Showing papers by "fondazione bruno kessler published in 2014"

A promoter-level mammalian expression atlas

Abstract: Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Abstract: We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

A SICK cure for the evaluation of compositional distributional semantic models

Abstract: Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.

The nuXmv Symbolic Model Checker

Abstract: This paper describes the nuXmv symbolic model checker for finite- and infinite-state synchronous transition systems. nuXmv is the evolution of the nuXmv open source model checker. It builds on and extends nuXmv along two main directions. For finite-state systems it complements the basic verification techniques of nuXmv with state-of-the-art verification algorithms. For infinite-state systems, it extends the nuXmv language with new data types, namely Integers and Reals, and it provides advanced SMT-based model checking techniques. Besides extended functionalities, nuXmv has been optimized in terms of performance to be competitive with the state of the art. nuXmv has been used in several industrial projects as verification back-end, and it is the basis for several extensions to cope with requirements analysis, contract based design, model checking of hybrid systems, safety assessment, and software model checking.

SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment

Abstract: This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014. Participation was open to systems based on any approach. Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. The task attracted 21 teams, most of which participated in both subtasks. We received 17 submissions in the relatedness subtask (for a total of 66 runs) and 18 in the entailment subtask (65 runs).

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Abstract: The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

A Fully Digital 8 $\,\times\,$ 16 SiPM Array for PET Applications With Per-Pixel TDCs and Real-Time Energy Output

Abstract: An 8 × 16 pixel array based on CMOS small-area silicon photomultipliers (mini-SiPMs) detectors for PET applications is reported. Each pixel is 570 × 610 μm2 in size and contains four digital mini-SiPMs, for a total of 720 SPADs, resulting in a full chip fill-factor of 35.7%. For each gamma detection, the pixel provides the total detected energy and a timestamp, obtained through two 7-b counters and two 12-b 64-ps TDCs. An adder tree overlaid on top of the pixel array sums the sensor total counts at up to 100 Msamples/s, which are then used for detecting the asynchronous gamma events on-chip, while also being output in real-time. Characterization of gamma detection performance with an 3 × 3 × 5 mm3 LYSO scintillator at 20°C is reported, showing a 511-keV gamma energy resolution of 10.9% and a coincidence timing resolution of 399 ps.

Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News

Abstract: While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting – in a totally automated way – a highcoverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way ‘crowd-sourced’ affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a naive approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Abstract: Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.

Neural Decision Forests for Semantic Image Labelling

Abstract: In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier--An idea that is intuitively supported by the hierarchical nature of the decision forest model where the input space is typically left unchanged during training and testing. We bridge this gap by introducing randomized Multi- Layer Perceptrons (rMLP) as new split nodes which are capable of learning non-linear, data-specific representations and taking advantage of them by finding optimal predictions for the emerging child nodes. To prevent overfitting, we i) randomly select the image data fed to the input layer, ii) automatically adapt the rMLP topology to meet the complexity of the data arriving at the node and iii) introduce an l1-norm based regularization that additionally sparsifies the network. The key findings in our experiments on three different semantic image labelling datasets are consistently improved results and significantly compressed trees compared to conventional classification trees.

We are not All Equal: Personalizing Models for Facial Expression Analysis with Transductive Parameter Transfer

Abstract: Previous works on facial expression analysis have shown that person specific models are advantageous with respect to generic ones for recognizing facial expressions of new users added to the gallery set. This finding is not surprising, due to the often significant inter-individual variability: different persons have different morphological aspects and express their emotions in different ways. However, acquiring person-specific labeled data for learning models is a very time consuming process. In this work we propose a new transfer learning method to compute personalized models without labeled target data Our approach is based on learning multiple person-specific classifiers for a set of source subjects and then directly transfer knowledge about the parameters of these classifiers to the target individual. The transfer process is obtained by learning a regression function which maps the data distribution associated to each source subject to the corresponding classifier's parameters. We tested our approach on two different application domains, Action Units (AUs) detection and spontaneous pain recognition, using publicly available datasets and showing its advantages with respect to the state-of-the-art both in term of accuracy and computational cost.

Boron-Proton Nuclear-Fusion Enhancement Induced in Boron-Doped Silicon Targets by Low-Contrast Pulsed Laser

Abstract: Nuclear reactions that produce alpha particles have been studied for decades, but new experiments yield higher fluxes of alpha particles using only moderate-power lasers.

Gas-Drone: Portable gas sensing system on UAVs for gas leakage localization

Abstract: Volatile chemical concentration and gas leakage recognition can be crucial in environmental monitoring for risk assessment. The use of Unmanned Aerial Vehicles (UAVs) to measure spatially distributed gas concentration is of great interest because it allows a Simultaneous Localization And Mapping (SLAM) of the volatiles. This field is quite recent and, so far, few efforts have been dedicated to the design of integrated sensing instruments that focus on the optimization of crucial features as weight, dimension and energy autonomy, as important as selectivity and sensitivity of sensors on board UAVs. The proposed Gas Sensing System (GSS) is a fully autonomous board based on a 32bit MCU with 30min autonomy (on its own battery), data storing, wireless connectivity for real-time feedback and embeds a custom micro-machined MOX (Metal Oxide) sensor. This system can be mounted on any UAV thanks to its small dimensions and light weight. Experiments demonstrate that the sensing performance is not impaired by the air flow during the flight and we are able to spatially describe the volatile concentration.

