Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting tumor-suppressing genes in cancer via clustering the developmental stage gene expression profile

In this paper we study the problem of predicting which genes are likely to have a role in tumor-suppression in lung and colorectal cancer. Mutation frequencies alone cannot serve to differentiate between ‘drivers’ (mutations that cause cancer) and ‘passengers’ (mutations that are caused by cancer) some other features must be added. Our hypothesis is that the developmental stage gene expression profile provides one such additional feature, that can potentially serve to differentiate between drivers and passengers. The developmental stage gene expression profile refers to the seven-dimensional vector of the gene's expression, as found in the Unigene database [15].
A new locally linear KNN method with an improved marginal Fisher analysis for image classification

This paper presents a novel locally linear KNN method with an improved marginal Fisher analysis for image classification. First, the discriminating color space (DCS), which is derived by discriminant analysis of the red, green, and blue primary colors, is integrated into the proposed method. Second, an improved marginal Fisher analysis (IMFA) applies an eigenvalue spectrum analysis to improve the generalization performance of the marginal Fisher analysis method. Third, a new locally linear KNN classifier (LLKNN), which represents the test image as a linear combination of its k nearest training images and assigns it to the class with the largest sum of weights, is presented to improve upon the traditional KNN approach. The effectiveness of the proposed method is evaluated on two representative datasets, namely the AR face image data set and the ETH-80 image data set. Experimental results show that the proposed method performs better than some representative state-of-the-art methods.
Pattern Recognition

In spite of the advantages of biometrics as an identity veriﬁcation technology, some concerns have been raised due to the high sensitivity of biometric data: any information leakage poses a severe privacy threat. To solve those issues only protected templates should be stored or exchanged for recognition purposes. In order to improve the performance and achieve more secure and privacy-preserving systems, we pro-pose a general framework for multi-biometric template protection based on homomorphic probabilistic encryption, where only encrypted data is handled. Three fusion levels are thoroughly analysed, showing that all requirements described in the ISO/IEC 24745 standard on biometric data protection are met with no accuracy degradation. Furthermore, even if all the process is carried out in the encrypted domain, no encryptions are necessary during veriﬁcation, thereby allowing an eﬃcient veriﬁcation which can be deployed for real-time applications. Finally, experiments are carried out on a reproducible research framework. The results obtained show high accuracy rates, reaching EERs as low as 0.12%, and requiring protected templates comprising 200 KB.
Pattern Recognition

Cross-modal retrieval aims at retrieving relevant points across different modalities, such as retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the heterogeneous gap across diverse modalities. To overcome this challenge, we propose a novel method termed as Cross-modal discriminant Adversarial Network (CAN). Taking bi-modal data as a showcase, CAN consists of two parallel modality-speciﬁc generators, two modality-speciﬁc discriminators, and a Cross-modal Discriminant Mechanism (CDM). To be speciﬁc, the generators project diverse modalities into a latent cross-modal discriminant space. Meanwhile, the discriminators compete against the generators to alleviate the heterogeneous discrepancy in this space, i
Data Quality Requirements of Collaborative Business Processes

High-quality data is not just a competitive factor for individual companies but also an enabler for collaboration in business networks. The paper takes a cross-company perspective on data quality and identifies requirements collaborative business processes pose to data quality. A qualitative content analysis on Business Networking case studies is conducted. The results show which combinations of data classes (e.g. order data, forecast data) and quality dimensions (e.g. business rule conformity) are crucial for the different collaborative business processes in business networks. The paper interprets the results and closes with a discussion of current data quality trends for collaborative processes.
The effects of nicotine and non‐nicotine smoking factors on working memory and associated brain function

Smoking abstinence impairs executive function, which may promote continued smoking behavior and relapse. The differential influence of nicotine and non‐nicotine (i.e. sensory, motor) smoking factors and related neural substrates is not known. In a fully factorial, within‐subjects design, 33 smokers underwent fMRI scanning following 24 hours of wearing a nicotine or placebo patch while smoking very low nicotine content cigarettes or remaining abstinent from smoking. During scanning, blood oxygenation level‐dependent (BOLD) signal was acquired while participants performed a verbal N‐back task. Following 24‐hour placebo (versus nicotine) administration, accuracy on the N‐back task was significantly worse and task‐related BOLD signal lower in dorsomedial frontal cortex. These effects were observed irrespective of smoking. Our data provide novel evidence that abstinence‐induced deficits in working memory and changes in underlying brain function are due in large part to abstinence from nicotine compared with non‐nicotine factors. This work has implications both for designing interventions that target abstinence‐induced cognitive deficits and for nicotine‐reduction policy.
Single‐ and two‐photon spectral imaging of intrinsic fluorescence of transformed human hepatocytes

