Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous compact model for MuGFETs simulations

This work presents an analytical continuous compact model for FinFETs which is based on the doped Symmetrical Double Gate model. Our model covers a wide range of technological parameters including different doping concentrations from 1×10<sup>14</sup> to 3×10<sup>18</sup> cm<sup>−3</sup>, short channel effects (down to 80 nm) and high temperatures (up to 200°C). Recently, we have also implemented and validated it in a Verilog-A module. Good agreements between device measurements and simulations have been obtained in all operation regions and at different temperatures.
Distributed Diagnosis of Faults in Circuits and Biological Systems

Model-based diagnosis aims at inferring abnormalities of components in a system, given its behavior. One specific goal is to determine the minimal set of candidates, which is useful e.g. to efficiently perform further measurements. A key difference between the centralized and the distributed versions of this problem is that the former uses knowledge of the designer. In the latter case we cannot count on any agent knowing all inputs and outputs of the system. Distributed modelling means that only local knowledge is available to the agents. This paper focuses on distributed troubleshooting of model based devices in which outputs are produced from given inputs. The consequences of distributed representation of the knowledge and of distributed reasoning
Draft Genome Sequence of a Psychrotolerant Sulfur-Oxidizing Bacterium, Sulfuricella denitrificans skB26, and Proteomic Insights into Cold Adaptation

ABSTRACT Except for several conspicuous cases, very little is known about sulfur oxidizers living in natural freshwater environments. Sulfuricella denitrificans skB26 is a psychrotolerant sulfur oxidizer recently isolated from a freshwater lake as a representative of a new genus in the class Betaproteobacteria. In this study, an approximately 3.2-Mb draft genome sequence of strain skB26 was obtained. In the draft genome, consisting of 23 contigs, a single rRNA operon, 43 tRNA genes, and 3,133 coding sequences were identified. The identified genes include those required for sulfur oxidation, denitrification, and carbon fixation. Comparative proteomic analysis was conducted to assess cold adaptation mechanisms of this organism. From cells grown at 22°C and 5°C, proteins were extracted for analysis by nano-liquid chromatography–electrospray ionization–tandem mass spectrometry. In the cells cultured at 5°C, relative abundances of ribosomal proteins, cold shock proteins, and DEAD/DEAH box RNA helicases were increased in comparison to those at 22°C. These results suggest that maintenance of proper translation is critical for growth under low-temperature conditions, similar to the case for other cold-adapted prokaryotes.
Bolus injection of aqueous antigen leads to a high density of T‐cell‐receptor ligand in the spleen, transient T‐cell activation and anergy induction

In vivo anergy can be modelled by administration of soluble peptide to T‐cell receptor (TCR) transgenic mice specific for the moth cytochrome c peptide 88–103 (MCCp). Two weeks after initial peptide treatment, T cells were present in normal numbers but were unresponsive to antigen stimulation in vitro. Only bolus injections of peptide, either subcutaneous or intravenous, were effective at inducing tolerance, while slowly released antigen administered via mini‐osmotic pump failed to result in anergy. Examination of T cells soon after bolus peptide administration revealed that anergy induction was preceded by a transient hyperactivation of T cells in vivo. Within 2 hr of peptide treatment, interleukin‐2 was detectable in the plasma of the transgenic mice. Interestingly, only bolus injections of peptide led to high levels of T‐cell activation, while adjuvant emulsified and pump‐administered peptide resulted in very low stimulation in vivo. When the dose of bolus‐injected peptide used for tolerization was titrated, the extent of anergy induction directly correlated with the intensity of early T‐cell activation. Indirect measurements of TCR‐ligand density on the surface of antigen‐presenting cells following peptide administration revealed that aqueous peptide delivered via bolus injection generated a large number of major histocompatibility complex–peptide complexes, while pump‐delivered and adjuvant‐emulsified peptide did not. These data suggest that high levels of TCR ligand are required for in vivo T‐cell hyperactivation and induction of anergy.
Unsupervised Word Segmentation Improves Dialectal Arabic to English Machine Translation

We demonstrate the feasibility of using unsupervised morphological segmentation for dialects of Arabic, which are poor in linguistics resources. Our experiments using a Qatari Arabic to English machine translation system show that unsupervised segmentation helps to improve the translation quality as compared to using no segmentation or to using ATB segmentation, which was especially designed for Modern Standard Arabic (MSA). We use MSA and other dialects to improve Qatari Arabic to English machine translation, and we show that a uniform segmentation scheme across them yields an improvement of 1.5 BLEU points over using no segmentation.
A Laboratory Environment for Experimenting with Xinu

