Miracle-dz/UniFAR-FTUs huggingface dataset:
Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees

Animals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.
Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.
Coupling Between Leg Muscle Activation and EEG During Normal Walking, Intentional Stops, and Freezing of Gait in Parkinson's Disease

In this paper, we apply novel techniques for characterizing leg muscle activation patterns via electromyograms (EMGs) and for relating them to changes in electroencephalogram (EEG) activity during gait experiments. Specifically, we investigate changes of leg-muscle EMG amplitudes and EMG frequencies during walking, intentional stops, and unintended freezing-of-gait (FOG) episodes. FOG is a frequent paroxysmal gait disturbance occurring in many patients suffering from Parkinson's disease (PD). We find that EMG amplitudes and frequencies do not change significantly during FOG episodes with respect to walking, while drastic changes occur during intentional stops. Phase synchronization between EMG signals is most pronounced during walking in controls and reduced in PD patients. By analyzing cross-correlations between changes in EMG patterns and brain-wave amplitudes (from EEGs), we find an increase in EEG-EMG coupling at the beginning of stop and FOG episodes. Our results may help to better understand the enigmatic pathophysiology of FOG, to differentiate between FOG events and other gait disturbances, and ultimately to improve diagnostic procedures for patients suffering from PD.
The Oncolytic Virus dl922-947 Triggers Immunogenic Cell Death in Mesothelioma and Reduces Xenograft Growth

Background: Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure that urgently requires effective therapeutic strategies. Current treatments are unable to increase significantly patient survival, which is often limited to <1 year from diagnosis. Virotherapy, based on the use of oncolytic viruses that exert anti-cancer effects by direct cell lysis and through the induction of anti-tumor immune response, represents an alternative therapeutic option for rare tumors with limited life expectancy. In this study, we propose the use of the adenovirus dl922-947, engineered to allow selective replication in cancer cells, to counteract MPM. Methods: We performed a thorough preclinical assessment of dl922-947 effects in a set of MPM cell lines and xenografts. Cytotoxicity of dl922-947 alone and in combination assays was evaluated by sulforhodamine B assay. Cell cycle, calreticulin expression, and high mobility group box protein 1 (HMGB1) secretion were determined by flow cytometry, whereas ATP content was determined by a luminescence-based bioassay. The modulation of angiogenic factors in MPM-infected cells was evaluated through ELISA. Results: We found that dl922-947 infection exhibits cytotoxic effects in MPM cell lines, affecting cell viability, cell cycle progression, and regulating main hallmarks of immunogenic cell death inducing calreticulin surface exposure, HMGB1 and ATP release. Our results also suggest that dl922-947 may affect angiogenic signals by regulation of VEGF-A and IL-8 secretion. Furthermore, dl922-947 shows anti-tumor efficacy in murine xenograft models reducing tumor growth and enhancing survival. Finally, the combination with cisplatin potentiated the cytotoxic effect of dl922-947. Conclusions: Overall our data identify virotherapy, based on the use of dl922-947, as a new possible therapeutic strategy against MPM, which could be used alone, in combination with standard chemotherapy drugs, as shown here, or other approaches also aimed at enhancing the antitumoral immune response elicited by the virus.
Static Metabolic Bubbles as Precursors of Vascular Gas Emboli During Divers’ Decompression: A Hypothesis Explaining Bubbling Variability

Introduction The risk for decompression sickness (DCS) after hyperbaric exposures (such as SCUBA diving) has been linked to the presence and quantity of vascular gas emboli (VGE) after surfacing from the dive. These VGE can be semi-quantified by ultrasound Doppler and quantified via precordial echocardiography. However, for an identical dive, VGE monitoring of divers shows variations related to individual susceptibility, and, for a same diver, dive-to-dive variations which may be influenced by pre-dive pre-conditioning. These variations are not explained by currently used algorithms. In this paper, we present a new hypothesis: individual metabolic processes, through the oxygen window (OW) or Inherent Unsaturation of tissues, modulate the presence and volume of static metabolic bubbles (SMB) that in turn act as precursors of circulating VGE after a dive. Methods We derive a coherent system of assumptions to describe static gas bubbles, located on the vessel endothelium at hydrophobic sites, that would be activated during decompression and become the source of VGE. We first refer to the OW and show that it creates a local tissue unsaturation that can generate and stabilize static gas phases in the diver at the surface. We then use Non-extensive thermodynamics to derive an equilibrium equation that avoids any geometrical description. The final equation links the SMB volume directly to the metabolism. Results and Discussion Our model introduces a stable population of small gas pockets of an intermediate size between the nanobubbles nucleating on the active sites and the VGE detected in the venous blood. The resulting equation, when checked against our own previously published data and the relevant scientific literature, supports both individual variation and the induced differences observed in pre-conditioning experiments. It also explains the variability in VGE counts based on age, fitness, type and frequency of physical activities. Finally, it fits into the general scheme of the arterial bubble assumption for the description of the DCS risk. Conclusion Metabolism characterization of the pre-dive SMB population opens new possibilities for decompression algorithms by considering the diver’s individual susceptibility and recent history (life style, exercise) to predict the level of VGE during and after decompression.
Inductive Proof Rules Beyond Safety Properties

