Miracle-dz/UniFAR-FTUs huggingface dataset:
Face/Off: live facial puppetry

We present a complete integrated system for live facial puppetry that enables high-resolution real-time facial expression tracking with transfer to another person's face. The system utilizes a real-time structured light scanner that provides dense 3D data and texture. A generic template mesh, fitted to a rigid reconstruction of the actor's face, is tracked offline in a training stage through a set of expression sequences. These sequences are used to build a person-specific linear face model that is subsequently used for online face tracking and expression transfer. Even with just a single rigid pose of the target face, convincing real-time facial animations are achievable. The actor becomes a puppeteer with complete and accurate control over a digital face.
Improved timing closure by analytical buffer and TSV planning in three-dimensional chips

: In this paper, a mathematical solution for integrated buﬀer and Through-Silicon Via (TSV) planning in three-dimensional chips is presented in which the optimal location of both buﬀer and TSV of each net is determined simultaneously. In this method, two-dimensional buﬀer planning formulation is extended to three-dimensional era. Experimental results show that performance and probability of successful buﬀer/TSV insertion is increased considerably, especially for large and congested three-dimensional circuits.
High Mannose-binding Lectin with Preference for the Cluster of α1–2-Mannose from the Green Alga Boodlea coacta Is a Potent Entry Inhibitor of HIV-1 and Influenza Viruses*

The complete amino acid sequence of a lectin from the green alga Boodlea coacta (BCA), which was determined by a combination of Edman degradation of its peptide fragments and cDNA cloning, revealed the following: 1) B. coacta used a noncanonical genetic code (where TAA and TAG codons encode glutamine rather than a translation termination), and 2) BCA consisted of three internal tandem-repeated domains, each of which contains the sequence motif similar to the carbohydrate-binding site of Galanthus nivalis agglutinin-related lectins. Carbohydrate binding specificity of BCA was examined by a centrifugal ultrafiltration-HPLC assay using 42 pyridylaminated oligosaccharides. BCA bound to high mannose-type N-glycans but not to the complex-type, hybrid-type core structure of N-glycans or oligosaccharides from glycolipids. This lectin had exclusive specificity for α1–2-linked mannose at the nonreducing terminus. The binding activity was enhanced as the number of terminal α1–2-linked mannose substitutions increased. Mannobiose, mannotriose, and mannopentaose were incapable of binding to BCA. Thus, BCA preferentially recognized the nonreducing terminal α1–2-mannose cluster as a primary target. As predicted from carbohydrate-binding propensity, this lectin inhibited the HIV-1 entry into the host cells at a half-maximal effective concentration of 8.2 nm. A high association constant (3.71 × 108 m−1) of BCA with the HIV envelope glycoprotein gp120 was demonstrated by surface plasmon resonance analysis. Moreover, BCA showed the potent anti-influenza activity by directly binding to viral envelope hemagglutinin against various strains, including a clinical isolate of pandemic H1N1-2009 virus, revealing its potential as an antiviral reagent.
An Approach to Simulate Shape Distortion due to Cooling in Aluminum Extrusion

Cooling subsequent to extrusion is a crucial process in aluminum extrusion value chain. Non-uniform cooling-induced shape distortion, such as deflection, twisting and etc., is a challenge for extrusion profile manufacturers. Temperature management is therefore a key to the aluminum extrusion process. Appropriate modeling, using both physical and numerical methods, can help us achieve a better temperature control in extrusion plants. In this work, finite element (FE) method was used to simulate shape distortion due to cooling and the most important challenge was to make FE models compatible to real conditions in plants. The effects of three important items I) mechanical boundary condition II) cooling source type and III) effective cooling length were examined. It was shown that for compatible prediction of distortions it was necessary to define these items similar to real life. It was also revealed that with a suitable definition of boundary conditions it is possible to use a short lab scale sample to understand mechanisms in real life profiles.
Integrating a Discriminative Classifier into Phrase-based and Hierarchical Decoding

Abstract Current state-of-the-art statistical machine translation (SMT) relies on simple feature functions which make independence assumptions at the level of phrases or hierarchical rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or hierarchical rule, which is available at decoding time. We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.
Enterprise Systems Implementation Success in the Shakedown Phase

