Miracle-dz/UniFAR-FTUs huggingface dataset:
Animating Lip-Sync Characters With Dominated Animeme Models

Character speech animation is traditionally considered as important but tedious work, especially when taking lip synchronization (lip-sync) into consideration. Although there are some methods proposed to ease the burden on artists to create facial and speech animation, almost none is fast and efficient. In this paper, we introduce a framework for synthesizing lip-sync character speech animation in real time from a given speech sequence and its corresponding texts, starting from training dominated animeme models (DAMs) for each kind of phoneme by learning the character's animation control signal through an expectation-maximization (EM)-style optimization approach. The DAMs are further decomposed to polynomial-fitted animeme models and corresponding dominance functions while taking coarticulation into account. Finally, given a novel speech sequence and its corresponding texts, the animation control signal of the character can be synthesized in real time with the trained DAMs. The synthesized lip-sync animation can even preserve exaggerated characteristics of the character's facial geometry. Moreover, since our method can perform in real time, it can be used for many applications, such as lip-sync animation prototyping, multilingual animation reproduction, avatar speech, and mass animation production. Furthermore, the synthesized animation control signal can be imported into 3-D packages for further adjustment, so our method can be easily integrated into the existing production pipeline.
RNA-Sequencing Analysis of HepG2 Cells Treated with Atorvastatin

The cholesterol-lowering drug atorvastatin is among the most prescribed drug in the world. Alternative splicing in a number of genes has been reported to be associated with variable statin response. RNA-seq has proven to be a powerful technique for genome-wide splice variant analysis. In the present study, we sought to investigate atorvastatin responsive splice variants in HepG2 cells using RNA-seq analysis to identify novel candidate genes implicated in cholesterol homeostasis and in the statin response. HepG2 cells were treated with 10 µM atorvastatin for 24 hours. RNA-seq and exon array analyses were performed. The validation of selected genes was performed using Taqman gene expression assays. RNA-seq analysis identified 121 genes and 98 specific splice variants, of which four were minor splice variants to be differentially expressed, 11 were genes with potential changes in their splicing patterns (SYCP3, ZNF195, ZNF674, MYD88, WHSC1, KIF16B, ZNF92, AGER, FCHO1, SLC6A12 and AKAP9), and one was a gene (RAP1GAP) with differential promoter usage. The IL21R transcript was detected to be differentially expressed via RNA-seq and RT-qPCR, but not in the exon array. In conclusion, several novel candidate genes that are affected by atorvastatin treatment were identified in this study. Further studies are needed to determine the biological significance of the atorvastatin responsive splice variants that have been uniquely identified using RNA-seq.
Using augmented reality to interact with an autonomous mobile platform

To allow users without special knowledge to interact with robots, it is desirable to make interaction methods as intuitive as possible. This goal is in many cases difficult to achieve since data flow from the robot to the human is limited, especially if free locomotion of both the human and the robot are required. Therefore, new communication channels need to be created. We propose the use of an augmented reality display together with a wearable, wirelessly networked computer to achieve this goal. This system makes it possible to overlay planning, world model and sensory data provided by the robot over the wearer's field of view. We discuss the system architecture, interaction methods and experimental results. We demonstrate an example application for rapid prototyping of a warehouse transport system using the augmented reality system and a mobile platform. The user can create a topological map in an unknown environment on-the fly by setting and manipulating map nodes. This is done by pointing at the floor with a special interaction device, and issuing voice commands. The map is shown to the user as an augmentation of the real world view. Additionally, the robot's path planning data is visualized.
Adaptive functional programming

An adaptive computation maintains the relationship between its input and output as the input changes. Although various techniques for adaptive computing have been proposed, they remain limited in their scope of applicability. We propose a general mechanism for adaptive computing that enables one to make any purely-functional program adaptive.We show that the mechanism is practical by giving an efficient implementation as a small ML library. The library consists of three operations for making a program adaptive, plus two operations for making changes to the input and adapting the output to these changes. We give a general bound on the time it takes to adapt the output, and based on this, show that an adaptive Quicksort adapts its output in logarithmic time when its input is extended by one key.To show the safety and correctness of the mechanism we give a formal definition of AFL, a call-by-value functional language extended with adaptivity primitives. The modal type system of AFL enforces correct usage of the adaptivity mechanism, which can only be checked at run time in the ML library. Based on the AFL dynamic semantics, we formalize the change-propagation algorithm and prove its correctness.
Wavelet transform based land cover classification of hyperspectral images

