Miracle-dz/UniFAR-FTUs huggingface dataset:
A wide input voltage range level shifter circuit for extremely low-voltage digital LSIs

In this paper, we propose a level shifter circuit capable of handling a wide input voltage range. The circuit is based on a conventional two-stage comparator, and has a distinctive feature in that it operates a current amplification scheme for ultra low-power operation. The proposed circuit can convert low-voltage input digital signals into high-voltage output digital signals. The circuit achieves low power dissipation because it dissipates operating current only when the input signals change. Measurement results demonstrated that the circuit can convert low voltage input signals of 0.4V into 3V output signals. The power dissipation was 0. 15µW at 0.4-V and 10-kHz input pulse.
Stat5 is indispensable for the maintenance of bcr/abl-positive leukaemia

Tumourigenesis caused by the Bcr/Abl oncoprotein is a multi‐step process proceeding from initial to tumour‐maintaining events and finally results in a complex tumour‐supporting network. A key to successful cancer therapy is the identification of critical functional nodes in an oncogenic network required for disease maintenance. So far, the transcription factors Stat3 and Stat5a/b have been implicated in bcr/abl‐induced initial transformation. However, to qualify as a potential drug target, a signalling pathway must be required for the maintenance of the leukaemic state. Data on the roles of Stat3 or Stat5a/b in leukaemia maintenance are elusive. Here, we show that both, Stat3 and Stat5 are necessary for initial transformation. However, Stat5‐ but not Stat3‐deletion induces G0/G1 cell cycle arrest and apoptosis of imatinib‐sensitive and imatinib‐resistant stable leukaemic cells in vitro. Accordingly, Stat5‐abrogation led to effective elimination of myeloid and lymphoid leukaemia maintenance in vivo. Hence, we identified Stat5 as a vulnerable point in the oncogenic network downstream of Bcr/Abl representing a case of non‐oncogene addiction (NOA).
Flavour development of East Midlands cheeses and evaluation of flavour producing microorganisms in a small scale real-cheese model

Stilton is a blue-veined cheese made from pasteurised milk. The diversity of the microflora found within the cheese helps develop the unique flavour and aroma of Stilton compared to other blue cheese. However, this flora is not controlled and so product may be variable. A small-scale cheese model was developed to allow examination of the effect of different microflora on flavour production in a controlled way. Texture analysis, water activity and viable count of the cheese models were compared to commercial cheeses in order to optimise the model. The final model had the hardness of Danish Blue and the water activity of Stilton. The growth of lactic acid bacteria through the cheese making process was monitored and showed a characteristic reduction after salting. Changes in total aerobic count and yeast and moulds after salt addition and handling showed the ease with which the cheese can become contaminated with external flora such as yeasts and moulds even under aseptically manipulated conditions. Solid Phase Microextraction Gas Chromatography Mass-Spectrometry was used to evaluate the flavour volatiles produced when milk and cheese were inoculated with different microorganisms. Some yeast species such as Trichosporon beigleii inhibited the metabolism of P. roqueforti within the models, where as others such as Debaryomyces hansenii aided the metabolism of compounds in the models to develop different flavour volatiles. Overall a small-scale blue cheese model was produced and can be used to understand the role of different organisms in the development of the flavour and aroma of blue cheeses in the future.
BY COMBINING DOA CLUSTERING AND SPEAKER IDENTIFICATION

This paper presents a meeting diarization system that estimates who spoke when in a meeting, especially if there is a speaker position change. Our previous system utilized solely the direction of arrival (DOA) information for meeting diarization. Therefore, when someone moves from one place to another, our previous system mistakes the utterances from second place for another person’s utterances. In order to handle such a speaker position change in a meeting, this paper tries to combine DOA information and Gaussian mixture model (GMM)-based speaker identification (SI). First, relying on the DOA information, we cluster the recorded meeting into segments. Several segments coming from the same DOA are utilized to construct GMMs for the Mel frequency cepstral coefficients (MFCC) for each speaker. The diarization result is obtained by evaluating every segment clustered by DOA against all speaker models. We obtained encouraging results for simulated meetings with a measured room impulse response and a recorded meeting, where the reverberation time of the room was about 350 ms.
Singularity-Tolerant Inverse Kinematics for Bipedal Robots: An Efficient Use of Computational Power to Reduce Energy Consumption

