen (L/S) ratio, as well as several parameters of glycemic and lipid metabolism, were compared before and after 12 months. Results. The telmisartan group showed a significant decline in serum free fatty acid (FFA) level (from 0.87 ± 0.26 to 0.59 ± 0.22 mEq/L (mean ± SD), P = 0.005) and a significant increase in L/S ratio (P = 0.049) evaluated by CT scan, while these parameters were not changed in the losartan group. Conclusion. Although there was no significant difference in improvement in liver enzymes with telmisartan and losartan treatment in hypertensive NAFLD patients with type 2 diabetes after 12 months, it is suggested that telmisartan may exert beneficial effects by improving fatty liver.
Optimizing Speech Intelligibility in a Noisy Environment: A unified view

Modern communication technology facilitates communication from anywhere to anywhere. As a result, low speech intelligibility has become a common problem, which is exacerbated by the lack of feedback to the talker about the rendering environment. In recent years, a range of algorithms has been developed to enhance the intelligibility of speech rendered in a noisy environment. We describe methods for intelligibility enhancement from a unified vantage point. Before one defines a measure of intelligibility, the level of abstraction of the representation must be selected. For example, intelligibility can be measured on the message, the sequence of words spoken, the sequence of sounds, or a sequence of states of the auditory system. Natural measures of intelligibility defined at the message level are mutual information and the hit-or-miss criterion. The direct evaluation of high-level measures requires quantitative knowledge of human cognitive processing. Lower-level measures can be derived from higher-level measures by making restrictive assumptions. We discuss the implementation and performance of some specific enhancement systems in detail, including speech intelligibility index (SII)-based systems and systems aimed at enhancing the sound-field where it is perceived by the listener. We conclude with a discussion of the current state of the field and open problems.
Agent-based evaluation for effects of signaling in markets with asymmetric information

In this paper, we evaluate effects of signaling in a market in which there is asymmetric information between sellers and buyers. In such a market, only sellers know more about their products than buyers. Therefore buyers do not want to pay for the product more than the price for the average quality they expect in the market. Those buyers action make sellers hesitate to submit products with better quality in the market. Then Shortage of better products reduces the average quality in the market. In turn, buyers expect to pay for the product at lower price. Such iterations make the market with lower quality products. In order to cope with the problem, sellers are encouraged to show appropriate signals about the products. In our simulation, we show how signals have effects on the quality of products in a market by an agent-based approach. A preceding study shows a signal whose cost depends on the quality of products is effective. However, through our simulations and analysis, we find that the balance between the cost of quality-related signals and the product of the amount of products and their producing cost should be carefully considered in the market. Our simulation shows that the market with high quality products can be attained when the cost of quality-related signals is specified appropriately.
Design and simulation a MEMS microgripper with integrated electrothermal actuator and force sensor

The conceptual design of a novel compliant microelectromechanical systems (MEMS)-based gripper equipped with elecrothermal actuating as well as electrothermal force sensing is introduced in this paper. By this design combination, the device possesses some unique characteristics including a small and compact footprint size, and a large and steady force from the thermal actuator. Simulation results show that the thermal actuator produces the gripping force to execute the gripping operation with a range of approximately 85 μm under a low source voltage of 2 V. At the same time, owning to a compliant rotation bearing, a large displacement of gripping range is obtained. The established theoretical models have a coincident matching with the FEA simulation results, which verifies the feasible design of the gripper in this work.
Cannabinoid Transmission in the Prefrontal Cortex Bi-Phasically Controls Emotional Memory Formation via Functional Interactions with the Ventral Tegmental Area

Disturbances in cortical cannabinoid CB1 receptor signaling are well established correlates of various neuropsychiatric disorders, including depression and schizophrenia. Importantly, the ability of cannabinoid transmission to modulate emotional processing is functionally linked to interactions with subcortical DA systems. While considerable evidence demonstrates that CB1 receptor-mediated modulation of emotional processing and related behaviors follows a biphasic functional curve, little is known regarding how CB1 signaling within cortical networks may interact with subcortical DAergic systems involved in emotional behavior regulation. Using a combination of in vivo electrophysiological recordings and behavioral pharmacology in rats, we investigated the relationship between mPFC cannabinoid transmission, fear memory formation, and subcortical DA neuron activity patterns. We report that direct intra-mPFC CB1 activation biphasically modulates spontaneous, subcortical VTA DA neuron activity in a dose-dependent fashion; while lower doses of a CB1 receptor agonist, WIN 55,212–2, significantly increased spontaneous firing and bursting rates of VTA DA neurons, higher doses strongly inhibited spontaneous DA neuron activity. Remarkably, this same dose-related functional difference was observed with the regulation of fear-related emotional memory formation. Thus, lower levels of CB1 activation potentiated the emotional salience of normally subthreshold fear memory, whereas higher levels completely blocked fear memory acquisition. Furthermore, while the potentiation of subthreshold fear memory salience was blocked by DA receptor antagonism, CB1-mediated blunting of suprathreshold fear memory was rescued by intra-VTA administration of a GABAB receptor antagonist, demonstrating that reversal of GABAergic inhibitory mechanisms in the VTA can reverse the inhibitory influence of intra-PFC CB1 transmission on mesolimbic DA activity.
Color conceptualization

