Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-optimal caches with guaranteed lifetime

This work addresses the aging of the memory sub-system due to NBTI (Negative Bias Temperature Instability) in systems that have to provide a guaranteed level of service, and specifically, a guaranteed lifetime. Our approach leverages a novel cache architecture in which a smart joint use of redundancy and power management allows us to obtain caches that meet a desired lifetime target with minimal energy consumption. This is made possible by exploiting the possibility of putting the cache sub-block used for redundancy into a deep low-power state, thus allowing more energy saving than a regular architecture. Sacrificing a portion of the cache for aging mitigation only marginally affects performance thanks to the non-linear dependency of miss rate versus cache size, which allows to find the best cache size that maximizes the objective. Simulation results show that it is possible to meet the target lifetime by achieving energy reductions (measured over the lifetime of the system) ranging from 3X to 10X (2X to 8X) for a lifetime target of 15 (25) years, with marginal miss rate overhead.
Homoploid hybrid speciation in action

Abbott, R. J., Hegarty, M. J., Hiscock, S. J., Brennan, A. C. (2010). Homoploid hybrid speciation in action. Taxon, International Association for Plant Taxonomy, 59 (5), 1375-1386. IMPF: 02.36
Sequential Behavior and Learning in Evolved Dynamical Neural Networks

This article explores the use of a real-valued modular genetic algorithm to evolve continuous-time recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals of time. We also evolve networks that can learn to generate one of a set of possible sequences based on reinforcement from the environment. Finally, we utilize concepts from dynamical systems theory to understand the operation of some of these evolved networks. A novel feature of our approach is that we assume neither an a priori discretization of states or time nor an a priori learning algorithm that explicitly modifies network parameters during learning. Rather, we merely expose dynamical neural networks to tasks that require sequential behavior and learning and allow the genetic algorithm to evolve network dynamics capable of accomplishing these tasks.
A Mesh Data Structure for Rendering and Subdivision

Generating subdivision surfaces from polygonal meshes requires the complete topological information of the original mesh, in order to find the neighbouring faces, and vertices used in the subdivision computations. Normally, winged-edge type data-structures are used to maintain such information about a mesh. For rendering meshes, most of the topological information is irrelevant, and winged-edge type data-structures are inefficient due to their extensive use of dynamical data structures. A standard approach is the extraction of a rendering mesh from the winged-edge type data structure, thereby increasing the memory footprint significantly. We introduce a mesh data-structure that is efficient for both tasks: creating subdivision surfaces as well as fast rendering. The new data structure maintains full topological information in an efficient and easily accessible manner, with all information necessary for rendering optimally suited for current graphics hardware. This is possible by disallowing modifications of the mesh, once the topological information has been created. In order to avoid any inconveniences due to this limitation, we provide an API that makes it possible to stitch multiple meshes and access the topology of the resulting combined mesh as if it were a single mesh. This API makes the new mesh data structure also ideally suited for generating complex geometry using mesh-based L-systems.
The Business Value of CRM Systems: A Resource-Based Perspective

Drawing upon the resource-based view and the process-oriented view, we develop a conceptual model to examine the business value of customer relationship management (CRM) systems, using a dataset of 150 U.S. banking firms. The results show that: (1) CRM resources improve firm performance mainly through creating strategic benefits (e.g. increased customer retention, improved product and service), although there are significant operational benefits (e.g. efficiency gains and cost reduction in customer- oriented business processes); (2) in different competitive environments, the same resources play different roles in creating strategic benefits: technological resources have a weaker impact on strategic benefits in more competitive environments, while organizational alignment has a stronger impact in such environments; (3) operational benefits tend to be "competed away", while strategic benefits sustain to generate performance advantage in spite of competition. These findings provide important implications for understanding how CRM value is shaped by various resources in competitive environments.
Corruption Distance and Foreign Direct Investment

Abstract We study the effects of “corruption distance,” defined as the difference in corruption levels between country pairs on bilateral foreign direct investment (FDI). Using a “gravity” model and the Heckman (1979) two-stage framework on a data set of forty-five countries from 1997 to 2007, we find that corruption distance adversely influences both the likelihood of FDI and the volume of FDI. A novel finding in this study is that we identify the asymmetric effect of corruption distance and find that the positive corruption distance, defined as the corruption distance from a high corruption source to a low corruption host country, is the prominent one that affects the behavior of bilateral FDI.
Stratus: Load Balancing the Cloud for Carbon Emissions Control

