Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermally Adaptive Cache Access Mechanisms for 3D Many-Core Architectures

A compelling confluence of technology and application trends in which the cost, execution time, and energy of applications are being dominated by the memory system is driving the industry to 3D packages for future microarchitectures. However, these packages result in high heat fluxes and increased thermal coupling challenging current thermal solutions. Conventional design approaches utilize design margins that correspond to worst case temperatures and process corners leading to a significant impact on system level performance. This paper advocates a design approach based on microarchitecture adaptation to device-level temperature-dependent delay variations to realize average case performance that is superior to which can be achieved by using worst case design margins. We demonstrate this approach with adaptation principles for the last level cache (LLC) in a 3D many-core architecture. We propose and evaluate two adaptation mechanisms. In the first case, the access time to the LLC from the L1 tracks the LLC's temperature-delay variations. In the second case, the processor DVFS state tracks the LLC temperature as a negative feedback. Compared to a worst case design baseline, the full system simulation results show that both approaches increase the IPC by over 20 percent, and improve the energy efficiency by up to 3 percent.
Mirror neuron system based therapy for aphasia rehabilitation

Objective: To investigate the effect of hand action observation training, i.e., mirror neuron system (MNS) based training, on language function of aphasic patients after stroke. In addition, to reveal the tentative mechanism underlying this effect. Methods: Six aphasic patients after stroke, meeting the criteria, undergo 3 weeks' training protocol (30 min per day, 6 days per week). Among them, four patients accepted an ABA training design, i.e., they implemented Protocol A (hand action observation combined with repetition) in the first and third weeks and carried out Protocol B (static object observation combined with repetition) in the second week. Conversely, for the other two patients, BAB training design was adopted, i.e., patients took Protocol B in the first and third weeks and accepted Protocol A in the second week. Picture naming test, western aphasia battery (WAB) and Token Test were applied to evaluate the changes of language function before and after each week's training. Furthermore, two subjects (one aphasic patient and one healthy volunteer) attended a functional MRI (fMRI) experiment, by which we tried to reveal the mechanism underlying possible language function changes after training. Results: Compared with static object observation and repetition training (Protocol B), hand action observation and repetition training (Protocol A) effectively improved most aspects of the language function in all six patients, as demonstrated in the picture naming test, subtests of oral language and aphasia quotient (AQ) of WAB. In addition, the fMRI experiment showed that Protocol A induced more activations in the MNS of one patient and one healthy control when compared to Protocol B. Conclusion: The mirror neuron based therapy may facilitate the language recovery for aphasic patients and this, to some extent, provides a novel direction of rehabilitation for aphasia patients.
Classification of weather situations on single color images

Present vision based driver assistance systems are designed to perform under good-natured weather conditions. However, limited visibility caused by heavy rain or fog strongly affects vision systems. To improve machine vision in bad weather situations, a reliable detection system is necessary as a ground base. We present an approach that is able to distinguish between multiple weather situations based on the classification of single monocular color images, without any additional assumptions or prior knowledge. The proposed image descriptor clearly outperforms existing descriptors for that task. Experimental results on real traffic images are characterized by high accuracy, efficiency, and versatility with respect to driver assistance systems.
Problems in Assessment of Novel Biopotential Front-End with Dry Electrode: A Brief Review

Developers of novel or improved front-end circuits for biopotential recordings using dry electrodes face the challenge of validating their design. Dry electrodes allow more user-friendly and pervasive patient-monitoring, but proof is required that new devices can perform biopotential recording with a quality at least comparable to existing medical devices. Aside from electrical safety requirement recommended by standards and concise circuit requirement, there is not yet a complete validation procedure able to demonstrate improved or even equivalent performance of the new devices. This short review discusses the validation procedures presented in recent, landmark literature and offers interesting issues and hints for a more complete assessment of novel biopotential front-end.
Viral Opinion Spreading Model in Social Networks

We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Klein berg " the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.
Polarity analysis of texts using discourse structure

Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.
Votes for Women: Electoral Systems and Support for Female Candidates

