Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration environment for 3D heterogeneous tree-based FPGA architectures (3D HT-FPGA)

We describe the physical design and exploration methodology to optimize 3-dimensional (3D) heterogeneous Tree-based FPGA (HT-FPGA) by introducing a break-point at a particular tree level interconnect to optimize the speed, power consumption and area. The ability of the flow to decide a horizontal or vertical partitioning of the multilevel programmable tree network based on design specifications is a defining feature. The break-point of vertically partitioned tree is designed to balance the placement of logic blocks and switch blocks into multiple tiers while the break-point of horizontally partitioned tree is designed to optimize the interconnect delay of the programmable tree network. We finally evaluate the performance, area and power consumption of the proposed 3D HT-FPGA using the newly developed flow and show that vertical and horizontally partitioned 3D stacked HT-FPGA improves speed by 16% and 55% respectively. Silicon footprint reduced by 50% for vertical and 46 % for horizontal partitioning method and power consumption reduced by 35% compared to 2D counterpart.
Inputs of aspect oriented programming for the profiling of C++ parallel applications on manycore platforms

High Performance Computing systems expect applications to leverage the most of their processing power. This need is even more present for applications such as Monte Carlo simulations that require noteworthy CPU time and memory footprint. Optimizing applications is one approach to reduce the consumption of these resources. Before optimizing, it is mandatory to profile the application in order to pinpoint bottlenecks and hot spots. In this paper, we propose an approach to applications profiling based on Aspect Oriented Programming (AOP). We introduce a profiling approach for C++ codes with the pthread library based on free open source software with low overhead, multicore awareness, multi-threading handling, ease of use and quality outputs compared to established profilers. We will present how our prototype, based on an AOP approach proved to be useful and efficient on a test application.
Replication Management Framework for HDFS Based on Prediction Technique

The number of application based on Apache Hadoop is increasing dramatically due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop File System (HDFS) provides the reliability, scalability and high availability to computation by applying a static replication strategy. However, because of the characteristics of parallel operations on the application layer, the accessing frequency for each data file in HDFS is totally different. Consequently, maintaining the same replicating mechanism for every data file might lead to bad effects on the performance. By rigorously considering the drawbacks of HDFS architecture, this paper proposes an approach to dynamically replicate the data file based on the predictive analysis. With the help of probability theory, the utilization of each data file can be predicted to create an individual replication strategy. Eventually, the data file can subsequently be replicated depending on its own access potential. Hence, this approach simultaneously improves the data locality while keeping the analogous redundancy of data storage in comparison with the default replicating scheme.
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments

Significant performance gains in deep learning coupled with the exponential growth of image and video data on the Internet have resulted in the recent emergence of automated image captioning systems. Ensuring scalability of automated image captioning systems with respect to the ever increasing volume of image and video data is a significant challenge. This paper provides a valuable insight in that the detection of a few significant (top) objects in an image allows one to extract other relevant information such as actions (verbs) in the image. We expect this insight to be useful in the design of scalable image captioning systems. We address two parameters by which the scalability of image captioning systems could be quantified, i.e., the traditional algorithmic time complexity which is important given the resource limitations of the user device and the system development time since the programmers' time is a critical resource constraint in many real-world scenarios. Additionally, we address the issue of how word embeddings could be used to infer the verb (action) from the nouns (objects) in a given image in a zero-shot manner. Our results show that it is possible to attain reasonably good performance on predicting actions and captioning images using our approaches with the added advantage of simplicity of implementation.
Defining Programming Problems as Learning Objects

— Standards for learning objects focus primarily on content presentation. They were already extended to support automatic evaluation but it is limited to exercises with a predefined set of answers. The existing standards lack the metadata required by specialized evaluators to handle types of exercises with an indefinite set of solutions. To address this issue existing learning object standards were extended to the particular requirements of a specialized domain. A definition of programming problems as learning objects, compatible both with Learning Management Systems and with systems performing automatic evaluation of programs, is presented in this paper. The proposed definition includes metadata that cannot be conveniently represented using existing standards, such as: the type of automatic evaluation; the requirements of the evaluation engine; and the roles of different assets - tests cases, program solutions, etc. The EduJudge project and its main services are also presented as a case study on the use of the proposed definition of programming problems as learning objects.
Are There Downward Nominal Rigidities in Product Markets?