1Welve years ago, the Compuler Science Department eslablished the Xinu laboratory, which is currenl1y used for education as well as operating systems research. During the past year, facilities in the lab were repIaced by Intel Pentium computers. and lhe support system was redesigned to accommodate the new hardware. This paper describes the new hardware and software used in lhe lab. It shows how computers are connected to networks, and explains how lite support software permits users to cooperate in using the new hardware.
The Affective Tipping Point: Do Motivated Reasoners Ever “Get It”?

In order to update candidate evaluations voters must acquire information and determine whether that new information supports or opposes their candidate expectations. Normatively, new negative information about a preferred candidate should result in a downward adjustment of an existing evaluation. However, recent studies show exactly the opposite; voters become more supportive of a preferred candidate in the face of negatively valenced information. Motivated reasoning is advanced as the explanation, arguing that people are psychologically motivated to maintain and support existing evaluations. Yet it seems unlikely that voters do this ad infinitum. To do so would suggest continued motivated reasoning even in the face of extensive disconfirming information. In this study we consider whether motivated reasoning processes can be overcome simply by continuing to encounter information incongruent with expectations. If so, voters must reach a tipping point after which they begin more accurately updating their evaluations. We show experimental evidence that such an affective tipping point does in fact exist. We also show that as this tipping point is reached, anxiety increases, suggesting that the mechanism that generates the tipping point and leads to more accurate updating may be related to the theory of affective intelligence. The existence of a tipping point suggests that voters are not immune to disconfirming information after all, even when initially acting as motivated reasoners.
Molecular analysis of Hodgkin's disease‐derived cell lines

Three Hodgkin's disease‐derived cell lines were analysed for the organization of immunoglobulin and T cell receptor genes, for the expression of the interleukin 2 (IL‐2) receptor gene, and for the cellular oncogene c‐myc. All three cell lines have characteristic genotypic markers of lymphoid cells and can be classified as immature lymphoid cells with respect to the incomplete expression of their antigen receptor genes. This immature genotype is in contrast to the expression of activation antigens Ki‐1 (CD 30), IL‐2 receptor (CD 25), and HLA‐DR. The results obtained are in agreement with studies obtained from primary Hodgkin's lymphomas, which are activated by as yet unknown mechanisms.
The discriminative functions of primary and conditional reinforcers : signalling the local and global contingencies of reinforcement

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Global EDF-Based Scheduling with Efficient Priority Promotion

This paper presents an algorithm, called Earliest Deadline Critical Laxity (EDCL), for the efficient scheduling of sporadic real-time tasks on multiprocessors systems. EDCL is a derivative of the Earliest Deadline Zero Laxity (EDZL) algorithm in that the priority of a job reaching certain laxity is imperiously promoted to the top, but it differs in that the occurrence of priority promotion is confined to at the release time or the completion time of a job. This modification enables EDCL to bound the number of scheduler invocations and to relax the implementation complexity of scheduler, while the schedulability is still competitive with EDZL. The schedulability test of EDCL is designed through theoretical analysis. In addition, an error in the traditional schedulability test of EDZL is corrected. Simulation studies demonstrate the effectiveness of EDCL in terms of guaranteed schedulability and exhaustive schedulability by comparing with traditional efficient scheduling algorithms.
Constrained CP-nets

We present a novel approach to deal with preferences expressed as a mixture of hard constraints, soft constraints, and CP nets. We construct a set of hard constraints whose solutions are the optimal solutions of the set of preferences. This allows us to avoid dominance testing (is one outcome better than another? ) which is a very expensive operation often used when finding optimal solutions or testing optimality. We also show how hard constraints can eliminate cycles in the preference ordering. Finally, we extend this approach to deal with the preferences of multiple agents. This simple and elegant technique permits conventional constraint and SAT solvers to solve problems involving both preferences and constraints.
Time Encoding of Bandlimited Signals, an Overview ∗

Time encoding is a real-time, asynchronous mechanism for encoding the amplitude information of an analog bandlimited signal into a time sequence, or time codes, based on which the signal can be reconstructed. Time codes can be generated by simple nonlinear asynchronous analog circuits with low power consumption. The circuits generating the time codes and the procedures carrying out the reconstruction are referred to as Time Encoding Machines (TEMs) and Time Decoding Machines (TDMs), respectively. We give an overview of this novel information representation modality.
A review and meta-analysis of the genetic epidemiology of anxiety disorders.