IC3 Modulo Theories via Implicit Predicate Abstraction

Abstract: We present a novel approach for generalizing the IC3 algorithm for invariant checking from finite-state to infinite-state transition systems, expressed over some background theories. The procedure is based on a tight integration of IC3 with Implicit (predicate) Abstraction, a technique that expresses abstract transitions without computing explicitly the abstract system and is incremental with respect to the addition of predicates. In this scenario, IC3 operates only at the Boolean level of the abstract state space, discovering inductive clauses over the abstraction predicates. Theory reasoning is confined within the underlying SMT solver, and applied transparently when performing satisfiability checks. When the current abstraction allows for a spurious counterexample, it is refined by discovering and adding a sufficient set of new predicates. Importantly, this can be done in a completely incremental manner, without discarding the clauses found in the previous search.

Effects of divergent ghost loops on the Green’s functions of QCD

Abstract: In the present work, we discuss certain characteristic features encoded in some of the fundamental QCD Green's functions, for which the origin can be traced back to the nonperturbative masslessness of the ghost field, in the Landau gauge. Specifically, the ghost loops that contribute to these Green's functions display infrared divergences, akin to those encountered in the perturbative treatment, in contradistinction to the gluonic loops, for which perturbative divergences are tamed by the dynamical generation of an effective gluon mass. In $d=4$, the aforementioned divergences are logarithmic, thus causing a relatively mild impact, whereas in $d=3$ they are linear, giving rise to enhanced effects. In the case of the gluon propagator, these effects do not interfere with its finiteness, but make its first derivative diverge at the origin, and introduce a maximum in the region of infrared momenta. The three-gluon vertex is also affected, and the induced divergent behavior is clearly exposed in certain special kinematic configurations, usually considered in lattice simulations; the sign of the corresponding divergence is unambiguously determined. The main underlying concepts are developed in the context of a simple toy model, which demonstrates clearly the interconnected nature of the various effects. The picture that emerges is subsequently corroborated by a detailed nonperturbative analysis, combining lattice results with the dynamical integral equations governing the relevant ingredients, such as the nonperturbative ghost loop and the momentum-dependent gluon mass.

Vocal tract length normalisation approaches to DNN-based children's and adults' speech recognition

Abstract: This paper introduces approaches based on vocal tract length normalisation (VTLN) techniques for hybrid deep neural network (DNN) - hidden Markov model (HMM) automatic speech recognition when targeting children's and adults' speech. VTLN is investigated by training a DNN-HMM system by using first mel frequency cepstral coefficients (MFCCs) normalised with standard VTLN. Then, MFCCs derived acoustic features are combined with the VTLN warping factors to obtain an augmented set of features as input to a DNN. In this later, novel, approach the warping factors are obtained with a separate DNN and the decoding can be operated in a single pass when standard VTLN approach requires two decoding passes. Both VTLN-based approaches are shown to improve phone error rate performance, up to 20% relative improvement, compared to a baseline trained on a mixture of children's and adults' speech.

An ontology for the Business Process Modelling Notation

Abstract: In this paper we describe a formal ontological description of the Business Process Modelling Notation (BPMN), one of the most popular languages for business process modelling. The proposed ontology (the BPMN Ontology) provides a classification of all the elements of BPMN, together with the formal description of the attributes and conditions describing how the elements can be combined in a BPMN business process description. Using the classes and properties defined in the BPMN Ontology any BPMN diagram can be represented as an A-box (i.e., a set of instances and assertions on them) of the ontology: this allows the exploitation of ontological reasoning services such as consistency checking and query answering to investigate the compliance of a process with the BPMN Specification as well as other structural property of the process. The paper also presents the modelling process followed for the creation of the BPMN Ontology, and describes some application scenarios exploiting the BPMN Ontology.

Money walks: a human-centric study on the economics of personal mobile data

Abstract: In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.

The DIRHA simulated corpus

Abstract: This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA). The corpus is composed of several sequences obtained by convolution of dry acoustic events with more than 9000 impulse responses measured in a real apartment equipped with 40 microphones. The acoustic events include in-domain sentences of different typologies uttered by native speakers in four different languages and non-speech events representing typical domestic noises. To increase the realism of the resulting corpus, background noises were recorded in the real home environment and then added to the generated sequences. The purpose of this work is to describe the simulation procedure and the data sets that were created and used to derive the corpus. The corpus contains signals of different characteristics making it suitable for various multi-microphone signal processing and distant speech recognition tasks.