We propose a nonlinear inverse kinematics formulation which solves for positions directly. Compared to various other popular methods that integrate velocities, this formulation can better handle fast, asymmetric, and singular-postured balancing tasks for humanoid robots. We also introduce joint position and velocity boundaries as inequality constraints in the optimization to ensure feasibility. Such boundaries provide safety when approaching or getting away from joint limits or singularities. Besides, mixing positions and velocities in our proposed algorithm facilitates recovery from singularities, which is very difficult for conventional inverse kinematics methods. Extensive demonstrations on the real robot prove the applicability of the proposed algorithm while improving power consumption. Our formulation automatically handles different numerical and behavioral difficulties rising from singularities, which makes it a reliable low-level conversion block for different Cartesian planners.
Imaging granularity of leukocytes with third harmonic generation microscopy

Using third harmonic generation (THG) microscopy, we demonstrate that granularity differences of leukocytes can be revealed without a label. Excited by a 1230 nm femtosecond laser, THG signals were generated at a significantly higher level in neutrophils than other mononuclear cells, whereas signals in agranular lymphocytes were one order of magnitude smaller. Interestingly, the characteristic THG features can also be observed in vivo to track the newly recruited leukocytes following lipopolysaccharide (LPS) challenge. These results suggest that label-free THG imaging may provide timely tracking of leukocyte movement without disturbing the normal cellular or physiological status.
Human Pose Estimation Using a Joint Pixel-wise and Part-wise Formulation

Our goal is to detect humans and estimate their 2D pose in single images. In particular, handling cases of partial visibility where some limbs may be occluded or one person is partially occluding another. Two standard, but disparate, approaches have developed in the field: the first is the part based approach for layout type problems, involving optimising an articulated pictorial structure, the second is the pixel based approach for image labelling involving optimising a random field graph defined on the image. Our novel contribution is a formulation for pose estimation which combines these two models in a principled way in one optimisation problem and thereby inherits the advantages of both of them. Inference on this joint model finds the set of instances of persons in an image, the location of their joints, and a pixel-wise body part labelling. We achieve near or state of the art results on standard human pose data sets, and demonstrate the correct estimation for cases of self-occlusion, person overlap and image truncation.
Link quality and path based clustering in IEEE 802.15.4-2015 TSCH networks

Advance clustering techniques have been widely used in Wireless Sensor Networks (WSNs) since they can potentially reduce latency, improve scheduling, decrease end-to-end delay and optimise energy consumption within a dense network topology. In this paper, we present a novel clustering algorithm for high density IEEE 802.15. 4-2015 Time-Slotted Channel Hopping (TSCH). In particular, the proposed methodology merges a variety of solutions into an integrated clustering design. Assuming an homogeneous network distribution, the proposed configuration deploys a hierarchical down-top approach of equally numbered sub-groups, in which the formation of the separate sub-groups is adapted to the network density and the node selection metric is based on the link quality indicator. The presented algorithm is implemented in Contiki Operating System (OS) and several test vectors have been designed in order to evaluate the performance of the proposed algorithm in a COOJA simulation environment. Performance results demonstrate the capability of the clustering structure since compared to the default scheme it significantly improves the energy efficiency up to 35%, packet drops more than 40% as well the packet retransmission rate. Last but not least, the outcome of this study indicates a major increase in the network lifetime, i.e., up to 50%.
Playing audiogames without instructions for use? To do without an instruction leaflet or without language itself

Audiogames, which are supposed to be accessible to the blind, have two problems related to language to be understood by most of their users : the first one is linked to an instruction reading phase, which is often essential, and the pleasure of which is often very different from the pleasure of playing the game. The second problem is even more bothersome: the major part of candidates for playing do not understand the language used by those games. In games, we can discern two approaches to learning how interactivity works: the instructions for use and the interactivity itself. So, if the purpose of a game is to learn in a friendly but challenging way how interactivity can became complex, why not first start this process from the very beginning, without the need of textual instructions ? We have therefore designed a audiogame in two versions: one with the instructions incorporated in the game, so that it is possible to play without an instruction leaflet, another without any language at all. Through the analysis of case studies in which players test those games, the version with linguistic instructions incorporated in the game appears more efficient for understanding the game and the pleasure of playing seems good. The version without linguistic instructions was properly used by two players out of five. These results, though still insufficient to prompt us to do without any language at all, encourage the improvement of the game design and sound design principles allowing a greater internationality of audiogames.
Rho exchange factor ECT2 is induced by growth factors and regulates cytokinesis through the N‐terminal cell cycle regulator‐related domains