This paper aims at the wavelet transform based algorithm for landcover classification of Hyperspectral remote sensing images using Support Vector Machines (SVM). In this paper Feature Extraction and Hyperspectral pixel classification are done based on Discrete Wavelet Transform (DWT) features which includes the Statistical Features and the Gray Level Co-occurrence Features. The experiment is performed on a hyperspectral dataset acquired from ROSIS sensor and the experimental results indicate that it provides an Overall accuracy of about 98.28%. When compared to the other methods, the wavelet transform based method increases the overall classification accuracy.
PRADA: Prioritizing Android Devices for Apps by Mining Large-Scale Usage Data

Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for individual apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile apps – the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.
An Exploration of Gene–Environment Interaction and Asthma in a Large Sample of 5-Year-Old Dutch Twins

Abstract A consistent finding from twin studies is that the environment shared by family members does not contribute to the variation in susceptibility to asthma. At the same time, it is known that environmental risk factors that are shared by family members are associated with the liability for asthma. We hypothesize that the absence of a main effect of shared environmental factors in twin studies can be explained by gene–environment interaction, that is, that the effect of an environmental factor shared by family members depends on the genotype of the individual. We explore this hypothesis by modeling the resemblance in asthma liability in twin pairs as a function of various environmental risk factors and test for gene–environment interaction. Asthma data were obtained by parental report for nearly 12,000 5-year-old twin pairs. A series of environmental risk factors was examined: birth cohort, gestational age, time spent in incubator, breastfeeding, maternal educational level, maternal smoking during pregnancy, current smoking of parents, having older siblings, and amount of child care outside home. Results revealed that being a boy, born in the 1990s, premature birth, longer incubator time, and child care outside home increased the risk for asthma. With the exception of premature birth, however, none of these factors modified the genetic effects on asthma. In very premature children shared environmental influences were important. In children born after a gestation of 32 weeks or more only genetic factors were important to explain familial resemblance for asthma.
CSF‐1, IGF‐1, and the control of postnatal growth and development

Growth hormone controls somatic growth in mammals by regulating the production of IGF‐1, which is predominantly made by the liver. The development of cells within the MPS is controlled by the lineage‐specific growth factor M‐CSF (CSF‐1). In this review, we summarize the role of CSF‐1‐dependent macrophages in somatic growth and organogenesis. We propose that macrophages are the major extrahepatic source of IGF‐1 and that a surge of CSF‐1 production contributes to the control of postnatal growth and organ maturation. Accordingly, CSF‐1 may be considered a part of the GH/IGF‐1 axis.
Rate alteration attacks in smart grid

Smart Grid addresses the problem of existing power grid's increasing complexity, growing demand and requirement for greater reliability, through two-way communication and automated residential load control among others. These features also makes the Smart Grid a target for a number of cyber attacks. In the paper, we study the problem of rate alteration attack (RAA) through fabrication of price messages which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is NP-Complete and provide its inapproximability. We devise two approaches for the problem, former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure while the later takes cascading potential into account while choosing the lines to fail. To get more insight into the impact of RAA, we also extend our algorithms to maximize number of node failures. Empirical results on both IEEE Bus data and real network help us evaluate our approaches under various settings of grid parameters.
Usage Patterns of the Java Standard API

The Java Standard API has grown enormously since Java's beginnings, now consisting of over 3,000 classes and 20,000 methods. The intent of this API is to provide high quality components that can be easily reused and so increase the Java developer's productivity - but does it? In this paper, we present a study that begins to answer this question. Specifically we take a corpus-based approach to help determine the "typical" usage of the Standard API. We find that, in an extensive corpus of open-source software, only about 50% of the classes in the Standard API are used at all, and around 21% of the methods are used. We discuss the implications this has for future development of both the API itself, and for tools to support the API.
Sibling Peer Support Group for Young People with a Sibling Using Drugs: A Pilot Study