It is a well-established finding that proportional representation (PR) electoral systems are associated with greater legislative representation for women than single member systems. However, the degree to which different types of PR rules affect voting for female candidates has not been fully explored. The existing literature is also hampered by a reliance on cross-national data in which individual vote preferences and electoral system features are endogenous. In this study, we draw upon an experiment conducted during the 2014 European Parliament (EP) elections to isolate the effects of different PR electoral systems. Participants in the experiment were given the opportunity to vote for real EP candidates in three different electoral systems: closed list, open list, and open list with panachage and cumulation. Because voter preferences can be held constant across the three different votes, we can evaluate the extent to which female candidates were more or less advantaged by the electoral system itself. We find that voters, regardless of their gender, support female candidates, and that this support is stronger under open electoral rules.
High-resolution radio observations of Seyfert galaxies in the extended 12-μm sample - II. The properties of compact radio components

We discuss the properties of compact nuclear radio components in Seyfert galaxies from the extended 12-μm AGN sample of Rush et al. Our main results can be summarized as follows. Type 1 and type 2 Seyferts produce compact radio components which are indistinguishable in strength and aspect, indicating that their central engines are alike, as proposed by the unification model. Infrared IRAS fluxes are more closely correlated with low-resolution radio fluxes than high-resolution radio fluxes, suggesting that they are dominated by kiloparsec-scale, extranuclear emission regions; extranuclear emission may be stronger in type 2 Seyferts. Early-type Seyfert galaxies tend to have stronger nuclear radio emission than late-type Seyfert galaxies. V-shaped extended emission-line regions, indicative of ‘ionization cones’, are usually found in sources with large, collimated radio outflows. Hidden broad lines are most likely to be found in sources with powerful nuclear radio sources. Type 1 and type 2 Seyferts selected by their IRAS 12-μm flux densities have well-matched properties.
SIMILARITY‐BASED RETRIEVAL WITH STRUCTURE‐SENSITIVE SPARSE BINARY DISTRIBUTED REPRESENTATIONS

We present an approach to similarity‐based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single‐stage approach provides results compatible with or better than the results of two‐stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large‐scale knowledge bases and free‐structured database applications.
Intelligent Embedded System based Remote Monitoring using Mobile

The central idea of this research is to develop low cost intelligent remote monitoring system using mobile (cell phone) with emphasis on its utilization in rural areas. In past few years, there has been tremendous rise in number of mobile users in India. Due to widespread growth of wireless cellular networks and drastic reduction in call rates and handsets, mobile usage has percolated all sections of society from business magnates to skilled and unskilled laborers like carpenters, masons, farmers and even dabbawalahs. Cell phone is gradually emerging as powerful tool for many commercial applications like train reservation booking, banking, etc. Remote monitoring of processes, machines, etc is popular due to advances in technology and reduction in hardware cost. Internet based monitoring is one of common approaches of remote monitoring. Cellular networks provide Short Messaging Service (SMS) and Multimedia Messaging Service (MMS), which have been utilized by many researchers for telemetry applications especially in medical field. Wireless sensor networks (WSN) also offer attractive opportunity for remote monitoring. However, deployment entails substantial investments in infrastructure. Major applications of WSN are in field of environment monitoring, defense, etc. The research work presented here aimed to provide cellular phone based remote controlled smart embedded system with various features to provide status of system and fault detection capabilities. It was observed that technological capabilities of cellular phones have varied widely during last decade from simple voice and messaging features to very powerful ones having high resolution camera, high speed GPRS capabilities, etc and connection interface have varied from simple RS-232c based data link, USB based link to wireless Bluetooth and Wi-Fi based links. There have been rapid advances in microcontroller technologies and powerful processors with low power consumption have been developed. Due to diverse technological developments, work was carried out on range of Nokia cell phones starting from 3310 model which works on F-Bus protocols to 2700 classic which is Series 40 5 th Edition model which involved the use of Java ME platform. Major focus of the work was to develop system which can cater to the needs of local rural population where industrial firms are reluctant to invest due to lower returns and lack of suitable infrastructural facilities. The work has achieved tremendous success in this regard as it is able to offer remote control capability using obsolete cell phone model and even cell phone having non-working display and operational cost can be …
American Journal of Epidemiology Original Contribution Personality Traits and Cancer Risk and Survival Based on Finnish and Swedish Registry Data Personality Traits and Cancer Risk and Survival 379