This paper looks at disaggregated price data in the UK to see if there is evidence of downward nominal rigidity: are prices less likely to fall after a downward shock than they are to rise after an upward shock? The test is to see if, as the mean inflation rate falls, the skewness, or the tendency for price changes to bunch-up at zero, rises. The paper finds no convincing evidence of downward nominal rigidity.
Push-Pull Two-Layer Super-Peer based P2P Live Media Streaming

Peer-to-peer (P2P) file sharing has become increasingly popular, accounting for as much as 70% of Internet traffic by some estimates. Recently, we have been witnessing the emergence of a new class of popular P2P applications, namely, P2P audio and video streaming. In this paper, we propose and investigate a full distributed, scalable, and cooperative protocol for live video streaming in an overlay peer-to-peer network. Our protocol, termed P2P Super-Peer based Unstructured Live Media Streaming (PALMS-SP), makes use of combination of push-pull scheduling methods to achieve high performance (in term of delay, stream continuity, cooperation, etc.). The main contribution of PALMS-SP is that it reduces the end-to-end streaming delay and in turn results better delivered quality. We have extensively evaluated the performance of PALMS-SP. Our experiments demonstrate that PALMS-SP with the existence of super-peers achieves better streaming quality in comparison with other existing streaming applications.
RESEARCH METHODOLOGY STRATEGIES IN STRATEGIC MANAGEMENT

This paper review and examine how strategic management researchers apply research methods, and what strategies use as part of the research process, to locate, organize, manage, transform, create, communicate and evaluate research tools, data and information resources. It also analyzes recent developments on research methodology to create scientific knowledge in theory building and practice in strategic management offering an overview of methodologies used in strategic management research. The assessment of strategic management’s research methodology is based on a review and analysis of strategies for the incorporation of knowledge of managerial research methods. Finally, the paper identifies and discusses some methodological research issues and reviews future directions on research methodologies in strategic management.
Laying down the law : a discussion of the people, processes and problems that shape acts of parliament

In this book Daniel Greenberg draws on his experience as a legislative drafter to present a current account of how legislation is put together. In explaining the process of parliamentary drafting Greenberg identifies and examines parts of the legislative process that are not well-known, and offers thoughts on how the system works or should work. The book will appeal to undergraduate and postgraduate students of law, policy and politics - in fact, any reader with an interest in the British Government - and will be of interest to those involved in the preparation and practice of legislation.
Attention for Learning Signals in Anterior Cingulate Cortex

Learning theory suggests that animals attend to pertinent environmental cues when reward contingencies unexpectedly change so that learning can occur. We have previously shown that activity in basolateral nucleus of amygdala (ABL) responds to unexpected changes in reward value, consistent with unsigned prediction error signals theorized by Pearce and Hall. However, changes in activity were present only at the time of unexpected reward delivery, not during the time when the animal needed to attend to conditioned stimuli that would come to predict the reward. This suggested that a different brain area must be signaling the need for attention necessary for learning. One likely candidate to fulfill this role is the anterior cingulate cortex (ACC). To test this hypothesis, we recorded from single neurons in ACC as rats performed the same behavioral task that we have used to dissociate signed from unsigned prediction errors in dopamine and ABL neurons. In this task, rats chose between two fluid wells that produced varying magnitudes of and delays to reward. Consistent with previous work, we found that ACC detected errors of commission and reward prediction errors. We also found that activity during cue sampling encoded reward size, but not expected delay to reward. Finally, activity in ACC was elevated during trials in which attention was increased following unexpected upshifts and downshifts in value. We conclude that ACC not only signals errors in reward prediction, as previously reported, but also signals the need for enhanced neural resources during learning on trials subsequent to those errors.
The OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition

Opportunistic sensing can be used to obtain data from sensors that just happen to be present in the user’s surroundings. By harnessing these opportunistic sensor configurations to infer activity or context, ambient intelligence environments become more robust, have improved user comfort thanks to reduced requirements on body-worn sensor deployment and they are not limited to a predefined and restricted location, defined by sensors specifically deployed for an application. We present the OPPORTUNITY Framework and Data Processing Ecosystem to recognize human activities or contexts in such opportunistic sensor configurations. It addresses the challenge of inferring human activities with limited guarantees about placement, nature and run-time availability of sensors. We realize this by a combination of: (i) a sensing/context framework capable of coordinating sensor recruitment according to a high level recognition goal, (ii) the corresponding dynamic instantiation of data processing elements to infer activities, (iii) a tight interaction between the last two elements in an “ecosystem” allowing to autonomously discover novel knowledge about sensor characteristics that is reusable in subsequent recognition queries. This allows the system to operate in open-ended environments. We demonstrate OPPORTUNITY on a large-scale dataset collected to exhibit the sensor richness and related characteristics, typical of opportunistic sensing systems. The dataset comprises 25 hours of activities of daily living, collected from 12 subjects. It contains data of 72 sensors covering 10 modalities and 15 networked sensor systems deployed in objects, on the body and in the environment. We show the mapping from a recognition goal to an instantiation of the recognition system. We also show the knowledge acquisition and reuse of the autonomously discovered semantic meaning of a new unknown sensor, the autonomous update of the trust indicator of a sensor due to unforeseen deteriorations, and the autonomous discovery of the on-body sensor placement.
Received signal strength indication for movement detection