The verification of temporal logic properties of reactive systems has been classically introduced as a model checking problem where one has to check that a temporal logic property holds on all initial states of a considered state transition system. A major breakthrough has been achieved by symbolic model checking, first by fixpoint computations where the state sets were stored as canonical normal forms of propositional logic formulas like BDDs, and later as bounded and SAT-based model checking procedures using SAT solvers. Interpolation-based model checking finally paved the way for induction-based methods like property-directed reachability (PDR) that are currently the most efficient verification procedures. However, PDR is so-far only used for the verification of safety properties, i.e., to prove that a desired property holds on all reachable states. In this paper, we prove the correctness and completeness of induction rules for general fixpoint formulas that extend Park’s fixpoint induction rules. We instantiate these rules for all CTL operators so that induction becomes available for all CTL properties. Moreover, we develop further induction rules for liveness properties by considering liveness properties as bounded safety properties. In general, we therefore point out that induction-based proof methods are not limited to safety properties.
CNN-based System for Low Resolution Face Recognition

Since the publication of the AlexNet in 2012, Deep Convolutional Neural Network models became the most promising and powerful technique for image representation. Specifically, the ability of their inner layers to extract high level abstractions of the input images, called deep features vectors, has been employed. Such vectors live in a high dimensional space in which an inner product and thus a metric is defined. The latter allows to carry out similarity measurements among them. This property is particularly useful in order to accomplish tasks such as Face Recognition. Indeed, in order to identify a person it is possible to compare deep features, used as face descriptors, from different identities by means of their similarities. Surveillance systems, among others, utilize this technique. To be precise, deep features extracted from probe images are matched against a database of descriptors from known identities. A critical point is that the database typically contains features extracted from high resolution images while the probes, taken by surveillance cameras, can be at a very low resolution. Therefore, it is mandatory to have a neural network which is able to extract deep features that are robust with respect to resolution variations. In this paper we discuss a CNN-based pipeline that we built for the task of Face Recognition among images with different resolution. The entire system relies on the ability of a CNN to extract deep features that can be used to perform a similarity search in order to fulfill the face recognition task.
Improved Quality Keyframe Selection Method for HD Video

With the widespread use of the Internet, services for providing large-capacity multimedia data such as video-on-demand (VOD) services and video uploading sites have greatly increased. VOD service providers want to be able to provide users with high-quality keyframes of high quality videos within a few minutes after the broadcast ends. However, existing keyframe extraction tends to select keyframes whose quality as a keyframe is insufficiently considered, and it takes a long computation time because it does not consider an HD class image. In this paper, we propose a keyframe selection method that flexibly applies multiple keyframe quality metrics and improves the computation time. The main procedure is as follows. After shot boundary detection is performed, the first frames are extracted as initial keyframes. The user sets evaluation metrics and priorities by considering the genre and attributes of the video. According to the evaluation metrics and the priority, the low-quality keyframe is selected as a replacement target. The replacement target keyframe is replaced with a high-quality frame in the shot. The proposed method was subjectively evaluated by 23 votes. Approximately 45% of the replaced keyframes were improved and about 18% of the replaced keyframes were adversely affected. Also, it took about 10 minutes to complete the summary of one hour video, which resulted in a reduction of more than 44.5% of the execution time.
Querying Cultural Heritage Knowledge Bases in Natural Language: Discussion Paper

Knowledge Based Question Answering (KBQA) is concerned with the possibility of querying data by posing questions in Natural Language (NL), that is by far, the most (human) common form of expressing an information need. This work reviews an approach to Question Answering that has the goal to transform Natural Language questions into SPARQL queries over cultural heritage knowledge bases. The key idea is to apply a rule-based classification process that we call template matching and that we have implemented in a prototype using logic programming. In the paper we discuss about the application of the prototype in real use-case, and indicate ongoing and future directions of work.
Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: a study with the semantic learning machine

Prostate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.
Information Discovery in Polystores: The Augmented Way

Polystores provide a loosely coupled integration of heterogeneous data sources based on the direct access, with the local language, to each storage engine for exploiting its distinctive features. In this framework, given the absence of a middleware exposing a global schema, it is hard to know if a query to one system can be satisfied by data stored elsewhere in the polystore. We address this problem by illustrating query augmentation, a data manipulation operator for polystores based on the automatic enrichment of the answer to a local query with related data in the rest of the polystore. Augmentation can be used to implement augmented search and augmented exploration: two effective methods for information discovery in polystores that avoid middleware layers, abstract query languages, and shared data models.
Improved binary additive epsilon indicator for obtaining uniformly distributed solutions in multi-objective optimization