Personality traits have been studied extensively as risk and prognostic factors for cancer; however, the association remains unclear. This prospective, population-based cohort study comprised 59,548 Swedish (1974–1999) and Finnish (1976–2004) participants who completed a questionnaire eliciting information for the Eysenck Personality Inventory and on health behavior at baseline. To analyze the association of personality traits extraversion and neuroticism with risk of cancer, the authors identified 4,631 cancer cases for a maximum 30 years of follow-up. To assess the association with cancer survival among the Finnish participants, they identified 2,733 cancer cases and, later, 1,548 deaths for a maximum 29 years of follow-up. Hazard ratios were estimated by treating the personality scales as continuous variables and are presented per one increase in score on each scale. In multi-variate analyses, extraversion and neuroticism were not significantly associated with risk of cancers at all sites (extraversion: hazard ratio ¼ 0.99, 95% confidence interval: 0.98, 1.01; neuroticism: hazard ratio ¼ 1.00, 95% confidence interval: 0.99, 1.02). Results showed no significant association between these traits and the hazard ratio for death after cancers at all sites, and they do not support the hypothesis that extraversion and neuroticism are direct risk factors for cancer or survival after cancer. Personality has long been hypothesized to play a causal role in the development and progression of cancer. In 1962, Kissen and Eysenck (1) conducted one of the first modern studies of the association between personality and cancer and reported that, compared with hospital controls, patients with lung cancer were more likely to be extraverted and less likely to be neurotic. Their theory could be interpreted to suggest that extraverts are at increased risk of cancer because they seek stimulation and thus experience high levels of stress, whereas individuals with low levels of neuroticism could be at increased risk of cancer because they tend to have a reduced emotional outlet and thus accumulate emotional stress (2). The higher exposure to stress could influence cancer risk by influencing immune and endocrine function (3, 4). Supporting this theory, Morris et al. (5) reported that persons with lower levels of neuroticism and trait anxiety scores were at increased risk of breast cancer. Since then, the results of several well-conducted prospective studies have not confirmed an association between personality traits (e.g., extraversion (6–8), neuroticism (6–9), and trait anxiety (10)) and cancer risk; however, the majority of these studies had methodological limitations, including a …
Absence of NMDA receptors in dopamine neurons attenuates dopamine release but not conditioned approach during Pavlovian conditioning

During Pavlovian conditioning, phasic dopamine (DA) responses emerge to reward-predictive stimuli as the subject learns to anticipate reward delivery. This observation has led to the hypothesis that phasic dopamine signaling is important for learning. To assess the ability of mice to develop anticipatory behavior and to characterize the contribution of dopamine, we used a food-reinforced Pavlovian conditioning paradigm. As mice learned the cue–reward association, they increased their head entries to the food receptacle in a pattern that was consistent with conditioned anticipatory behavior. D1-receptor knockout (D1R-KO) mice had impaired acquisition, and systemic administration of a D1R antagonist blocked both the acquisition and expression of conditioned approach in wild-type mice. To assess the specific contribution of phasic dopamine transmission, we tested mice lacking NMDA-type glutamate receptors (NMDARs) exclusively in dopamine neurons (NR1-KO mice). Surprisingly, NR1-KO mice learned at the same rate as their littermate controls. To evaluate the contribution of NMDARs to phasic dopamine release in this paradigm, we performed fast-scan cyclic voltammetry in the nucleus accumbens of awake mice. Despite having significantly attenuated phasic dopamine release following reward delivery, KO mice developed cue-evoked dopamine release at the same rate as controls. We conclude that NMDARs in dopamine neurons enhance but are not critical for phasic dopamine release to behaviorally relevant stimuli; furthermore, their contribution to phasic dopamine signaling is not necessary for the development of cue-evoked dopamine or anticipatory activity in a D1R-dependent Pavlovian conditioning paradigm.
Estimating Effects and Making Predictions from Genome-Wide Marker Data