Enterprise systems (ES) are popular in both business and academia: many organizations adopt an ES and much has been published on the topic. However, we argue not much is known about success (and its contributing factors) during the different moments of an ES implementation, although this is highly relevant for practice. This study considers the relevant success factors during the shakedown phase of an ES implementation. We use a framework that incorporates three dimensions of success: user, correspondence, and system success. Each success dimension has its own set of success factors. We empirically study an ES implementation in its shakedown phase at a large university in the Netherlands, through conducting interviews and a survey. Results show that data accuracy, troubleshooting, and user support were rated the most important for ES implementation success, whereas business process reengineering (BPR), customization, and again data accuracy were evaluated the best for this ES implementation.
Fast Filtering of Reflection Probes

Game and movie studios are switching to physically based rendering en masse, but physically accurate filter convolution is difficult to do quickly enough to update reflection probes in real‐time. Cubemap filtering has also become a bottleneck in the content processing pipeline. We have developed a two‐pass filtering algorithm that is specialized for isotropic reflection kernels, is several times faster than existing algorithms, and produces superior results. The first pass uses a quadratic b‐spline recurrence that is modified for cubemaps. The second pass uses lookup tables to determine optimal sampling in terms of placement, mipmap level, and coefficients. Filtering a full 1282 cubemap on an NVIDIA GeForce GTX 980 takes between 160 µs and 730 µs with out method, depending on the desired quality.
Improved adsorption-desorption extraction applied to the partial characterization of the antilisterial bacteriocin produced by Carnobacterium maltaromaticum C2

Bacteriocins are ribosomally produced peptides useful for food biopreservation. An improved adsorption-desorption process is proposed for the partial purification of the bacteriocin produced by the fish isolate Carnobacterium maltaromaticum C2. Analyzis of extract by SDS-PAGE indicated this method may offer an alternative to improve the yield of purification of bacteriocins.
High-Performance Remote Access to Climate Simulation Data: A Challenge Problem for Data Grid Technologies

In numerous scientific disciplines, terabyte and soon petabyte-scale data collections are emerging as critical community resources. A new class of Data Grid infrastructure is required to support management, transport, distributed access to, and analysis of these datasets by potentially thousands of users. Researchers who face this challenge include the Climate Modeling community, which performs long-duration computations accompanied by frequent output of very large files that must be further analyzed. We describe the Earth System Grid prototype, which brings together advanced analysis, replica management, data transfer, request management, and other technologies to support high-performance, interactive analysis of replicated data. We present performance results that demonstrate our ability to manage the location and movement of large datasets from the user’s desktop. We report on experiments conducted over SciNET at SC’2000, where we achieved peak performance of 1.55Gb/s and sustained performance of 512.9Mb/s for data transfers between Texas and California.
SUMOylation Regulates the Transcriptional Repression Activity of FOG-2 and Its Association with GATA-4

Friend of GATA 2 (FOG-2), a co-factor of several GATA transcription factors (GATA-4, -5 and 6), is a critical regulator of coronary vessel formation and heart morphogenesis. Here we demonstrate that FOG-2 is SUMOylated and that this modification modulates its transcriptional activity. FOG-2 SUMOylation occurs at four lysine residues (K312, 471, 915, 955). Three of these residues are part of the characteristic SUMO consensus site (ψKXE), while K955 is found in the less frequent TKXE motif. Absence of SUMOylation did not affect FOG-2′s nuclear localization. However, mutation of the FOG-2 SUMOylation sites, or de-SUMOylation, with SENP-1 or SENP-8 resulted in stronger transcriptional repression activity in both heterologous cells and cardiomyocytes. Conversely, increased FOG-2 SUMOylation by overexpression of SUMO-1 or expression of a SUMO-1-FOG-2 fusion protein rendered FOG-2 incapable of repressing GATA-4-mediated activation of the B-type natriuretic peptide (BNP) promoter. Moreover, we demonstrate both increased interaction between a FOG-2 SUMO mutant and GATA-4 and enhanced SUMOylation of wild-type FOG-2 by co-expression of GATA-4. These data suggest a new dynamics in which GATA-4 may alter the activity of FOG-2 by influencing its SUMOylation status.
An Energy-Aware Ant Colony Algorithm for Network-Aware Virtual Machine Placement in Cloud Computing