In this paper, we propose a method to manipulate colors of an image. Based on a library of natural color images, our system evolves several prototypes of color distribution of the library, which we call "color concepts". By applying these color concepts on an input image, a user can easily change the mood of image colors in a global manner. Our results of photographs and paintings indicate that this method is capable of high-quality color manipulations.
Sigmund Exner’s (1887) Einige Beobachtungen über Bewegungsnachbilder (Some Observations on Movement Aftereffects): An Illustrated Translation With Commentary

In his original contribution, Exner’s principal concern was a comparison between the properties of different aftereffects, and particularly to determine whether aftereffects of motion were similar to those of color and whether they could be encompassed within a unified physiological framework. Despite the fact that he was unable to answer his main question, there are some excellent—so far unknown—contributions in Exner’s paper. For example, he describes observations that can be related to binocular interaction, not only in motion aftereffects but also in rivalry. To the best of our knowledge, Exner provides the first description of binocular rivalry induced by differently moving patterns in each eye, for motion as well as for their aftereffects. Moreover, apart from several known, but beautifully addressed, phenomena he makes a clear distinction between motion in depth based on stimulus properties and motion in depth based on the interpretation of motion. That is, the experience of movement, as distinct from the perception of movement. The experience, unlike the perception, did not result in a motion aftereffect in depth.
Distributed Control and Synchronization of Diffusively Coupled Systems

Diffusively coupled networks, in which different components of a network adjust their behavior according to the local sum of differences between their own and neighbors' states, are a ubiquitous class of spatially-distributed models appearing in engineered and biological systems, and produce surprisingly rich dynamics. In this dissertation, we develop analysis methods and distributed algorithms that exploit the local structure of the network as well as individual component dynamics in order to guarantee the desirable operation of the aggregate network system in the absence of centralized coordination.We begin by formulating network design problems to guarantee coordination of multi-agent systems by imposing constraints on the underlying structure of the diffusive coupling graph linking agents. The resulting constraint-derived linear matrix inequalities may then be iteratively solved using convex semidefinite programming, resulting in significant performance gains in several multi-agent systems problems. Our approach furthermore identifies critical nodes and edges in a network, and aids in developing strategies to enhance connectivity and robustness.Next considering the case where desired steady-state trajectories may be time-varying, we derive conditions to determine whether limit cycle oscillations synchronize in diffusively coupled systems. Conversely, we highlight cases of diffusion-driven instability, a phenomenon widely hypothesized as a mechanism behind pattern formation in biological systems, in which sufficiently large diffusive coupling may destabilize a spatially homogeneous periodic orbit. The analytical and numerical conditions we derive lend insight to designing distributed laws where the local behavior about a specific attractor is of interest.We then turn to the problem of developing distributed laws that guarantee spatially uniform behavior globally in diffusively coupled systems. We first study systems with spatially-dependent diffusive coupling, and subsequently examine the case where the network itself has dynamics and adapts according to the agents' states. We develop adaptive laws to guarantee synchronization in systems with spatially-dependent coupling, and apply these results to a number of problems of interest arising from multi-agent systems and cooperative control. We finally address the case where the dynamics of the agents may be subject to spatially-varying input disturbances, and derive distributed laws to guarantee synchronization in the presence of such heterogeneities.
Utility maximization scheme for end-to-end QoS routing in communication networks with heterogeneous service classes

We present an integer programming approach to offer optimal bandwidth allocation and QoS routing for network management problems with heterogeneous service classes. The bandwidth allocation policy in such communication networks is determined in accordance with the proportionally fair rule, which can be expressed by utility functions of network users. The objective of this utility maximization scheme is to prepare a routing table for future user request. This routing table identifies the allocated bandwidth and the suitable end-to-end path under QoS guarantees for each user. We analyze the mathematical properties of the optimal bandwidth allocation and provide computational results to demonstrate the validity and efficiency of the proposed scheme.
THE FUTURE OF THE FRENCH WINE INDUSTRY: GLOBALIZATION VS. QUALITY EFFECTS