An Analysis of Causality between Events and its Relation to Temporal Information

Abstract: In this work we present an annotation framework to capture causality between events, inspired by TimeML, and a language resource covering both temporal and causal relations. This data set is then used to build an automatic extraction system for causal signals and causal links between given event pairs. The evaluation and analysis of the system’s performance provides an insight into explicit causality in text and the connection between temporal and causal relations.

A family of experiments to assess the effectiveness and efficiency of source code obfuscation techniques

Abstract: Context: code obfuscation is intended to obstruct code understanding and, eventually, to delay malicious code changes and ultimately render it uneconomical. Although code understanding cannot be completely impeded, code obfuscation makes it more laborious and troublesome, so as to discourage or retard code tampering. Despite the extensive adoption of obfuscation, its assessment has been addressed indirectly either by using internal metrics or taking the point of view of code analysis, e.g., considering the associated computational complexity. To the best of our knowledge, there is no publicly available user study that measures the cost of understanding obfuscated code from the point of view of a human attacker. Aim: this paper experimentally assesses the impact of code obfuscation on the capability of human subjects to understand and change source code. In particular, it considers code protected with two well-known code obfuscation techniques, i.e., identifier renaming and opaque predicates. Method: We have conducted a family of five controlled experiments, involving undergraduate and graduate students from four Universities. During the experiments, subjects had to perform comprehension or attack tasks on decompiled clients of two Java network-based applications, either obfuscated using one of the two techniques, or not. To assess and compare the obfuscation techniques, we measured the correctness and the efficiency of the performed task. Results: --at least for the tasks we considered--simpler techniques (i.e., identifier renaming) prove to be more effective than more complex ones (i.e., opaque predicates) in impeding subjects to complete attack tasks.

The matecat tool

Abstract: We present a new web-based CAT tool providing translators with a professional work environment, integrating translation memories, terminology bases, concordancers, and machine translation. The tool is completely developed as open source software and has been already successfully deployed for business, research and education. The MateCat Tool represents today probably the best available open source platform for investigating, integrating, and evaluating under realistic conditions the impact of new machine translation technology on human post-editing.

Characterization of Single-Photon Time Resolution: From Single SPAD to Silicon Photomultiplier

Abstract: In this paper, we report on the characterization of the single-photon time resolution (SPTR) of the RGB (Red-Green-Blue) type silicon photomultipliers (SiPM) produced at FBK. We measured and compared single-photon timing jitter of 1 ×1 mm 2 and 3 ×3 mm 2 SiPMs, and also of square SPADs with integrated passive quenching, identical to the cells composing the SiPMs. We reached a single-photon time resolution of about 180 ps full-width at half-maximum for 3 ×3 mm 2 SiPM, 80 ps for 1 ×1 mm 2 SiPM and less than 50 ps for single cells. From measurements with pinholes placed in front of 1 ×1 mm 2 detector we see a very good cell-to-cell uniformity: it is not a limiting factor for time resolution. We also characterized the timing jitter of SiPMs as a function of the number of photons per laser pulse (N) finding that it does not decrease exactly with the square root of N because of the optical crosstalk between cells.

New method for determining the quark-gluon vertex

Abstract: We present a novel nonperturbative approach for calculating the form factors of the quark-gluon vertex in terms of an unknown three-point function, in the Landau gauge. The key ingredient of this method is the exact all-order relation connecting the conventional quark-gluon vertex with the corresponding vertex of the background field method, which is Abelian-like. When this latter relation is combined with the standard gauge technique, supplemented by a crucial set of transverse Ward identities, it allows the approximate determination of the nonperturbative behavior of all 12 form factors comprising the quark-gluon vertex, for arbitrary values of the momenta. The actual implementation of this procedure is carried out in the Landau gauge, in order to make contact with the results of lattice simulations performed in this particular gauge. The most demanding technical aspect involves the approximate calculation of the components of the aforementioned (fully dressed) three-point function, using lattice data as input for the gluon propagators appearing in its diagrammatic expansion. The numerical evaluation of the relevant form factors in three special kinematical configurations (soft-gluon and quark symmetric limit, zero quark momentum) is carried out in detail, finding qualitative agreement with the available lattice data. Most notably, a concrete mechanism is proposed for explaining the puzzling divergence of one of these form factors observed in lattice simulations.

The Excitement Open Platform for Textual Inferences

Abstract: This paper presents the Excitement Open Platform (EOP), a generic architecture and a comprehensive implementation for textual inference in multiple languages. The platform includes state-of-art algorithms, a large number of knowledge resources, and facilities for experimenting and testing innovative approaches. The EOP is distributed as an open source software.

Annotating Causality in the TempEval-3 Corpus

Abstract: While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen’s temporal logic, the question on how to annotate other types of event relations, in particular causal ones, is still open. In this work, we present some annotation guidelines to capture causality between event pairs, partly inspired by TimeML. We then implement a rule-based algorithm to automatically identify explicit causal relations in the TempEval-3 corpus. Based on this annotation, we report some statistics on the behavior of causal cues in text and perform a preliminary investigation on the interaction between causal and temporal relations.