Abstract Siblings of young people abusing drugs are at particular risk for drug abuse and other health compromising behaviors. A Sibling Peer Support Group was developed by the Centre for Adolescent Health (Melbourne, Australia) for young people aged 13 to 18 years with a problematic drug user in their family. Groups aimed to provide support and in formation, promote harm minimization, and reduce the sense of isolation. The project emanated from the recognized need for specific support for adolescent siblings of problematic drug users. Evaluation of two pilot groups indicated positive benefits for group members, who reported feeling better informed, more supported, and having a reduced sense of isolation. Parents reported that their adolescent attending the group demonstrated improved communication with, and greater understanding and tolerance of, the family member using drugs. Promising indicators at a community level were manifested in enthusiastic collaboration among schools, police and local service agencies, and the organization of a local drug forum. There appeared to be little evidence that the groups inadvertently encouraged drug use. Recruitment of young people into groups was the major challenge for the project, but among drug and alcohol and family organizations there was support for the concept of a Sibling Peer Support Group. A new model to overcome the challenge of recruitment is proposed.
On‐line coupling of solid‐phase microextraction to commercial CE‐MS equipment

We developed a new interface for the on‐line coupling of a fiber for solid‐phase microextraction (SPME) with a CE system with an electrospray interface for MS detection. The on‐line interface was a modification of a split‐flow interface typically used for coupling flow systems to CE. The modification consists of an additional channel closed with a septum that permits the introduction of the fiber in the interface, right at the inlet region of the separation capillary. The interface was designed to be used with commercial fibers. Although commercial fibers can be used directly, we recommend cutting the stationary phase and using fibers with a 2‐mm stationary phase. The system was tested for the analysis of tetracycline antibiotic residues, viz., tetracycline, oxytetracycline and doxycycline in soils. The overall accuracy was assessed by regression analysis of spiked samples, showing successful results at a 95% confidence level. The reproducibility, expressed as RSD, was 5.3% and the LOD of tetracycline antibiotics in soil ranged from 2.9 to 3.2 μg/kg.
On the function and heterogeneity of extracellular vesicles.

Both, prokaryotic and eukaryotic cells contain the ability to communicate with cells in their environment. Classically, intercellular cell communication was thought to be mainly mediated by direct cell-cell contacts and by soluble factors including cytokines, growth factors and hormones. In 1996 Raposo and colleagues showed that intercellular communication can also be mediated by vesicles; specifically they reported that B lymphoblastoid cells release MHC class II carrying vesicles which are able to induce antigen-specific MHC class II-restricted T cell responses (1). Since then, an exponentially increasing amount of studies demonstrates the importance of extracellular vesicle (EV) mediated intercellular signaling in a huge variety of different cellular systems and organisms ranging from bacteria and yeast to plants and humans. It became evident that EV-mediated intercellular communication is essentially involved in many physiological and pathophysiological processes (2). Thus, EV-mediated intercellular communication provides a third, very complex mode of intercellular communication. Unraveling the basic principles of this novel intercellular communication system and gaining insight in its complexity will for sure have large impacts on the overall understanding of biological processes and certainly will pave the way for novel diagnostic and therapeutic approaches (2-4).
ngineering Crises : Favoritism and Strategic Fiscal Indiscipline ∗

If people understand that some macroeconomic policies are unsustainable, why would they vote for them in the first place? We develop a political economy theory of the endogenous emergence of fiscal crises, based on the idea that the adjustment mechanism to a crisis favors some social groups, that may be induced ex-ante to vote in favor of policies that are more likely to lead to a crisis. People are entitled to a certain level of a publicly provided good, which may be rationed in times of crises. After voting on that level, society votes on the extend to which it will be financed by debt. Under bad enough macro shocks, a crisis arises: taxes are set at their maximum but despite that some agents do not get their entitlement. Some social groups do better in this rationing process than others. We show that public debt —which makes crises more likely — is higher, as is the probability of a crisis, the greater the level of favoritism. If the favored group is important enough to be pivotal when society votes on the entitlement level, favoritism also leads to greater public expenditure. We show that the favored group may strategically favor a weaker state in order to make crises more frequent. Finally, the decisive voter when choosing expenditure may be different from the one when voting on debt. In such a case, constitutional limits on debt may raise the utility of all the poor, relative to the equilibrium outcome absent such limits.
Attributions and Attitudes of Mothers and Fathers in the United States