This paper surveys the current features and the current trends of the French wine industry, which, while still leading in terms of both quantity and quality, is facing increasing and global competition in international wine markets. In terms of long-term trends, the French wine supply is characterised by a strong shift towards increased quality. Ro ughly speaking, it can be concluded that the markets of the best quality wines, such as the best wines with Appellation d'Origine Controlee (AOC) in Bordeaux, Burgundy and other areas, are expanding. On the contrary, in the lower quality brackets (Vins ordinaires or ordinary table wines), the market is receding. Moreover, wines of intermediate but still quite good quality such as Vins de Pays (country wines) and Vins de Cepage (variety-denominated wines) are also expanding, thus adjusting to international competition, and to the trends of demand on international markets. An econometric application, focused on the demand for wines with Appellation d'Origine Controlee (AOC) and ordinary wines, is carried out, yielding statistically significant results. Our application illustrates the reality of the shift towards higher quality of French wines, as reflected in consumption patterns.
CYTOCHROME P450-DEPENDENT METABOLISM OF L-DEPRENYL IN THE BRAIN OF DIFFERENT EXPERIMENTAL MAMMALS

Although the CYP content of brain is too low to significantly influence the overall pharmaco-kinetics of drugs and hormones in the body, brain CYP could play a role in tissue-and/or cell-specific sensitivity to certain drugs or xenobiotics. We have characterized the CYP-dependent metabolism of l-deprenyl by brain microsomal preparations obtained from different experimental animals: monkey, C57BL/6 mouse and rabbit. In monkey the kinetic analysis revealed a similar methamphetamine and nordeprenyl formation from l-deprenyl in striatum and cortex. Both metabolic pathways appeared to be more efficient in the cortex than in the striatum, since the V max for microsomal preparations was lower in the striatum for the formation of both metabolites. Inhibition analysis suggested that l-methamphetamine formation is catalysed by CYP2A and CYP3A, whereas only CYP3A appears to be involved in nordeprenyl formation. The kinetic study performed with microsomal preparations from whole brain of C57BL/6 mice highlighted that the only detectable l-deprenyl metabolite was methamphetamine with K m and V max values similar to those determined in monkey cortex. However, the inhibition studies suggested the involvement of CYP2E1. In rabbit the apparent K m value of both metabolic pathways obtained using whole brain microsomal preparations resulted about five fold higher to the values observed in previous mammalian preparations. Inhibition studies suggested the involvement of CYP2A family in deprenyl metabolism. In conclusion, the present study indicates that l-deprenyl is effectively metabolised by CYP-dependent oxidases in the brain, giving rise mainly to the formation of methamphetamine and that there are differences between species in CYP-dependent metabolism of l-deprenyl.
High-Efficiency 312-kVA Three-Phase Inverter Using Parallel Connection of Silicon Carbide MOSFET Power Modules

This paper presents the design process of a 312-kVA three-phase silicon carbide inverter using ten parallel-connected metal-oxide-semiconductor field-effect-transistor power modules in each phase leg. The design processes of the gate-drive circuits with short-circuit protection and power circuit layout are also presented. Measurements in order to evaluate the performance of the gate-drive circuits have been performed using a double-pulse setup. Moreover, electrical and thermal measurements in order to evaluate the transient performance and steady-state operation of the parallel-connected power modules are shown. Experimental results showing proper steady-state operation of the power converter are also presented. Taking into account measured data, an efficiency of approximately 99.3% at the rated power has been measured for the inverter.
Refined Data Partitioning for improved video prioritization

Data partitioning is a coarse form of compressed data prioritization or layering that is effective in error-prone environments. This paper proposes a further sub-partition of one of the H.264/AVC codec's data-partitions, which can result in up to a 5 dB improvement in objective video quality. Specifically, the partition bearing spatially-coded data is divided into those data naturally inserted by the encoder and those data inserted for intra-refresh purposes. The refinement is helpful when communicating over wireless channels or when selectively dropping packets during congestion. Suitable applications are in two-way user-to-user communication for which HTTP adaptive streaming is inappropriate and the HEVC codec consumes too much energy.
Ad-hoc object retrieval in the web of data