Autofluorescence (AF) originating from the cytoplasmic region of mammalian cells has been thoroughly investigated; however, AF from plasma membranes of viable intact cells is less well known, and has been mentioned only in a few older publications. Herein, we report results describing single‐ and two‐photon spectral properties of a strong yellowish‐green AF confined to the plasma‐membrane region of transformed human hepatocytes (HepG2) grown in vitro as small three‐dimensional aggregates or as monolayers. The excitation–emission characteristics of the membrane AF indicate that it may originate from a flavin derivative. Furthermore, the AF was closely associated with the plasma membranes of HepG2 cells, and its presence and intensity were dependent on cell metabolic state, membrane integrity and presence of reducing agents. This AF could be detected both in live intact cells and in formaldehyde‐fixed cells. Microsc. Res. Tech., 2007. © 2007 Wiley‐Liss, Inc.
Extracting comprehensible models from trained neural networks

Although neural networks have been used to develop highly accurate classifiers in numerous real-world problem domains, the models they learn are notoriously difficult to understand. This thesis investigates the task of extracting comprehensible models from trained neural networks, thereby alleviating this limitation. The primary contribution of the thesis is an algorithm that overcomes the significant limitations of previous methods by taking a novel approach to the task of extracting comprehensible models from trained networks. This algorithm, called Trepan, views the task as an inductive learning problem. Given a trained network, or any other learned model, Trepan uses queries to induce a decision tree that approximates the function represented by the model. Unlike previous work in this area, Trepan is broadly applicable as well as scalable to large networks and problems with high-dimensional input spaces. The thesis presents experiments that evaluate Trepan by applying it to individual networks and to ensembles of neural networks trained in classification, regression, and reinforcement-learning domains. These experiments demonstrate that Trepan is able to extract decision trees that are comprehensible, yet maintain high levels of fidelity to their respective networks. In problem domains in which neural networks provide superior predictive accuracy to conventional decision tree algorithms, the trees extracted by Trepan also exhibit superior accuracy, but are comparable in terms of complexity, to the trees learned directly from the training data. A secondary contribution of this thesis is an algorithm, called BBP, that constructively induces simple neural networks. The motivation underlying this algorithm is similar to that for Trepan: to learn comprehensible models in problem domains in which neural networks have an especially appropriate inductive bias. The BBP algorithm, which is based on a hypothesis-boosting method, learns perceptrons that have relatively few connections. This algorithm provides an appealing combination of strengths: it provides learnability guarantees for a fairly natural class of target functions; it provides good predictive accuracy in a variety of problem domains; and it constructs syntactically simple models, thereby facilitating human comprehension of what it has learned. These algorithms provide mechanisms for improving the understanding of what a trained neural network has learned.
ANNALES DE L’INSTITUT FOURIER

— We establish the Schwarz Reﬂection Principle for J -complex discs attached to a real analytic J -totally real submanifold of an almost complex manifold with real analytic J . We also prove the precise boundary regularity and derive the precise convergence in Gromov compactness theorem in C k,α -classes. Résumé. — Nous établissons le Principe de Symétrie de Schwarz pour les disques complexes attachés à une sous-variété analytique réelle et totalement réelle d’une variété presque complexe munie d’une structure presque complexe analytique réelle. Nous prouvons également la régularité au bord précise de ces disques et nous en déduisons la convergence exacte dans le théorème de compacité de Gromov dans les classes C k,α .
Scaling nitrogen retention from trees to forests through succession

We seek to understand how the ability of trees to acquire and retain nitrogen (N) changes throughout their lifetimes. This capacity enables trees to act as carbon (C) sinks individually and collectively in forest ecosystems over successional time scales. We evaluate how properties that govern nutrient retention change with tree size and forest age, and how allometric relationships scale up to influence ecosystem-level patterns of N cycling and retention. Most generally, we hypothesized that changes in N uptake and recycling efficiency with increasing tree size would vary with forest age and N availability. Additionally, we evaluated changes in ecosystem-level C and N accumulation throughout secondary forest succession following clear-cut logging disturbances in an effort to understand how N limitation may become expressed over time and interact with forest successional dynamics. Our findings highlight the importance of large trees in ecosystem N cycling and growth. We find that increasing mass growth rates are matched by an increasing capacity to acquire and retain N without necessitating increases in growth efficiency. Research findings indicate that mortality of single trees may hold profound consequences for standlevel N retention in addition to C storage. At the ecosystem scale, we find N accumulation and limitation are dynamic processes that fluctuate in strength and source over forest succession, and that ecosystem accumulation of N was driven predominately by increasing N in plant biomass rather than in soil pools.
A New Fuzzy-Based Spatial Model for Robot Navigation among Dynamic Obstacles