OBJECTIVE The authors conducted meta-analyses of data from family and twin studies of panic disorder, generalized anxiety disorder, phobias, and obsessive-compulsive disorder (OCD) to explore the roles of genetic and environmental factors in their etiology. METHOD MEDLINE searches were performed to identify potential primary studies of these disorders. Data from studies that met inclusion criteria were incorporated into meta-analyses that estimated summary statistics of aggregate familial risk and heritability for each disorder. RESULTS For family studies, odds ratios predicting association of illness in first-degree relatives with affection status of the proband (disorder present or absent) were homogeneous across studies for all disorders. The calculated summary odds ratios ranged from 4 to 6, depending on the disorder. Only for panic disorder and generalized anxiety disorder could the authors identify more than one large-scale twin study for meta-analysis. These yielded heritabilities of 0.43 for panic disorder and 0.32 for generalized anxiety disorder. For panic disorder, the remaining variance in liability could be attributed primarily to nonshared environment. For generalized anxiety disorder, this was true for men, but for women, a potentially significant role for common familial environment was also seen. CONCLUSIONS Panic disorder, generalized anxiety disorder, phobias, and OCD all have significant familial aggregation. For panic disorder, generalized anxiety disorder, and probably phobias, genes largely explain this familial aggregation; the role of family environment in generalized anxiety disorder is uncertain. The role of nonshared environmental experience is significant, underscoring the importance of identifying putative environmental risk factors that predispose individuals to anxiety.
Perception of Mandarin Lexical Tones when F0 Information is Neutralized

In tone languages, the identity of a word depends on its tone pattern as well as its phonetic structure. The primary cue to tone identity is the fundamental frequency (F0) contour. Two experiments explore how listeners perceive Mandarin monosyllables in which all or part of the F0 information has been neutralized. In Experiment 1, supposedly critical portions of the tonal pattern were neutralized with signal processing techniques, yet identification of the tonal pattern remained quite good. In Experiment 2, even more drastic removal of tonal information was tested, using stimuli whispered by Mandarin speakers, or signal processed to remove the pitch cues. Again, performance was surprisingly good, showing that listeners can use secondary cues when the primary cue is unavailable. Moreover, a comparison of tone perception of naturally whispered monosyllables and the signal processed ones suggests that Mandarin speakers promote the utility of secondary cues when they know that the primary cue will be unavailable. The flexible use of cues to tone in Mandarin is similar to the flexibility that has been found in the production and perception of cues to phonetic identity in Western languages.
Reducing Spreadsheet Errors

Abstract : Increasing computational power and memory capacity in desktop computers has resulted in access to numerical techniques previously unavailable to many users. Although computational capabilities have increased, many users are unable to take full advantage of this power due to limited programming expertise. Spreadsheets offer these users a means to capitalize upon computational potential without extensive training, though spreadsheet models can confer risks due to high error rates and lack of quality control. Table 1 presents several strengths and weaknesses associated with using spreadsheets as numerical tools for the masses.
Low complexity k-best based iterative receiver for MIMO systems

Iterative receiver combining multiple-input multiple-output (MIMO) detection with channel decoding is an attractive solution to greatly improve the error rate performance of wireless communication systems. However, such iterative process poses significant challenges for efficient receiver design. In this paper, a modification of soft-input soft-output K-Best decoder is proposed to provide high throughput with low complexity. Moreover, the convergence of combining sphere decoder based detection algorithms with LTE turbo decoder is analyzed with the help of EXtrinsic Information Transfer (EXIT) charts. An optimized scheduling of the number of iterations is therefore proposed. Simulation results show that the proposed decoder and the new schedule achieve almost the same performance compared to the conventional schedule with a large saving in computational complexity.
A Colour Palette for Automatic Detection of Blue-White Veil