Several studies report that the solutions obtained by indicator-based evolutionary algorithm (IBEA) with the binary additive epsilon indicator are biased to specific regions in the objective space. This paper reveals the reason of the bias is due to the asymmetricity of the epsilon indicator without considering the dominance relationship. This paper also proposes a modified epsilon indicator to obtain uniformly distributed solutions in the objective space. The proposed indicator uses indicator value based on Chebyshev distance when the dominance relationship does not exist between two solutions. The results of the experiments show that the diversity of IBEA is enhanced by the proposed indicator on WFG5 problem.
Image Analysis in Technical Documentation

In the era of Big Data, manufacturing companies are overwhelmed by a lot of disorganized information: the large amount of digital content that is increasingly available in the manufacturing process makes the retrieval of accurate information a critical issue. In this context, and thanks also to the Industry 4.0 campaign, the Italian manufacturing industries have made a lot of effort to ameliorate their knowledge management system using the most recent technologies, like big data analysis and machine learning methods. This paper presents the on-going work done within the ADA project, with special emphasis on the specific image analysis work carried out to extract information from images contained in the so different document of the manufacturing companies, partners of the project.
Exploitation of multiple surrogate models in multi-point infill sampling strategies

This work presents interesting multi-point search algorithms exploiting several surrogate models, implemented in Minamo, the multi-disciplinary optimization platform of Cenaero. Many types of surrogate models are used in the literature with their own forces and weaknesses. More generally, each one models differently a given problem and provides its own representation of the reality. The idea of this paper is to exploit simultaneously different types of surrogate models in order to catch automatically their forces and to outshine some of their weaknesses. This strategy is based on a multi-point enrichment at each iteration, each candidate point being provided by one kind of surrogate model and/or criterion. This strategy can be tuned by selecting different infill criteria, based on different surrogate models, in order to improve more specifically different aspects such as feasibility, exploration and/or exploitation. The performance of this surrogate-based optimization framework is illustrated on well-known constrained benchmark problems available in the literature (such as GX-functions and MOPTA08 test cases). Good performance both in terms of identification of feasible regions and objective gains is demonstrated.
Exotic Compilers as a Malware Evasion Technique

The increasing complexity of new malware and the constant refinement of detection mechanisms are driving malware writers to rethink the malware development process. In this respect, compilers play a key role and can be used to implement evasion techniques able to defeat even the new generation of detection algorithms. In this paper we provide an overview of the endless battle between malware writers and detectors and we discuss some considerations on the benefits of using high level languages and even exotic compilers (e.g. single instruction compilers) in the process of writing malicious code.
Benchmarking surrogate-assisted genetic recommender systems

We propose a new approach for building recommender systems by adapting surrogate-assisted interactive genetic algorithms. A pool of user-evaluated items is used to construct an approximative model which serves as a surrogate fitness function in a genetic algorithm for optimizing new suggestions. The surrogate is used to recommend new items to the user, which are then evaluated according to the user's liking and subsequently removed from the search space. By updating the surrogate model after new recommendations have been evaluated by the user, we enable the model itself to evolve towards the user's preferences. In order to precisely evaluate the performance of that approach, the human's subjective evaluation is replaced by common continuous objective benchmark functions for evolutionary algorithms. The system's performance is compared to a conventional genetic algorithm and random search. We show that given a very limited amount of allowed evaluations on the true objective, our approach outperforms these baseline methods.
An experimental comparison of algebraic differential evolution using different generating sets

In this paper we provide a comparative empirical analysis of four different generating sets for the algebraic Differential Evolution for Permutations (DEP) applied to the Traveling Salesman Problem (TSP). In particular, DEP has been extended in order to use the reversal moves as generating set. Two different randomized decomposers are proposed for the reversal generators. The experiments have been conducted on a selected set of commonly adopted TSP instances, and the results show the newly proposed generating set leads to better performances with respect to other three generating sets based on alternative search moves.
An Exhaustive Review on Security Issues in Cloud Computing

The Cloud Computing is growing rapidly in the current IT industry. Cloud computing has become a buzzword in relation to Grid & Utility computing. It provides on demand services to customers and customers will pay for what they get. Various “Cloud Service Provider” such as Microsoft Azure, Google Web Services etc. enables the users to access the cloud in cost effective manner. However, security, privacy and integrity of data is a major concern. In this paper various security challenges have been identified and the survey briefs the comprehensive overview of various security issues in cloud computing. The classification of security issues in cloud computing have been studied. In this paper we have discussed security challenges in cloud computing and also list recommended methods available for addressing them in the literature.
GAIA: A Framework for Schema Mapping Reuse (extended abstract)

Schema mappings are widely used as a tool for data exchange and integration. However, although there are systems supporting data architects in the creation of mappings [5], designing them is still a time-consuming task. In this framework, given the overwhelming amount of “enterprise knowledge” stored in traditional data warehouses and in data lakes, reuse is an opportunity of increasing importance [1]. In particular, data transformation scenarios are often defined over schemas that are different in structure but similar in semantics. This is especially true if data sources, which are extremely heterogeneous, have to be mapped to a shared format. It follows that a great opportunity to reduce the effort of transformation design is to reuse existing schema mappings. Unfortunately, there is no obvious approach for this problem. Consider the following example.