SYNOPSIS Objective. The present study examined mean level similarities and differences as well as correlations between U.S. mothers' and fathers' attributions regarding successes and failures in caregiving situations and progressive versus authoritarian attitudes. Design. Interviews were conducted with mothers and fathers in 139 European American, Latin American, and African American families. Results. Interactions between parent gender and ethnicity emerged for adult-controlled failure and perceived control over failure. Fathers reported higher adult-controlled failure and child-controlled failure attributions than did mothers, whereas mothers reported attitudes that were more progressive and modern than did fathers; these differences remained significant after controlling for parents' age, education, and possible social desirability bias. Ethnic differences emerged for 5 of the 7 attributions and attitudes examined; 4 remained significant after controlling for parents' age, education, and possible social desirability bias. Medium effect sizes were found for concordance between parents in the same family for attributions regarding uncontrollable success, child-controlled failure, progressive attitudes, authoritarian attitudes, and modernity of attitudes after controlling for parents' age, education, and possible social desirability bias. Conclusions. This work elucidates ways that parent gender and ethnicity relate to attributions regarding U.S. parents' successes and failures in caregiving situations and to their progressive versus authoritarian parenting attitudes.
Approximate Lifted Belief Propagation

Lifting can greatly reduce the cost of inference on first-order probabilistic models, but constructing the lifted network can itself be quite costly. In addition, the minimal lifted network is often very close in size to the fully propositionalized model; lifted inference yields little or no speedup in these situations. In this paper, we address both these problems. We propose a compact hypercube-based representation for the lifted network, which can greatly reduce the cost of lifted network construction. We also present two methods for approximate lifted network construction, which groups together similar but distinguishable objects and treats them as if they were identical. This can greatly reduce the size of the lifted network as well as the time required for lifted network construction, but potentially at some cost to accuracy. The coarseness of the approximation can be adjusted depending on the accuracy required, and we can bound the resulting error. Experiments on six domains show great efficiency gains with only minor loss in accuracy.
QLMOR: A new projection-based approach for nonlinear model order reduction

We present a new projection-based nonlinear model order reduction method, named QLMOR (MOR via quadratic-linear systems). QL-MOR employs two novel ideas: (1) we show that DAEs (differential-algebraic equations) with many commonly-encountered nonlinear kernels can be re-written equivalently into a special format, QL-DAEs (quadratic-linear differential algebraic equations, i.e., DAEs that are quadratic in their state variables and linear in their inputs); (2) we adapt the moment-matching reduction technique of NORM[1] to reduce these QLDAEs into QLDAEs of much smaller size. Because of the generality of the QLDAE form, QLMOR has significantly broader applicability than Taylor-expansion based methods [2, 3, 1]. Importantly, QLMOR, unlike NORM, totally avoids explicit moment calculations (AiB terms), hence it has improved numerical stability properties as well. Because the reduced model has only quadratic nonlinearities (i.e., no cubic and higher-order terms), its computational complexity is less than that of similar prior methods[2, 3, 1]. We also prove that QLMOR-reduced models preserve local passivity, and provide an upper bound on the size of the QLDAEs derived from a polynomial system. We compare QLMOR against prior methods [2, 3, 1] on a circuit and a biochemical reaction-like system, and demonstrate that QLMOR-reduced models retain accuracy over a significantly wider range of excitation than Taylor-expansion based methods [2, 3, 1]. Indeed, QLMOR is able to reduce systems that Taylor-expansion based methods fail to reduce due to passivity loss and impracti-cally high computational costs. QLMOR therefore demonstrates that Volterra-kernel based nonlinear MOR techniques can in fact have far broader applicability than previously suspected, possibly being competitive with trajectory-based methods (e.g., TPWL [4]) and nonlinear-projection based methods (e.g., maniMOR [5]).
Work characteristics, anxiety and depression

In addition to serious human suffering, mental disorders result in work impairment.1 2 The costs of mental disorders, particularly depression, to employers in terms of lost work days and reduced productivity is substantial.3 Studies from several countries report an increase in work stress,4 and that mental disorders, especially depression, are growing reasons for work disability and early retirement.5 6 The question about psychosocial work characteristics as risk factors for depression has attracted increasing interest among researchers. A recent meta-analysis provided evidence that high demands and low decision latitude, high effort-reward imbalance, and low social support at work from co-workers and supervisors are risk factors for common mental disorders.7 A major methodological limitation in earlier studies on psychosocial work characteristics and mental health is related to bias due to response …
Integrating Theory and Practice in Parallel File Systems

Several algorithms for parallel disk systems have appeared in the literature recently, and they are asymptotically optimal in terms of the number of disk accesses. Scalable systems with parallel disks must be able to run these algorithms. We present a list of capabilities that must be provided by the system to support these optimal algorithms: control over declustering, querying about the configuration, independent I/O, turning off file caching and prefetching, and bypassing parity. We summarize recent theoretical and empirical work that justifies the need for these capabilities.