The energy cost is one of the major concerns for the cloud providers. Virtual machine placement has been demonstrated as an effective method for energy saving. In addition to constraints caused by the physical machine resources such as CPU and memory (PM-constraints), the constraints caused by the network resource such as bandwidth (Net-constraints) are also crucial, since virtual machines are not isolated and require communication with each other to exchange data. However, most current research on data center power optimization only focuses on server resource. As a result, the optimization results are often inferior, because server consolidation without considering the network may cause traffic congestion and thus degraded network performance. We take the traffic demands between virtual machines into consideration and formulate the virtual machine placement problem under both PM-constraints and Net-constraints to minimize the energy cost, and propose an approach based on ant colony optimization to solve the problem. We evaluate the expected performance of our proposed algorithm through a simulation study, providing strong indications to the superiority of our proposed solution.
Habitat disturbance and hydrological parameters determine the body size and reproductive strategy of alluvial ground beetles

Abstract Environmental variability is the main driver for the variation of biological characteristics (life-history traits) of species. Therefore, life-history traits are particularly suited to identify mechanistic linkages between environmental variability and species occurrence and can help in explaining ecological patterns. For ground beetles, few studies directly related species traits to environmental variables. This study aims to analyse how life-history traits of alluvial ground beetles are controlled by environmental factors. I expected that the occurrence of species and the occurrence of specific traits are closely related to hydrological and disturbance parameters. Furthermore I expected most of the trait-variation to be explained by a combination of environmental variables, rather than by their isolated effects. Ground beetles were sampled in the year 2005 in floodplain grassland along the Elbe River in Germany. I used redundancy analysis to quantify the effects of hydrological, sediment, and disturbance related parameters on both species occurrence and species traits. I applied variation partitioning to analyse which environmental compartments explain most of the trait variation. Species occurrence and trait variation were both mainly controlled by hydrological and flood disturbance parameters. I could clearly identify reproductive traits and body size as key traits for floodplain ground beetles to cope with the environmental variability. Furthermore, combinations of hydrological, habitat disturbance, habitat type, and species diversity parameters, rather than their isolated effects, explained large parts of ground beetle trait variation. Thus, a main conclusion of this study is that ground beetle occurrence is mainly determined by complex, multi-scale interactions between environmental variability and their life-history traits.
Antiwear Leveling Design for SSDs With Hybrid ECC Capability

With the joint considerations of reliability and performance, hybrid error correction code (ECC) becomes an option in the designs of solid-state drives (SSDs). Unfortunately, wear leveling (WL) might result in the early performance degradation to SSDs, which is common with a limited number of P/E cycles, due to the efforts to delay the bit-error-rate growth. In this paper, an anti-WL design is proposed to avoid such a performance problem so that the performance of SSDs with hybrid ECC capability can be improved without sacrificing their reliability. The capability of the proposed design was evaluated by a series of experiments, for which it was shown that the proposed design could greatly improve the read and write performance of SSDs up to 50% without affecting the endurance of the investigated SSDs, compared with traditional approaches.
A Novel Target-Objected Visual Saliency Detection Model in Optical Satellite Images

A target-oriented visual saliency detection model for optical satellite images is proposed in this paper. This model simulates the structure of the human vision system and provides a feasible way to integrate top-down and bottom-up mechanism in visual saliency detection. Firstly, low-level visual features are extracted to generate a low-level visual saliency map. After that, an attention shift and selection process is conducted on the low-level saliency map to find the current attention region. Lastly, the original version of hierarchical temporal memory (HTM) model is optimized to calculate the target probability of the attention region. The probability is then fed back to the low-level saliency map in order to obtain the final target-oriented high-level saliency map. The experiment for detecting harbor targets was performed on the real optical satellite images. Experimental results demonstrate that, compared with the purely bottom-up saliency model and the VOCUS top-down saliency model, our model significantly improves the detection accuracy
Innate Immune Responses and Rapid Control of Inflammation in African Green Monkeys Treated or Not with Interferon-Alpha during Primary SIVagm Infection