Wireless networks are spreading continuously, filling our homes and the world around us. By using a ZigBee network we will show that a person can be detected by analyzing the fluctuations of signal strength inside the network. The simplicity of our approach means that it could be extended to all wireless networks. This work shows both implications on privacy as well as promising advances in fields like home automation and smart devices by localizing people as they go about their daily lives.
Expression of calcium/calmodulin-dependent protein kinase in relation to dopamine islands and synaptic maturation in the cat striatum

During pre- and postnatal development the dopamine-containing nigrostriatal afferents of the striatum are arranged as a conspicuous series of patches (the “dopamine islands”). The development of this dopamine island system, which metamorphoses in early postnatal life to the striosomal architecture of the adult, has recently received considerable attention, but the factors initiating and influencing maturation of this architecture are largely unknown. In an attempt to clarify the relationships between the onset of clustering of dopamine- containing afferents, the grouping of neurons within future striosomes and the maturation of synapses in the striatum, we compared the initial prenatal appearance and subsequent development of immunohistochemical markers for the dopamine-containing innervation [tyrosine hydroxylase (TH)-like immunoreactivity], for synaptic vesicles (SV48-like immunoreactivity), and for a phosphorylation-related enzyme Ca2+/calmodulin-dependent protein kinase type II (CaM kinase II-like immunoreactivity) that is expressed in virtually all striatal neurons by adulthood. Here we present evidence that during striatal ontogeny, both neurons and neuropil expressing CaM kinase II-like immunoreactivity and SV48-positive terminals form discrete patches that are in register with dopamine islands. It is CaM kinase II-positive elements, however, rather than the TH-positive island fibers (or SV48- positive synapses), that initially form overt clusters. Dopamine- containing fibers begin to innervate the striatal anlage just prior to embryonic day (E) 32. Their distribution follows the general lateral to medial developmental gradient characteristic of the striatum but is not yet distinctly islandic. At this time, CaM kinase II-like immunoreactivity was very weak or not present at all and SV48-like immunoreactivity was undetectable. By E36, CaM kinase II-positive neurons are visible in discrete patches of immunopositive neuropil, but only faint inhomogeneities are detectable in the distribution of TH- positive fibers and scarcely any SV48-like immunoreactivity can be seen. By E45, all 3 markers are focused in typical islandic patterns, and they remain so into the early postnatal period. These observations suggest a developmental sequence in which dopamine-containing fibers invade the striatal anlage prior to forming distinct islandic foci and prior to the maturational events signaled by the production of CaM kinase II within the neurons and neuropil of future striosomes. (ABSTRACT TRUNCATED AT 400 WORDS)
Using decision rules for solving conflicts in extended feature models

Software Product Line Engineering has introduced feature modeling as a domain analysis technique used to represent the variability of software products and decision-making scenarios. We present a model-based transformation approach to solve conflicts among configurations performed by different stakeholders on feature models. We propose the usage of a domain-specific language named CoCo to specify attributes as non-functional properties of features, and to describe business-related decision rules in terms of costs, time, and human resources. These specifications along with the stakeholders' configurations and the feature model are transformed into a constraint programming problem, on which decision rules are executed to find a non-conflicting set of solution configurations that are aligned to business objectives. We evaluate CoCo's compositionality and model complexity simplification while using a set of motivating decision scenarios.
Poverty in India and Its Decompositions: A Critical Appraisal of the New Method