Semantic Search refers to a loose set of concepts, challenges and techniques having to do with harnessing the information of the growing Web of Data (WoD) for Web search. Here we propose a formal model of one specific semantic search task: ad-hoc object retrieval. We show that this task provides a solid framework to study some of the semantic search problems currently tackled by commercial Web search engines. We connect this task to the traditional ad-hoc document retrieval and discuss appropriate evaluation metrics. Finally, we carry out a realistic evaluation of this task in the context of a Web search application.
Decentralized Reputation System for Transaction Networks

Peer-to-peer transaction networks such as Bitcoin have gained traction in the past few years as they are touted as an anonymous, safe, and secure way to make electronic payments with low transaction fees. Our goal is to improve these technologies by developing a reputation system that calculates reputation based on underlying network structure and allows users to look up and record histories of transaction outcomes. We believe that this will facilitate a more honest market environment. Beyond standard market transactions, developers will also be able to quickly build reputation components into their applications by utilizing our work. Our main contributions comes in two parts: a reputation algorithm called net flow convergence and a decentralized system that records transaction outcomes.
Non-Essential Communication in Mobile Applications

This paper studies communication patterns in mobile applications. Our analysis shows that 65% of the HTTP, socket, and RPC communication in top-popular Android applications from Google Play have no effect on the user-observable application functionality. We present a static analysis that is able to detect non-essential communication with 84% -90% precision and 63%-64% recall, depending on whether advertisement content is interpreted as essential or not. We use our technique to analyze the 500 top-popular Android applications from Google Play and determine that more than 80% of the connection statements in these applications are non-essential.
Quantifying Application Behavior Space for Detection and Self-Healing

The increasing sophistication of software attacks has created the need for increasingly finer-grained intrusion and anomaly detection systems, both at the network and the host level. We believe that the next generation of defense mechanisms will require a much more detailed dynamic analysis of application behavior than is currently done. We also note that the same type of behavior analysis is needed by the current embryonic attempts at self-healing systems. Because such mechanisms are currently perceived as too expensive in terms of their performance impact, questions relating to the feasibility and value of such analysis remain unexplored and unanswered. We present a new mechanism for profiling the behavior space of an application by analyzing all function calls made by the process, including regular functions and library calls, as well as system calls. We derive behavior from aspects of both control and data flow. We show how to build and check profiles that contain this information at the binary level – that is, without making changes to the application’s source, the operating system, or the compiler. This capability makes our system, Lugrind, applicable to a variety of software, including COTS applications. Profiles built for the applications we tested can predict behavior with 97% accuracy given a context window of 15 functions. Lugrind demonstrates the feasibility of combining binary-level behavior profiling with detection and automated repair.
An automatic approach for discovering skill relationship from learning data

We have developed a method called skill2vec, which applies big data techniques to automatically analyze the learning data to discover skill relationship, leading to a more objective and data-informed decision making. Skill2vec is a neural network architecture which can transform a skill to a new vector space called embedding. The embedding can facilitate the comparison and visualization of different skills and their relationship. We conducted a pilot experiment using benchmark dataset to demonstrate the effectiveness of our method.
Delay-Aware Routing in Low Duty-Cycle Wireless Sensor Networks

In low duty-cycle sensor networks, sensors sleep most of their time; it is consequently hard for sensors to maintain always-awake communication links. One major challenge is therefore to design communication algorithms to reduce the delivery delay. In this paper we propose and evaluate a delay-aware routing algorithm for low duty-cycle sensor networks. The proposed algorithm first organizes the network into a layered structure. And then the sensor with packets ready to be sent dynamically determines a forwarder from multiple potential next-hop sensors in terms of timeliness. Simulation results show that the proposed algorithm outperforms the static shortest-path routing in delivery delay and drop rate.
Optimal Economic Transparency

In this paper, I explore the optimal extent to which the central bank should disseminate information among private agents. Individual firms are assumed to have diverse private information, and the central bank provides public information either implicitly, by setting its policy instrument, or explicitly, by making announcements about its short-run targets. The optimal degree of economic transparency is affected differently by cost and demand shocks. More-accurate central bank forecasts of demand shocks reduce optimal transparency, while more-accurate forecasts of cost shocks increase optimal transparency. Increased persistence in demand (cost) disturbances increases (reduces) optimal transparency.
Predicting Ordinal Classes in

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes in an ILP setting. We start with a relational regression algorithm named SRT (Structural Regression Trees) and study various ways of transforming it into a rst-order learner for ordinal classiication tasks. Combinations of these algorithm variants with several data preprocessing methods are compared on two ILP benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-oo between optimal categorical classiication accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classiication and regression in relational learning.