Colour assessment of pigmented skin lesions are essential for the diagnosis of malignant melanoma. However, visual interpretation of colour is subjective and prone to error. Computer programs can provide support to clinicians to overcome this subjectivity. So far, methods for colour analysis of this nature have utilised statistical classification models. This paper puts forward an alternative framework: an effort to reproduce the experience of human observer. The proposed method introduces a perceptually intuitive and semantically meaningful approach for colour and colour-related feature detection. As a case study, the task of automatic detection and segmentation of blue-white veil feature in dermoscopy images is examined. Our proposed method, as shown in our experiments, outperforms the prior art for this task, while it attempts to mimic the human perception of skin lesion colours.
Learning what to observe in multi-agent systems

A major challenge in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic multi-agent systems. As the state space grows, agent policies become more and more complex and learning slows down. Current more advanced single-agent techniques are already very capable of learning optimal policies in large unknown environments. When multiple agents are present however, we are challenged by an increase of the state-action space, exponential in the number of agents, even though these agents do not always interfere with each other and thus their presence should not always be included in the state information of the other agent. We introduce a framework capable of dealing with this issue. We also present an implementation of our framework, called 2observe which we apply to some gridworld problems.
A comprehensive XQuery to SQL translation using dynamic interval encoding

The W3C XQuery language recommendation, based on a hierarchical and ordered document model, supports a wide variety of constructs and use cases. There is a diversity of approaches and strategies for evaluating XQuery expressions, in many cases only dealing with limited subsets of the language. In this paper we describe an implementation approach that handles XQuery with arbitrarily-nested FLWR expressions, element constructors and built-in functions (including structural comparisons). Our proposal maps an XQuery expression to a single equivalent SQL query using a novel dynamic interval encoding of a collection of XML documents as relations, augmented with information tied to the query evaluation environment. The dynamic interval technique enables (suitably enhanced) relational engines to produce predictably good query plans that do not preclude the use of sort-merge join query operators. The benefits are realized despite the challenges presented by intermediate results that create arbitrary documents and the need to preserve document order as prescribed by semantics of XQuery. Finally, our experimental results demonstrate that (native or relational) XML systems can benefit from the above technique to avoid a quadratic scale up penalty that effectively prevents the evaluation of nested FLWR expressions for large documents.
Time-constrained code compaction for DSPs

DSP algorithms are, in most cases, subject to hard real-time constraints. In the case of programmable DSPs, meeting those constraints must be ensured by appropriate code generation techniques. For processors offering instruction-level parallelism, the task of code generation includes code compaction. The exact timing behavior of a DSP program is only known after compaction. Therefore, real-time constraints should be taken into account during the compaction phase. While most known DSP code generators rely on rigid heuristics for that phase, this paper proposes a novel approach to local code compaction based on an integer programming model, which obeys exact timing constraints. Due to a general problem formulation, the model also obeys encoding restrictions and possible side-effects.
Current instabilities in GaN-based devices

Current dispersion effects have been experimentally investigated in a variety of AlGaN/GaN heterostructure FETs with large signal and switching measurements including HEMTs with doped and undoped barrier layer. A range of dispersion frequencies from 10/sup -3/ Hz to 10 GHz were observed, where the output current amplitude is drastically reduced. Through this effect the full channel charge of an AlGaN/GaN heterostructure FET may be completely depleted under specific bias conditions. This indicates that this phenomena cannot be related to deep traps alone, but is also connected to piezorelated charge states and conduction to these states.
There is no prime for time: the missing link between form and concept of progressive aspect in L2 production

The acquisition of linguistic structures that require perspective-taking at the level of message generation is challenging. We investigate use of progressive aspect in L2 event encoding, using a sentence priming paradigm. We focus on Dutch, in which use of progressive aspect is optional. The progressive consists of a prepositional phrase (‘aan het,’ at-the), plus a verbal infinitive. We ask, to what extent L2 speakers, in comparison to native speakers, show priming effects in relation to form (prepositional phrase) or conceptual (progressive aspect) prime sentences. In native Dutch speakers we find a priming effect for the ‘progressive prime,’ compared to a ‘neutral prime’ (aspectually neutral event description). In L2 speakers this effect was absent. For the form prime, no priming effects were obtained in native speakers, rather, we find evidence for a partial blocking effect in L2 speakers. Results suggest that the strength of the link between concept and form of progressive aspect differs in native and L2 speakers. Specific factors contributed to the L2 findings, e.g., level of L2 proficiency and degree of L2 exposure. We conclude that (1) the conceptual basis of grammatical aspect can be primed in native speakers, and (2) in L2 speakers, access to conceptual information is less automatized.
Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.