The ECT2 protooncogene plays a critical role in cytokinesis, and its C‐terminal half encodes a Dbl homology‐pleckstrin homology module, which catalyzes guanine nucleotide exchange on the Rho family of small GTPases. The N‐terminal half of ECT2 (ECT2‐N) contains domains related to the cell cycle regulator/checkpoint control proteins including human XRCC1, budding yeast CLB6, and fission yeast Cut5. The Cut5‐related domain consists of two BRCT repeats, which are widespread to repair/checkpoint control proteins. ECT2 is ubiquitously expressed in various tissues and cell lines, but elevated levels of ECT2 expression were found in various tumor cell lines and rapidly developing tissues in mouse embryos. Consistent with these findings, induction of ECT2 expression was observed upon stimulation by serum or various growth factors. In contrast to other oncogenes whose expression is induced early in G1, ECT2 expression was induced later, coinciding with the initiation of DNA synthesis. To test the role of the cell cycle regulator/checkpoint control protein‐related domains of ECT2 in cytokinesis, we expressed various ECT2 derivatives in U2OS cells, and analyzed their DNA content by flow cytometry. Expression of the N‐terminal half of ECT2, which lacks the catalytic domain, generated cells with more than 4N DNA content, suggesting that cytokinesis was inhibited in these cells. Interestingly, ECT2‐N lacking the nuclear localization signals inhibited cytokinesis more strongly than the derivatives containing these signals. Mutational analyses revealed that the XRCC1, CLB6, and BRCT domains in ECT2‐N are all essential for the cytokinesis inhibition by ECT2‐N. These results suggest that the XRCC1, CLB6, and BRCT domains of ECT2 play a critical role in regulating cytokinesis. Published 2003 Wiley‐Liss, Inc.
Simulation-Based Valuation of Project Finance - Does Model Complexity Really Matter?

This paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice. First, we present a simulation-based project finance valuation model. Second, we vary several model aspects in order to analyze their impact on the valuation result. For forecast complexity, we apply different volatility and correlation forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model. Regarding simulation complexity, the number of Monte Carlo iterations, the equity valuation method, and the time resolution are varied. We find that the applied volatility forecasting models have a strong influence on the expected net present value distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and equity valuation are both crucial when specifying a valuation model for project finance. For the number of Monte Carlo iterations, we demonstrate that 100,000 iterations are sufficient to obtain reliable results.
Experimental Implementation of Non-binary Cyclic ADCs with Radix Value Estimation Algorithm

Proof-of-concept cyclic analog-to-digital converters (ADCs) have been designed and fabricated in 90-nm CMOS technology. The measurement results of an experimental prototype demonstrate the effectiveness of the proposed switched-capacitor (SC) architecture to realize a non-binary ADC based on β expansion. Different from the conventional binary ADC, a simple 1-bit/step structure for an SC multiplying digital-toanalog converter (MDAC) is proposed to present residue amplification by β (1 < β < 2). The redundancy of non-binary ADCs with radix β tolerates the non-linear conversion errors caused by the offsets of comparators, the mismatches of capacitors, and the finite DC gains of amplifiers, which are used in the MDAC. We also employed a radix value estimation algorithm to obtain an effective value of β for non-binary encoding; it can be realized by merely adding a simple conversion sequence and digital circuits. As a result, the power penalty of a high-gain wideband amplifier and the required accuracy of the circuit elements for a high-resolution ADC were largely relaxed so that the circuit design was greatly simplified. The implemented ADC achieves a measured peak signal-to-noise-and-distortionratio (SNDR) of 60.44 dB, even with an op-amp with a poor DC gain (< 50 dB) while dissipating 780 μW in analog circuits at 1.4 V and occupying an active area of 0.25 × 0.26 mm2. key words: Non-binary ADC, Cyclic ADC, Radix value estimation algorithm, β expansion, Multiply-by-β MDAC
Semantic Integration in Learning from Text

We define learning as the generation of meaningful knowledge representations which can be utilized in future decision making. Optimal learning entails that these knowledge representations be integrated with prior knowledge. In this paper, we introduce a knowledge representation based on an integration of a variety of shallow semantic parsing techniques. Entity detection, event detection, semantic role labeling and temporal relation identification are combined to produce graph-like structures which represent the most important semantic components of a text and the relations between these components. We show how new entities, events and relations can be successfully integrated into this representation using features derived from lexical and dependency-based sources.
Bit weaving: A non-prefix approach to compressing packet classifiers in TCAMs

Ternary Content Addressable Memories (TCAMs) have become the de facto standard in industry for fast packet classification. Unfortunately, TCAMs have limitations of small capacity, high power consumption, high heat generation, and high cost. The well-known range expansion problem exacerbates these limitations as each classifier rule typically has to be converted to multiple TCAM rules. One method for coping with these limitations is to use compression schemes to reduce the number of TCAM rules required to represent a classifier. Unfortunately, all existing compression schemes only produce prefix classifiers. Thus, they all miss the compression opportunities created by non-prefix ternary classifiers.
CD10, BCL6, and MUM1 expression in diffuse large B‐cell lymphoma on FNA samples

