Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast converging algorithm for sum power minimization in MIMO uplink

The sum power minimization with per MS rate constraint problem in multiple-input multiple-output (MIMO) multiple access channels (MACs) is considered in this paper. The problem can be transformed into a series of convex weighted sum rate problems and iteratively solved by standard solvers. However, even with the efficient interior-point method in the latest solver, the computational effort to solve the convex problem in each iteration is still prohibitively high. We propose an efficient algorithm that utilizes the convexity of the rate region to reduce number of iterations. It consists of power bisection and optimal user weights search. The algorithm avoids the full search for the optimal user weights. By continuously constructing a convex rate polyhedron, it determines the feasibility of the current rate target at a early phase and immediately goes to the power bisection without knowing the optimal user weights. The proposed algorithm is compared with the existing methods and is shown to be significantly more efficient than the existing methods in terms of computational complexities.
Graphene transistors 2011

Graphene is a promising electronic material and numerous experts already see it as the successor of silicon. Many groups are fabricating graphene transistors, and graphene MOSFETs with record cutoff frequencies of 300 GHz have been reported. However, in spite of this progress, the prospects of graphene transistors for future electronics are still not clear. The aim of the present paper is to shed some light on the merits and problems of graphene transistors and to review their current status.
The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach

In this paper we analyze the macroeconomic forecasts of the Consensus Forecasts for 12 countries over the period from 1996 to 2006 regarding bias and information efficiency. A pooled approach is employed which permits the evaluation of all forecasts for each target variable over 24 horizons simultaneously. It is shown how the pooled approach needs to be adjusted in order to accommodate the forecasting scheme of the Consensus Forecasts. Furthermore, the pooled approach is extended by a sequential test with the purpose of detecting the critical horizon after which the forecast should be regarded as biased. Moreover, heteroscedasticity in the form of year-specific variances of macroeconomic shocks is taken into account. The results show that in the analyzed period which was characterized by pronounced macroeconomic shocks, several countries show biased forecasts, especially with forecasts covering more than 12 months. In addition, information efficiency has to be rejected in almost all cases.
An 8/spl times/8 array of resonant cavity enhanced light emitting diodes integrated onto silicon grayscale (32 level) driver circuitry

We demonstrate a fully operational 8/spl times/8 array of GaAS RCELEDs integrated directly on top of a silicon grayscale array silicon driver circuit. This integrated optoelectronic circuit allowed 32-level grayscale emission from each individually addressable pixel. A yield of 100% was achieved from the array, with full circuit functionality demonstrated.
Cube Index: A Text Index Model for Retrieval and Mining

Text retrieval, Analysis, Mining and Knowledge management have gained a lot of importance in a time when we drown in information but are starved for knowledge. In this paper, we propose a novel Index that uses a Text Cube model to store the text information similar to a data cube in Data Mining. This model creates a direct index, next word index and inverted index in a single Cube Index which is three dimensional in nature. The Dimensions considered are first word, next word and document. The measure of the cube is the frequency of occurrence of the word next-word pair. The cube index has been tested by modifying the open source of terrier 2.1.
Performance of the polyurethane-matrix nanocomposites with different polymer types in polymer/organoclay system

Different conditions of process and preparation methods to intercalate and exfoliate organoclay in constituents of final nanocomposite, based on polyol or isocyanate have been studied. Different formulations of polyurethane-matrix nanocomposites have been examined by X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission gun scanning electron microscopy (FEGSEM), and rheological measurement. The results obtained from this study provide a good understanding of the relationship between the formulation, processing conditions, and performance. The processing conditions have great effects on the final properties of nanocomposites such as mechanical properties. The results show a high performance nanocomposite systems produced by high strength ultra-sonication in a short time.
A Non-Verbal Turing Test: Differentiating Mind from Machine in Gaze-Based Social Interaction