Large public cloud infrastructure can utilise power which is generated by a multiplicity of power plants. The cost of electricity will vary among the power plants and each will emit different amounts of carbon for a given amount of energy generated. This infrastructure services traffic that can come from anywhere on the planet. It is desirable, for latency purposes, to route the traffic to the data centre that is closest in terms of geographical distance, costs the least to power and emits the smallest amount of carbon for a given request. It is not always possible to achieve all of these goals so we model both the networking and computational components of the infrastructure as a graph and propose the Stratus system which utilises Voronoi partitions to determine which data centre requests should be routed to based on the relative priorities of the cloud operator.
Stream fusion: from lists to streams to nothing at all

This paper presents an automatic deforestation system, stream fusion, based on equational transformations, that fuses a wider range of functions than existing short-cut fusion systems. In particular, stream fusion is able to fuse zips, left folds and functions over nested lists, including list comprehensions. A distinguishing feature of the framework is its simplicity: by transforming list functions to expose their structure, intermediate values are eliminated by general purpose compiler optimisations. We have reimplemented the Haskell standard List library on top of our framework, providing stream fusion for Haskell lists. By allowing a wider range of functions to fuse, we see an increase in the number of occurrences of fusion in typical Haskell programs. We present benchmarks documenting time and space improvements.
A randomized, double-blind, placebo-controlled, 8-week study of vilazodone, a serotonergic agent for the treatment of major depressive disorder.

OBJECTIVE To evaluate the efficacy, and further establish the safety profile, of oral once-daily vilazodone, a potent and selective serotonin 1A receptor partial agonist and reuptake inhibitor, in the treatment of major depressive disorder (MDD). METHOD This phase 3, randomized, double-blind, placebo-controlled, 8-week study (conducted March 2008-February 2009) enrolled 481 adults with DSM-IV-TR-defined MDD. Patients received vilazodone (titrated to 40 mg/d) or placebo. The primary efficacy endpoint was change in Montgomery-Asberg Depression Rating Scale (MADRS) total score from baseline to end of treatment. Secondary efficacy measures included MADRS and 17-item Hamilton Depression Rating Scale (HDRS-17) response and change in HDRS-17, HDRS-21, Hamilton Anxiety Rating Scale (HARS), Clinical Global Impressions-Severity of Illness (CGI-S), and Clinical Global Impressions-Improvement (CGI-I) scores. The Changes in Sexual Functioning Questionnaire (CSFQ) was administered at baseline and week 8. RESULTS Vilazodone-treated patients had significantly greater improvement (P = .009) according to the MADRS than placebo patients (intent-to-treat; least-squares mean changes: -13.3, -10.8). MADRS response rates were significantly higher with vilazodone than placebo (44% vs 30%, P = .002). Remission rates for vilazodone were not significantly different based on the MADRS (vilazodone, 27.3% vs placebo, 20.3%; P = .066) or HDRS-17 (vilazodone, 24.2% vs placebo, 17.7%; P = .088). Vilazodone-treated patients had significantly greater improvements from baseline in HDRS-17 (P = .026), HDRS-21 (P = .029), HARS (P = .037), CGI-S (P = .004), and CGI-I (P = .004) scores than placebo patients. Rates of discontinuation due to adverse events were 5.1% (vilazodone) and 1.7% (placebo). The most common adverse events (vilazodone vs placebo) were diarrhea (31% vs 11%), nausea (26% vs 6%), and headache (13% vs 10%). Treatment-related effects on sexual function as measured by the CSFQ were small and similar to placebo. Effects on weight were no different from placebo. CONCLUSIONS Vilazodone 40 mg/d was well tolerated and effective in adult patients with MDD. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00683592.
Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data

Recently many scientific and engineering applications have involved the challenging task of analyzing large amounts of unsorted high-dimensional data that have very complicated structures. From both geometric and statistical points of view, such unsorted data are considered mixed as different parts of the data have significantly different structures which cannot be described by a single model. In this paper we propose to use subspace arrangements—a union of multiple subspaces—for modeling mixed data: each subspace in the arrangement is used to model just a homogeneous subset of the data. Thus, multiple subspaces together can capture the heterogeneous structures within the data set. In this paper, we give a comprehensive introduction to a new approach for the estimation of subspace arrangements. This is known as generalized principal component analysis (GPCA). In particular, we provide a comprehensive summary of important algebraic properties and statistical facts that are crucial for making the inference of subspace arrangements both efficient and robust, even when the given data are corrupted by noise or contaminated with outliers. This new method in many ways improves and generalizes extant methods for modeling or clustering mixed data. There have been successful applications of this new method to many real-world problems in computer vision, image processing, and system identification. In this paper, we will examine several of those representative applications. This paper is intended to be expository in nature. However, in order that this may serve as a more complete reference for both theoreticians and practitioners, we take the liberty of filling in several gaps between the theory and the practice in the existing literature.
Imperfect voxelized shadow volumes