This paper presents a new approach based on artificial potential fields (APF) method which provides simple and effective motion planners for practical path planning in fully dynamic environments. We have exploited the fuzzy modeling to define fuzzy artificial potential fields (FAPF) which provides a real-time and flexible path planning, in contrast with classical approaches which make use of analytic harmonic functions to solve the navigation problem. It is shown that FAPF paves a way to merge both global and local path planning strategies. In this particular application, the proposed fuzzy system is used to obtain the gradient of the harmonic functions via weighting the membership functions of obstacles and goal. Our proposed technique 1) constructs a "good" fuzzy model for APF; 2) efficiently escapes local minima occurred in path planning using virtual obstacle concept; and 3) can combine the advantages of both the local and global approaches. A path planner based on this technique has been implemented. Simulations show that the planner is both very fast and capable of handling the local minima which can trap mobile robots before reaching the goal. Variety of problems which are far beyond the capabilities of previously developed planners is solved.
Post nonlinear blind source separation by geometric linearization

We present a novel geometric approach to the popular post nonlinear (PNL) BSS problem. A PNL mixing system includes two stages: a linear mixing followed by a nonlinear transformation. In our method, the process to linearize the nonlinear observed signals, the most critical task in PNL model, is carried out by a geometric transformation. The basic idea is that in a multi-dimensional space, a PNL mixture is represented by a nonlinear surface while a linear mixture is represented by a plane. Thus, by transforming a PNL's representing nonlinear surface to a plane, the PNL mixture can be linearized. The hidden sources are then estimated from linearized signals by a linear BSS algorithm. Experiments show promising performance of our approach.
Piperacillin-Induced Immune Hemolytic Anemia in an Adult with Cystic Fibrosis

We report a case of drug-induced immune hemolytic anemia (DIIHA) in an adult female with cystic fibrosis (CF), complicating routine treatment of a pulmonary exacerbation with intravenous piperacillin-tazobactam. Workup revealed a positive direct antiglobulin test (DAT) due to red blood cell (RBC)-bound IgG and C3 and piperacillin antibodies detectable in the patient's serum. The potential influence of CF transmembrane conductance regulator mutations on the severity of DIIHA is discussed. This report illustrates the importance of early identification of DIIHA, a rare complication of a commonly utilized medication in CF.
Development of microbial community during primary succession in areas degraded by mining activities

Together with plants, soil microbial communities play an essential role in the development of stable ecosystems on degraded lands, such as postmining spoil heaps. Our study addressed concurrent development of the vegetation and soil fungal and bacterial communities in the course of primary succession in a brown coal mine spoil deposit area in the Czech Republic across a chronosequence spanning 54 years. During succession, the plant communities changed from sparse plants over grassland and shrubland into a forest, becoming substantially more diverse with time. Microbial biomass increased until the 21st year of ecosystem development and later decreased. Although there was a close association between fungi and vegetation, with fungi mirroring the differences in plant community assemblages, the development of the bacterial community was different. The early succession community in the barren nonvegetated soil largely differed from that in the older sites, especially in its high abundance of autotrophic and free‐living N2‐fixing bacteria. Later in succession, bacterial community changes were minor and reflected the chemical parameters of the soil, including pH, which also showed a minor change with time. Our results show that complex forest ecosystems developed over 54 years on the originally barren soil of the temperate zone and indicate an important role of bacteria in the initial stage of soil development. Although the arrival of vegetation affects substantially fungal as well as bacterial communities, it is mainly fungi that respond to the ongoing development of vegetation.
An Approach for Step-By-Step Query Refinement in the Ontology-Based Information Retrieval

In this paper we present a comprehensive approach for the refinement of ontology-based queries, which is based on incrementally (step-by-step) and interactively tailoring a query to the current information needs of a user, whereas these needs are implicitly and on-line elicited by analysing the user's behaviour during the searching process. The gap between a user's need and his query is quantified by measuring several types of query ambiguities. Consequently, in the refinement process a user is provided with a ranked list of refinements, which leads to a decrease of some of these ambiguities. Moreover, by exploiting the ontology background, the approach supports finding "similar" results that can help a user to satisfy his information need.
UvA-DARE (Digital Academic Repository) Linux containers networking: performance and scalability of kernel modules

—Linux container virtualisation is gaining momentum as lightweight technology to support cloud and distributed computing. Applications relying on container architectures might at times rely on inter-container communication, and container networking solutions are emerging to address this need. Contain-ers can be networked together as part of an overlay network, or with actual links from the container to the network via kernel modules. Most overlay solutions are not quite production ready yet; on the other hand kernel modules that can link a container to the network are much more mature. We benchmarked three kernel modules: veth, macvlan and ipvlan , to quantify their respective raw TCP and UDP performance and scalability. Our results show that the macvlan kernel module outperforms all other solutions in raw performance. All kernel modules seem to provide sufﬁcient scalability to be deployed effectively in multi-containers environments.
Classification of Electrocardiogram Signals with Deep Belief Networks