In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called ``winner's curse. '' We argue that the classical definition of unbiasedness is not useful in this context and propose to use a different definition of unbiasedness that is a property of the estimator we advocate. We suggest an integrated approach to the estimation of the SNP effects and to the prediction of trait values, treating SNP effects as random instead of fixed effects. Statistical methods traditionally used in the prediction of trait values in the genetics of livestock, which predates the availability of SNP data, can be applied to analysis of GWAS, giving better estimates of the SNP effects and predictions of phenotypic and genetic values in individuals.
Low-Cost Protection for SER Upsets and Silicon Defects

Extreme transistor scaling trends in silicon technology are soon to reach a point where manufactured systems will suffer from limited device reliability and severely reduced life-time, due to early transistor failures, gate oxide wear-out, manufacturing defects, and radiation-induced soft errors (SER). In this paper we present a low-cost technique to harden a microprocessor pipeline and caches against these reliability threats. Our approach utilizes online built-in self-test (BIST) and microarchitectural checkpointing to detect, diagnose and recover the computation impaired by silicon defects or SER events. The approach works by periodically testing the processor to determine if the system is broken. If so, we reconfigure the processor to avoid using the broken component. A similar mechanism is used to detect SER faults, with the difference that recovery is implemented by re-execution. By utilizing low-cost techniques to address defects and SER, we keep protection costs significantly lower than traditional fault-tolerance approaches while providing high levels of coverage for a wide range of faults. Using detailed gate-level simulation, we find that our approach provides 95% and 99% coverage for silicon defects and SER events, respectively, with only a 14% area overhead
Blockchain-based Database to Ensure Data Integrity in Cloud Computing Environments

Data is nowadays an invaluable resource; indeed, it guides all business decisions in most of the computer-aided human activities. Threats to data integrity are thus of paramount relevance, as tampering with data may maliciously affect crucial business decisions. This issue is especially true in cloud computing environments, where data owners cannot control fundamental data aspects, like the physical storage of data and the control of its accesses. Blockchain has recently emerged as a fascinating technology which, among others, provides compelling properties about data integrity. Using the Blockchain to face data integrity threats seems to be a natural choice, but its current limitations of low throughput, high latency, and weak stability hinder the practical feasibility of any Blockchain-based solutions. In this paper, by focusing on a case study from the European SUNFISH project, which concerns the design of a secure by-design cloud federation platform for the public sector, we precisely delineate the actual data integrity needs of cloud computing environments and the research questions to be tackled to adopt Blockchain-based databases. First, we detail the open research questions and the difficulties inherent in addressing them. Then, we outline a preliminary design of an effective Blockchain-based database for cloud computing environments.
New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops

The fraction of absorbed photosynthetically active radiation, fAPAR, is an important biophysical characteristic in models of gas exchange between the terrestrial boundary layer and the atmosphere, as well as in the analysis of vegetation productivity. Synoptic estimation of fAPAR has been performed by using NDVI as a linear proxy of fAPAR, despite the saturation of NDVI at fAPAR beyond 0.7. This paper analyzes the NDVI/fAPAR relationship in row crops (i.e. maize and soybean), and evaluates alternative vegetation indices to overcome the loss of sensitivity of NDVI at moderate‐to‐high vegetation biomass. Red‐edge NDVI, which uses NIR and a band around 700 nm and the recently proposed Wide Dynamic Range Vegetation Index, which uses red and NIR bands only, were found to be sensitive to fAPAR variation along its entire range and exhibited significant increase in sensitivity to fAPAR.
Information Theory Information rate bounds in common-mode aided wireline communications

Communication over the copper twisted-pair channel is performed by transmitting and receiving differential-mode (DM) signals. In this paper, we extend the conventional DM receiver scheme by incorporating the common-mode (CM) signal, which is strongly correlated with the DM signal and can be extracted at the end of every wire pair. The wireline channel, including the CM receive signal, is modelled as a Gaussian vector (multiple-input multiple-output) channel with memory and an arbitrary number of disturbers that introduce interference which can be correlated both temporally and spatially. We investigate the potential of CM-aided reception in terms of information rate for single-user digital subscriber line systems (xDSL) operating on a single pair. Numerical results of information rate calculations using measured channel data are presented. The achievable information rate of the twisted-pair channel when incorporating the CM signal in practically relevant example scenarios can exceed the information rate of the conventional DM channel by a factor of up to two. Copyright (C) 2005 AEIT. (Less)