This paper has two objectives. First, it critically discusses the new method of measuring poverty. In doing so, it raises some concerns implicit in the report (a) the need to go beyond calories to have an understanding of nutritional requirement, which could not be adequately addressed in the new method, (b) the need to incorporate expenditure on health, education and sanitation, as these are not being adequately provided by the state, which also raises serious apprehensions on whether India is a welfare state, and (c) the need to come up with multi-dimensional measures of poverty. Nevertheless, the paper contends that the approach followed in the report is pragmatic, but some additional information could have been put in the public domain to take the debate beyond the poverty line. Second, we use the new poverty lines to compute incidence, depth and severity and inequality across states and some other subgroups as also sectoral and growth-inequality decompositions at the aggregate all India level for 2004-5 and 2009-10. Among the old hotspots of BiMORU, Rajasthan and Uttaranchal have moved out and Assam has come in, Madhya Pradesh and Odisha show considerable reductions, but it is the increase of incidence in Bihar (which otherwise seems to have done well during this period) that raises curious eyebrows. The seven major states with high incidences are referred to as CABMOUJ (pronounced kab mouj, which in Hindi means ‘when to relax’). There is an increase in urbanisation of poverty. Four Northeast states and two smaller union territories have also shown some reversals in poverty reductions. Analysis across caste, occupations, religion, land size, and gender show the usual expected patterns, but of concern is greater vulnerability of children (0-14 age group) and increasing poverty levels at higher education groups. Appropriate planning and implementation will require complementing this information with quantitative and qualitative information at the district and sub-district level.
Towards modular resource-aware applications

Resource optimization is an increasingly important requirement in the design and implementation of software systems. It is applied to improve both environmental sustainability and usability of resource-constrained devices. This paper claims that to achieve more modular resource-aware applications, the resource utilization of components must explicitly be modeled. Due to shortcomings of existing modeling languages, we propose a notation for the resource consumption of components and we illustrate the suitability of this notation by means of two real-world examples. We observe that explicitly modeling resource consumption has as result that resource consumption information is scattered across and tangled with the functional services of components.
Contrasting effects of plant inter‐ and intraspecific variation on community trait responses to restoration of a sandy grassland ecosystem

Abstract Changes in plant community traits along an environmental gradient are caused by interspecific and intraspecific trait variation. However, little is known about the role of interspecific and intraspecific trait variation in plant community responses to the restoration of a sandy grassland ecosystem. We measured five functional traits of 34 species along a restoration gradient of sandy grassland (mobile dune, semi‐fixed dune, fixed dune, and grassland) in Horqin Sand Land, northern China. We examined how community‐level traits varied with habitat changes and soil gradients using both abundance‐weighted and non‐weighted averages of trait values. We quantified the relative contribution of inter‐ and intraspecific trait variation in specific leaf area (SLA), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), and plant height to the community response to habitat changes in the restoration of sandy grassland. We found that five weighted community‐average traits varied significantly with habitat changes. Along the soil gradient in the restoration of sandy grassland, plant height, SLA, LDMC, and LCC increased, while LNC decreased. For all traits, there was a greater contribution of interspecific variation to community response in regard to habitat changes relative to that of intraspecific variation. The relative contribution of the interspecific variation effect of an abundance‐weighted trait was greater than that of a non‐weighted trait with regard to all traits except LDMC. A community‐level trait response to habitat changes was due largely to species turnover. Though the intraspecific shift plays a small role in community trait response to habitat changes, it has an effect on plant coexistence and the maintenance of herbaceous plants in sandy grassland habitats. The context dependency of positive and negative covariation between inter‐ and intraspecific variation further suggests that both effects of inter‐ and intraspecific variation on a community trait should be considered when understanding a plant community response to environmental changes in sandy grassland ecosystems.
Microarchitecture and implementation of the synergistic processor in 65-nm and 90-nm SOI

This paper describes the architecture and implementation of the original gaming-oriented synergistic processor element (SPE) in both 90-nm and 65-nm silicon-on-insulator (SOI) technology and introduces a new SPE implementation targeted for the high-performance computing community. The Cell Broadband Engine™ processor contains eight SPEs. The dual-issue, four-way single-instruction multiple-data processor is designed to achieve high performance per area and power and is optimized to process streaming data, simulate physical phenomena, and render objects digitally. Most aspects of data movement and instruction flow are controlled by software to improve the performance of the memory system and the core performance density. The SPE was designed as an 11-F04 (fan-out-of-4-inverter-delay) processor using 20.9 million transistors within 14.8 mm 2 using the IBM 90-nm SOI low-k process. CMOS (complementary metal-oxide semiconductor) static gates implement the majority of the logic. Dynamic circuits are used in critical areas and occupy 19% of the non-static random access memory (SRAM) area. Instruction set architecture, microarchitecture, and physical implementation are tightly coupled to achieve a compact and power-efficient design. Correct operation has been observed at up to 5.6 GHz and 7.3 GHz, respectively, in 90-nm and 65-nm SOI technology.