In social interaction, gaze behavior provides important signals that have a significant impact on our perception of others. Previous investigations, however, have relied on paradigms in which participants are passive observers of other persons’ gazes and do not adjust their gaze behavior as is the case in real-life social encounters. We used an interactive eye-tracking paradigm that allows participants to interact with an anthropomorphic virtual character whose gaze behavior is responsive to where the participant looks on the stimulus screen in real time. The character’s gaze reactions were systematically varied along a continuum from a maximal probability of gaze aversion to a maximal probability of gaze-following during brief interactions, thereby varying contingency and congruency of the reactions. We investigated how these variations influenced whether participants believed that the character was controlled by another person (i.e., a confederate) or a computer program. In a series of experiments, the human confederate was either introduced as naïve to the task, cooperative, or competitive. Results demonstrate that the ascription of humanness increases with higher congruency of gaze reactions when participants are interacting with a naïve partner. In contrast, humanness ascription is driven by the degree of contingency irrespective of congruency when the confederate was introduced as cooperative. Conversely, during interaction with a competitive confederate, judgments were neither based on congruency nor on contingency. These results offer important insights into what renders the experience of an interaction truly social: Humans appear to have a default expectation of reciprocation that can be influenced drastically by the presumed disposition of the interactor to either cooperate or compete.
Saturated BLAST: an automated multiple intermediate sequence search used to detect distant homology

MOTIVATION Two proteins can have a similar 3-dimensional structure and biological function, but have sequences sufficiently different that traditional protein sequence comparison algorithms do not identify their relationship. The desire to identify such relations has led to the development of more sensitive sequence alignment strategies. One such strategy is the Intermediate Sequence Search (ISS), which connects two proteins through one or more intermediate sequences. In its brute-force implementation, ISS is a strategy that repetitively uses the results of the previous query as new search seeds, making it time-consuming and difficult to analyze. RESULTS Saturated BLAST is a package that performs ISS in an efficient and automated manner. It was developed using Perl and Perl/Tk and implemented on the LINUX operating system. Starting with a protein sequence, Saturated BLAST runs a BLAST search and identifies representative sequences for the next generation of searches. The procedure is run until convergence or until some predefined criteria are met. Saturated BLAST has a friendly graphic user interface, a built-in BLAST result parser, several multiple alignment tools, clustering algorithms and various filters for the elimination of false positives, thereby providing an easy way to edit, visualize, analyze, monitor and control the search. Besides detecting remote homologies, Saturated BLAST can be used to maintain protein family databases and to search for new genes in genomic databases.
DLK1 Is a Somato-Dendritic Protein Expressed in Hypothalamic Arginine-Vasopressin and Oxytocin Neurons

Delta-Like 1 Homolog, Dlk1, is a paternally imprinted gene encoding a transmembrane protein involved in the differentiation of several cell types. After birth, Dlk1 expression decreases substantially in all tissues except endocrine glands. Dlk1 deletion in mice results in pre-natal and post-natal growth deficiency, mild obesity, facial abnormalities, and abnormal skeletal development, suggesting involvement of Dlk1 in perinatal survival, normal growth and homeostasis of fat deposition. A neuroendocrine function has also been suggested for DLK1 but never characterised. To evaluate the neuroendocrine function of DLK1, we first characterised Dlk1 expression in mouse hypothalamus and then studied post-natal variations of the hypothalamic expression. Western Blot analysis of adult mouse hypothalamus protein extracts showed that Dlk1 was expressed almost exclusively as a soluble protein produced by cleavage of the extracellular domain. Immunohistochemistry showed neuronal DLK1 expression in the suprachiasmatic (SCN), supraoptic (SON), paraventricular (PVN), arcuate (ARC), dorsomedial (DMN) and lateral hypothalamic (LH) nuclei. DLK1 was expressed in the dendrites and perikarya of arginine-vasopressin neurons in PVN, SCN and SON and in oxytocin neurons in PVN and SON. These findings suggest a role for DLK1 in the post-natal development of hypothalamic functions, most notably those regulated by the arginine-vasopressin and oxytocin systems.
The Stop Signal Revised: Immature Cerebellar Granule Neurons in the External Germinal Layer Arrest Pontine Mossy Fiber Growth