Chronic immune activation (IA) is considered as the driving force of CD4+ T cell depletion and AIDS. Fundamental clues in the mechanisms that regulate IA could lie in natural hosts of SIV, such as African green monkeys (AGMs). Here we investigated the role of innate immune cells and IFN-α in the control of IA in AGMs. AGMs displayed significant NK cell activation upon SIVagm infection, which was correlated with the levels of IFN-α. Moreover, we detected cytotoxic NK cells in lymph nodes during the early acute phase of SIVagm infection. Both plasmacytoid and myeloid dendritic cell (pDC and mDC) homing receptors were increased, but the maturation of mDCs, in particular of CD16+ mDCs, was more important than that of pDCs. Monitoring of 15 cytokines showed that those, which are known to be increased early in HIV-1/SIVmac pathogenic infections, such as IL-15, IFN-α, MCP-1 and CXCL10/IP-10, were significantly increased in AGMs as well. In contrast, cytokines generally induced in the later stage of acute pathogenic infection, such as IL-6, IL-18 and TNF-α, were less or not increased, suggesting an early control of IA. We then treated AGMs daily with high doses of IFN-α from day 9 to 24 post-infection. No impact was observed on the activation or maturation profiles of mDCs, pDCs and NK cells. There was also no major difference in T cell activation or interferon-stimulated gene (ISG) expression profiles and no sign of disease progression. Thus, even after administration of high levels of IFN-α during acute infection, AGMs were still able to control IA, showing that IA control is independent of IFN-α levels. This suggests that the sustained ISG expression and IA in HIV/SIVmac infections involves non-IFN-α products.
Bayesian Compressive Sensing for clustered sparse signals

In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals. A hierarchical statistical model is employed via Bayesian approach to model both the sparse prior and cluster prior and Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms based on the cluster prior, the proposed algorithm solves the inverse problem without any prior knowledge of the cluster parameters, even without the knowledge of the sparsity. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.
Automatic recognition of conceptualization zones in scientific articles and two life science applications

Motivation: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. Results: We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with ‘Experiment’, ‘Background’ and ‘Model’ being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. Availability: A web-based tool for the automatic annotation of articles with CoreSCs and corresponding documentation is available online at http://www.sapientaproject. com/software http://www.sapientaproject. com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated articles, which served as our training data. Contact: liakata@ebi.ac. uk Supplementary information: Supplementary data are available at Bioinformatics online.
Global stability results of discrete recurrent neural networks with interval delays

This paper establishes improved global exponential stability method for a class of discrete-time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties. The method is robust and delay range dependent. It is derived based on a new Lyapunov–Krasovskii functional constructed to exhibit the delay-dependent dynamics and to compensate for the enlarged time span. The developed stability method eliminates the need for over bounding and utilizes smaller number of linear matrix inequality (LMI) decision variables. New and less conservative solutions to the global stability problem are provided in terms of feasibility testing of new parameterized LMIs. Numerical examples are presented to illustrate the effectiveness of the developed technique.
Clinical and economic evaluation of first-line therapy with FOLFIRI or modified FOLFOX6 for metastatic colorectal cancer.

OBJECTIVE Recently, significant progress in treatment of metastatic colorectal cancer has been achieved. Either FOLFIRI (fluorouracil, leucovorin and irinotecan) or modified FOLFOX6 (fluorouracil, leucovorin and oxaliplatin, oxaliplatin dose 85 mg/m(2)) is selected as first-line therapy in clinical practice in Japan. However, economic burden of colorectal cancer is considerable. METHODS Analysis was made for all patients who were treated with FOLFIRI or modified FOLFOX6 for metastatic colorectal cancer. Regimen of FOLFIRI was compared with modified FOLFOX6 under consideration from clinical and economic standpoints. Progression free survival, response, toxicity and cancer care cost in patients with metastatic colorectal cancer was analyzed. Direct costs based on the fee schedule of the Japanese national health insurance were calculated. RESULTS Median progression free survival was 7.7 months for FOLFIRI versus 8.4 months for modified FOLFOX6 (P = 0.48). Overall cost for first four cycles was yen756 284 for FOLFIRI and yen1 081 162 for modified FOLFOX6 (P < 0.0001). All grade alopecia was significantly more frequent with FOLFIRI than with modified FOLFOX6 (P = 0.04). All grade neuropathy was more observed with modified FOLFOX6 than FOLFIRI (P = 0.0002). CONCLUSIONS FOLFIRI is inexpensive in the initial stage of treatment which a number of patients can receive chemotherapy than modified FOLFOX6 as first-line therapy for metastatic colorectal cancer in Japanese national insurance system.
Realization Utility

A number of authors have suggested that investors derive utility from realizing gains and losses on assets that they own. We present a model of this "realization utility," analyze its predictions, and show that it can shed light on a number of puzzling facts. These include the disposition effect, the poor trading performance of individual investors, the higher volume of trade in rising markets, the effect of historical highs on the propensity to sell, the individual investor preference for volatile stocks, the low average return of volatile stocks, and the heavy trading associated with highly valued assets.