Voxelized shadow volumes [Wyman 2011] provide a discretized view-dependent representation of shadow volumes, but are limited to point or directional lights. We extend them to allow dynamic volumetric visibility from area light sources using imperfect shadow volumes. We show a coarser visibility sampling suffices for area lights. Combining this coarser resolution with a parallel shadow volume construction enables interactive rendering of dynamic volumetric shadows from area lights in homogeneous single-scattering media, at under 4x the cost of hard volumetric shadows.
Frequent pattern clustering for ADLs recognition in smart environments

Smart habitats are considered by many researchers as a promising potential solution to help supporting the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders to a resident in order to increase their autonomy. Smart homes can be seen as a huge data warehouse on the person's lifestyle. However, one of the major issues which emerge from this context of big data is learning. So it is essential to develop techniques to learn from patients before being able to assist them. In fact, each person makes a number of recurring activities, but not necessarily the same, not in the same way, not at the same time, etc. It is difficult for an expert to establish a knowledge library of activities as is often the case in the literature. A promising solution that is beginning to be explored seriously by many scientists concerning the application of data mining techniques to learn behaviors, habits and routines of people. About it, we present in this paper an affordable activity recognition system, based on frequent sensor clustering, able to recognize the patterns of the daily routine activities.
A Sensitive Image Filtering Method Based on Gabor Function and SVM

The filtering of sensitive images becomes increasingly important, and the image filtering technique based on content has become the focus of research. For the disadvantages of low filtering accuracy rate and poor flexibility that exist in many image filtering methods, a sensitive image filtering method based on Gabor function and SVM(Support Vector Machine) was proposed. And then such problems as how to improve the efficiency and accuracy of filtering were discussed. Finally this method was compared with the existing filtering methods, and the experimental results showed that this method has significant improvement in filtering effectiveness, which can effectively improve the rate of precision and flexibility.
The Effects of Anthropogenic Stress on Nitrogen-Cycling Microbial Communities in Temperate and Tropical Soils

THE EFFECTS OF ANTHROPOGENIC STRESS ON NITROGEN-CYCLING MICROBIAL COMMUNITIES IN TEMPERATE AND TROPICAL SOILS MAY 2017 GEORGE S. HAMAOUI JR., B.A., OHIO WESLEYAN UNIVERSITY Ph.D, UNIVERSITY OF MASSACHUSETTS-AMHERST Directed by: Professor Klaus Nüsslein In this dissertation several research studies are discussed that characterize the effects of anthropogenic, or human-induced, stress on both ammonia-oxidizing and total bacterial soil microbial communities. The disturbances of land-use change in tropical, South American rainforests and artificial warming and nitrogen (N) fertilization in temperate, North American forests were investigated as these disturbances represent past and current disturbances caused by human landscape alteration and climate change. Initially, the response of soil ammonia-oxidizing microbial communities to land-use change from primary rainforest to pasture and, finally, back to secondary forest was determined. Next, these analyses of land-use change effects were expanded to the total bacterial community in these rainforest soils sampled annually for three years. Lastly, the effects of increasing soil temperature and N-deposition on ammonia-oxidizing microbial communities in temperate forests were characterized. Land-use change affected ammonia-oxidizing communities in tropical soils. Both the abundance of ammonia-oxidizer marker genes and their community structure shifted
A voice-controlled web browser to navigate hierarchical hidden menus of web pages in a smart-tv environment

This paper proposes a new voice web browser that can be operated in smart TV environments. Previous voice web browsers had the limitation of being run under limited conditions; for example, a list of the specific contents of a page was outputted by voice, or the user entered a search term by voice. In our method proposed in this paper, all the hierarchical menu areas on a web page are recognized and controlled with voice keywords so that page navigation according to a menu can be conveniently done in a voice supported web browser. Although many studies have been conducted on web page menu recognition, most of them provide insufficient information to recognize the hierarchical menu structure. In other words, most web pages in recent browsers showed submenus only as a result of a specific user interaction, since these previous studies had no way of recognizing or controlling the submenus. Therefore, in the web browser proposed in this study, a hierarchical menu structure, which is inserted dynamically via user interaction, is recognized and selected by voice, thus making it possible to maneuver on the web page. Furthermore, the core code of the browser is implemented in JavaScript, so it can be flexibly used not only for a web browser on Smart TVs, but also as functional extensions of existing web browsers in a PC environment.
3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection

3D Statistical Shape Models (3D-SSM) are widely used for medical image segmentation. However, during segmentation, they typically perform a very limited unidirectional search for suitable landmark positions in the image, relying on weak learners or use-case specific appearance models that solely take local image information into account. As a consequence, segmentation errors arise, and results in general depend on the accuracy of a previous model initialization. Furthermore, these methods become subject to a tedious and use-case dependent parameter tuning in order to obtain optimized results. To overcome these limitations, we propose an extension of 3D-SSM by landmark-wise random regression forests that perform an enhanced omni-directional search for landmark positions, thereby taking rich non-local image information into account. In addition, we provide a long distance model fitting based on a multi-scale approach, that allows an accurate and reproducible segmentation even from distant image positions, thus enabling an application without model initialization. Finally, translation of the proposed method to different organs is straightforward and requires no adaptation of the training process. In segmentation experiments on 45 clinical CT volumes, the proposed omni-directional search significantly increased accuracy and displayed great precision regardless of model initialization. Furthermore, for liver, spleen and kidney segmentation in a competitive multi-organ labeling challenge on publicly available data, the proposed method achieved similar or better results than the state of the art. Finally, liver segmentation results were obtained that successfully compete with specialized state-of-the-art methods from the well-known liver segmentation challenge SLIVER.
Receptive fields and suppressive fields in the early visual system

Initial models proposed that these operations are weighted sums, with weights given by a neuron’s receptive field. These models explain the basic features of response selectivity. They were later extended to explain a number of suppressive effects originating within and outside the region of the receptive field. The resulting models rely on division. In this division, the receptive field feeds into the numerator, and the denominator is provided by a larger, non-classical suppressive field. While the receptive field confers to a neuron the basic selectivity for stimulus properties, the suppressive field modulates responsiveness. A divisive suppressive field confers to neurons in early visual system a number of computational advantages. Recent evidence in higher cortical areas suggests that the modulation of divisive suppression is the primary means of operation of visual attention. In this chapter I summarize research in receptive fields and suppressive fields in lateral geniculate nucleus (LGN) and in primary visual cortex (V1). In the following, I refer to a “suppressive field” as though this term had wide acceptance. In reality, the concept has been proposed only for LGN neurons (Levick et al., 1972), and lies forgotten since 30 years. My hope is that it will find wide acceptance to describe responses of both LGN and V1 neurons. Receptive fields in LGN
A quantitative view of feedback lexical markers in conversational French

This paper presents a quantitative description of the lexical items used for linguistic feedback in the Corpus of Interactional Data (CID). The paper includes the raw figures for feedback lexical item as well as more detailed figures concerning interindividual variability. This effort is a first step before a broader analysis including more discourse situations and featuring communicative function annotation.
Stabilization of RNT-1 Protein, Runt-related Transcription Factor (RUNX) Protein Homolog of Caenorhabditis elegans, by Oxidative Stress through Mitogen-activated Protein Kinase Pathway*

Background: RUNX proteins are important for development, but not much is known about the functions of RUNX after development. Results: RNT-1, the nematode RUNX homolog, was stabilized by oxidative stress through a p38 MAP kinase for proper response to oxidative stress. Conclusion: RNT-1 plays a role in defensive responses to oxidative stress. Significance: We identified a new role for a RUNX protein in a postdevelopmental process. RUNX proteins are evolutionarily conserved transcription factors known to be involved in various developmental processes. Here we report a new role for a RUNX protein: a role in stress response. We show that RNT-1, the Caenorhabditis elegans RUNX homolog, is constantly produced and degraded by the ubiquitination-proteasome pathway in the intestine of the nematode. RNT-1 was rapidly stabilized by oxidative stress, and the rnt-1-mutant animals were more sensitive to oxidative stress, indicating that rapid RNT-1 stabilization is a defense response against the oxidative stress. The MAP kinase pathway is required for RNT-1 stabilization, and RNT-1 was phosphorylated by SEK-1/PMK-1 in vitro. ChIP-sequencing analysis revealed a feedback loop mechanism of the MAP kinase pathway by the VHP-1 phosphatase in the RNT-1-mediated oxidative stress response. We propose that rnt-1 is regulated at the protein level for its role in the immediate response to environmental challenges in the intestine.
A Robot Learning to Play a Mobile Game Under Unknown Dynamics

With the advance in robotic hardware and intelligent software, humanoid robot could play an important role in various fields including service for human assistance and heavy job for hazardous industry. Under unknown dynamics operating smart devices with a humanoid robot is a even more challenging task because a robot needs to learn both swipe actions and complex state transitions inside the smart devices in a long time horizon. Recent advances in task learning enable humanoid robots to conduct dexterous manipulation tasks such as grasping objects and assembling parts of furniture. In this paper, we explore another step further toward building a human-like robot by introducing an architecture which enables humanoid robots to learn operating smart devices requiring complex tasks. We implement our learning architecture in the Baxter Research Robot and experimentally demonstrate that the robot with our architecture could play a challenging mobile game, the 2048 game, as accurate as in a simulated environment.