During the formation of neuronal circuits, afferent axons often enter target regions before their target cells are mature and then make temporary contacts with nonspecific targets before forming synapses on specific target cells. The regulation of these different steps of afferent-target interactions is poorly understood. The cerebellum is a good model for addressing these aspects, because cerebellar development is well defined and identified neurons in the circuitry can be purified and combined in vitro. Previous reports from our laboratory showed that cultured granule neurons specifically arrest the extension of their pontine mossy fiber afferents, leading us to propose that granule cells arrested growth of their afferents as a prelude to synaptogenesis. However, we knew little about the differentiation state of the cultured granule cells that mediate afferent arrest. In this study, we better define the purified granule cell fraction by marker expression and morphology, and demonstrate that only freshly plated granule cells in the precursor and premigratory state arrest mossy fiber outgrowth. Mature granule cells, in contrast, support extension, defasciculation, and synapse formation, as in vivo. In addition, axonal tracing in vivo during the first postnatal week indicates that immature mossy fibers extend into the Purkinje cell layer but never into the external germinal layer (EGL), where precursors of granule cell targets reside. We found that the stop-growing signals are dependent on heparin-binding factors, and we propose that such signals in the EGL restrict the extension of mossy fiber afferents and prevent invasion of proliferative regions.
Enrichment of Bilingual Dictionary through News Stream Data

Bilingual dictionaries are the key component of the cross-lingual similarity estimation methods. Usually such dictionary generation is accomplished by manual or automatic means. Automatic generation approaches include to exploit parallel or comparable data to derive dictionary entries. Such approaches require large amount of bilingual data in order to produce good quality dictionary. Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora. In this work we propose a method which exploits continuous quasi-comparable corpora to derive term level associations for enrichment of such limited dictionary. Though we propose our experiments for English and Hindi, our approach can be easily extendable to other languages. We evaluated dictionary by manually computing the precision. In experiments we show our approach is able to derive interesting term level associations across languages.
Hybrid Motion Control combining Inverse Kinematics and Inverse Dynamics Controllers for Simulating Percussion Gestures

Virtual characters playing in a realistic way virtual musical instruments need to interact in real-time with the simulated sounding environment. Dynamic simulation is a promising approach to finely represent and modulate this interaction. Moreover, capturing human motion provides a database covering a large variety of gestures with different levels of expressivity. We propose in this paper a new data-driven hybrid control technique combining Inverse Kinematics (IK) and Inverse Dynamics (ID) controllers, and we define an application for consistently editing the motion to be simulated by virtual characters performing percussion gestures.
Detection of New Biased Agonists for the Serotonin 5-HT2A Receptor: Modeling and Experimental Validation

Detection of biased agonists for the serotonin 5-HT2A receptor can guide the discovery of safer and more efficient antipsychotic drugs. However, the rational design of such drugs has been hampered by the difficulty detecting the impact of small structural changes on signaling bias. To overcome these difficulties, we characterized the dynamics of ligand-receptor interactions of known biased and balanced agonists using molecular dynamics simulations. Our analysis revealed that interactions with residues S5.46 and N6.55 discriminate compounds with different functional selectivity. Based on our computational predictions, we selected three derivatives of the natural balanced ligand serotonin and experimentally validated their ability to act as biased agonists. Remarkably, our approach yielded compounds promoting an unprecedented level of signaling bias at the 5-HT2A receptor, which could help interrogate the importance of particular pathways in conditions like schizophrenia.
XSnippet: mining For sample code

It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.
Evolutionary constraints on visual cortex architecture from the dynamics of hallucinations