Gene expression profiling has divided diffuse large B‐cell lymphoma (DLBCL) into 2 main subgroups: germinal center B (GCB) and non‐GCB type. This classification is reproducible by immunohistochemistry using specific antibodies such as CD10, B‐cell lymphoma 6 (BCL6), and multiple myeloma oncogene 1 (MUM1). Fine‐needle aspiration (FNA) plays an important role in the diagnosis of non‐Hodgkin lymphoma, and in some cases FNA may be the only available pathological specimen. The objectives of the current study were to evaluate CD10, BCL6, and MUM1 immunostaining on FNA samples by testing the CD10, BCL6, and MUM1 algorithm on both FNA cell blocks (CB) and conventional smears (CS), evaluating differences in CB and CS immunocytochemical (ICC) performance, and comparing results with histological data.
Survey Paper Analysis On Deblur Image Using Various Technique Method

The image processing is an important field of research in which we can get the complete information about any image. One of the main problems in this research field is the quality of an image. So the aim of this paper is to propose an algorithm for improving the quality of an image by removing blur, which is an image blur. Review of different deblurring techniques is obtained for a good quality image. The deblurring techniques are basically used to sharp an image using different methods & parameters. Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far.
Iterative optimization in the polyhedral model: part ii, multidimensional time

High-level loop optimizations are necessary to achieve good performance over a wide variety of processors. Their performance impact can be significant because they involve in-depth program transformations that aim to sustain a balanced workload over the computational, storage, and communication resources of the target architecture. Therefore, it is mandatory that the compiler accurately models the target architecture as well as the effects of complex code restructuring. However, because optimizing compilers (1) use simplistic performance models that abstract away many of the complexities of modern architectures, (2) rely on inaccurate dependence analysis, and (3) lack frameworks to express complex interactions of transformation sequences, they typically uncover only a fraction of the peak performance available on many applications. We propose a complete iterative framework to address these issues. We rely on the polyhedral model to construct and traverse a large and expressive search space. This space encompasses only legal, distinct versions resulting from the restructuring of any static control loop nest. We first propose a feedback-driven iterative heuristic tailored to the search space properties of the polyhedral model. Though, it quickly converges to good solutions for small kernels, larger benchmarks containing higher dimensional spaces are more challenging and our heuristic misses opportunities for significant performance improvement. Thus, we introduce the use of a genetic algorithm with specialized operators that leverage the polyhedral representation of program dependences. We provide experimental evidence that the genetic algorithm effectively traverses huge optimization spaces, achieving good performance improvements on large loop nests.
Content Distribution Networks Using Software Defined Networks

With exponential growth of the Internet traffic, Internet Service Providers (ISPs) have started to look for alternatives to meet the Service Level Agreements (SLAs) that have been promised to the customers. ISPs and Content Delivery Networks (CDNs) when merged together can deliver the content much faster and efficient by making the best use of the resources and also reduces the traffic within the network. Implementing a CDN on a Software Defined Network (SDN) provides better control and granularity over the type of content that each server holds or distributes. A centralized approach on top decoupled control plane and data plane reduces overhead, better user experience and cost by using CDN. SDN also provides better user experiences, and a dramatic decrease in network systems and equipment costs. Following the advantages of SDN architecture, we propose the CDN-SDN architecture and discuss the logic behind SDN Server/Controller and its functions needed to achieve the SDN-based CDN approach.
DSM-III-R disorders in preschool children from low-income families.

OBJECTIVE To generate rates of DSM-III-R disorders in a sample of preschool children from low-income families, examine impairment ratings for preschool children meeting criteria for DSM-III-R disorders, and compare rates of psychopathology using DSM-III-R criteria with rates generated by the Child Behavior Checklist (CBCL). METHOD As part of an ongoing longitudinal study of 104 mother-child dyads from low-income families, data were gathered when children were 5 years of age. DSM-III-R disorders were diagnosed through the administration of the Schedule for Affective Disorders and Schizophrenia for School-Age Children to the mothers, and mothers competed the CBCL. RESULTS Rates of DSM-III-R disorders among preschool children from low-income families were higher than those reported in community samples, but comparable with rates for low-income school-age children and adolescents. Children meeting criteria for DSM-III-R disorders were rated as significantly impaired. The prevalence of behavioral and emotional problems was similar by both DSM-III-R criteria and the CBCL. There was some evidence, however, that the two systems identified different children. CONCLUSIONS DSM-III-R criteria appear to adequately identify preschool children with serious behavioral and emotional problems. Longitudinal studies are needed to explore further the relative utility of the DSM and CBCL in the identification of psychopathology in preschool children.