In the cat or primate primary visual cortex (V1), normal vision corresponds to a state where neural excitation patterns are driven by external visual stimuli. A spectacular failure mode of V1 occurs when such patterns are overwhelmed by spontaneously generated spatially self-organized patterns of neural excitation. These are experienced as geometric visual hallucinations. The problem of identifying the mechanisms by which V1 avoids this failure is made acute by recent advances in the statistical mechanics of pattern formation, which suggest that the hallucinatory state should be very robust. Here, we report how incorporating physiologically realistic long-range connections between inhibitory neurons changes the behavior of a model of V1. We find that the sparsity of long-range inhibition in V1 plays a previously unrecognized but key functional role in preserving the normal vision state. Surprisingly, it also contributes to the observed regularity of geometric visual hallucinations. Our results provide an explanation for the observed sparsity of long-range inhibition in V1—this generic architectural feature is an evolutionary adaptation that tunes V1 to the normal vision state. In addition, it has been shown that exactly the same long-range connections play a key role in the development of orientation preference maps. Thus V1’s most striking long-range features—patchy excitatory connections and sparse inhibitory connections—are strongly constrained by two requirements: the need for the visual state to be robust and the developmental requirements of the orientational preference map.
The Effect of Deposition Conditions and Annealing on the Performance of High-Mobility GIZO TFTs

The influence of oxygen content, radio-frequency (rf) sputtering power, and postdeposition annealing on the electrical properties of gallium-indium-zinc oxide (GIZO) thin-film transistors (TFTs) is analyzed. Little to no oxygen content in the sputtering chamber is crucial to obtain high-performance devices, even before annealing. In contrast, a high oxygen content and rf power lead, respectively, to unstable/poor performing and depletion mode TFTs before annealing, and mainly for these "nonideal" conditions, annealing proves to be effective to improve device performance/stability and to decrease the performance discrepancy among TFTs processed under different oxygen and rf power conditions. Best TFTs present a field-effect mobility of 46 cm 2 /V s, subthreshold swing of 0.26 V/dec, threshold voltage of 0.70 V, and an on/off ratio 10 8 -10 9 . These results are a consequence of the optimized processing and of the usage of proper GIZO target composition, 1:2:1 mol.
Polarity Analysis Using Sentiwordnet Based an Algorithm

sentiment analysis aim to get the underlying viewpoint of text which could be opinion, online review, movie rating comments etc. the aim of this project is to offer better sentiment text analysis strategy which recognize the polarity of text message including positive, negative, and neutral using sentiwordnet. The contribution of this paper is use POS(parts of speech) tagger to examine specific prior polarity of text. Polarity analysis has been an important subtask in sentiment analysis but detecting correct polarity has been a major issues. Polarity enhancers and negations detection effect the overall polarity in an abnormal way. So we cannot depend on polarity of a particular word alone for accurate results. This paper discuss all the possible approaches available for accurate sentiment analysis and also the issues in detecting correct polarity of a sentence. Keywords-sentiment analysis, sentiwordnet, POS tagger, Polarity
Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems

The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained.
FastCap: An efficient and fair algorithm for power capping in many-core systems

Future servers will incorporate many active low-power modes for different system components, such as cores and memory. Though these modes provide flexibility for power management via Dynamic Voltage and Frequency Scaling (DVFS), they must be operated in a coordinated manner. Such coordinated control creates a combinatorial space of possible power mode configurations. Given the rapid growth of the number of cores, it is becoming increasingly challenging to quickly select the configuration that maximizes the performance under a given power budget. Prior power capping techniques do not scale well to large numbers of cores, and none of those works has considered memory DVFS. In this paper, we present FastCap, our optimization approach for system-wide power capping, using both CPU and memory DVFS. Based on a queuing model, FastCap formulates power capping as a non-linear optimization problem where we seek to maximize the system performance under a power budget, while promoting fairness across applications. Our FastCap algorithm solves the optimization online and efficiently (low complexity on the number of cores), using a small set of performance counters as input. To evaluate FastCap, we simulate it for a many-core server running different types of workloads. Our results show that FastCap caps power draw accurately, while producing better application performance and fairness than many existing CPU power capping methods (even after they are extended to use of memory DVFS as well).