This paper introduces an electrocardiogram beat classification method based on deep belief networks. This method includes two parts: feature extraction and classification. In the feature extraction part, features are extracted from the original electrocardiogram signal: including features extracted by deep belief networks and timing interval features. Several classifiers are selected to classify the electrocardiogram beat, and nonlinear support vector machine with Gaussian kernel achieves the best classification accuracy, reaching 98.49. Compared with other similar methods on electrocardiogram beat classification, our method can improve the recognition performance of some types of electrocardiogram beats.
Fusion of 3D B-spline surface patches reconstructed from image sequences

This paper considers the problem of merging a set of distinct three dimensional B-spline surface patches, which are reconstructed from observations of the motion of occluding contours in image sequences. We propose an original method of fusing these partially overlapping patches in order to obtain a whole surface. This approach is based on a triangular mesh and surface interpolation through regularized uniform bicubic B-spline surface patches. Experimental results are presented for both synthetic and real data.
Relay-Aided Successive Aligned Interference Cancellation for Wireless $X$ Networks With Full-Duplex Relays

This paper proposes a novel relay-aided successive aligned interference cancellation (RaSAIC) method for a <inline-formula><tex-math notation="LaTeX">$\text{2}\times K$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> channel with <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> full-duplex relays, each of which is fully connected to the channel. The core idea of the RaSAIC is to apply a cooperative relays' precoding technique that aligns interference signals at unintended receivers while creating an Alamouti structure for the desired signals. With the proposed cooperative relay transmission technique, it is shown that both the optimal sum degree of freedom (sum-DoF) gain of <inline-formula><tex-math notation="LaTeX">$\text{2}K/(K+\text{1})$</tex-math></inline-formula> and the diversity gain of two are achievable for both constant and time-varying channels when the numbers of relays' antennas are enough to meet the derived conditions, without requiring channel state information (CSI) at transmitters, but with global CSI at relays. Furthermore, it is demonstrated that the same sum-DoF and diversity gain are attainable, even in the presence of interrelay interference due to a full-duplex relaying operation if the memory at the relay nodes is exploited in the proposed manner.
TORINO Repository ISTITUZIONALE

—The Internet of Things (IoT), coupled with the edge computing paradigm, is enabling several pervasive networked applications with stringent real-time requirements, such as telemedicine and haptic telecommunications. Recent advances in network virtualization and artiﬁcial intelligence are helping solve network latency and capacity problems, learning from several states of the network stack. However, despite such advances, a network architecture able to meet the demands of next-generation networked applications with stringent real-time requirements still has untackled challenges. In this paper, we argue that only using network (or transport) layer information to predict trafﬁc evolution and other network states may be insufﬁcient, and a more holistic approach that considers predictions of application-layer states is needed to repair the inefﬁciencies of the TCP/IP architecture. Based on this intuition, we present the design and implementation of Reparo. At its core, the design of our solution is based on the detection of a packet loss and its restoration using a Hidden Markov Model (HMM) empowered with adversarial autoencoders. In our evaluation, we considered a telemedicine use case, speciﬁcally a telepathology session, in which a microscope is controlled remotely in real-time to assess histological imagery. Our results conﬁrm that the use of adversarial autoencoders enhances the accuracy of the prediction method satisfying our telemedicine application’s requirements with a notable improvement in terms of throughput and latency perceived by the user.
Effectiveness of Hypochlorous Acid to Reduce the Biofilms on Titanium Alloy Surfaces in Vitro

Chemotherapeutic agents have been used as an adjunct to mechanical debridement for peri-implantitis treatment. The present in vitro study evaluated and compared the effectiveness of hypochlorous acid (HOCl), sodium hypochlorite (NaOCl), and chlorhexidine (CHX) at eliminating Gram-negative (E. coli and P. gingivalis) and Gram-positive (E. faecalis and S. sanguinis) bacteria. The effect of irrigating volume and exposure time on the antimicrobial efficacy of HOCl was evaluated, and a durability analysis was completed. Live/dead staining, morphology observation, alamarBlue assay, and lipopolysaccharide (LPS) detection were examined on grit-blasted and biofilm-contaminated titanium alloy discs after treatment with the three chemotherapeutic agents. The results indicated that HOCl exhibited better antibacterial efficacy with increasing irrigating volumes. HOCl achieved greater antibacterial efficacy as treatment time was increased. A decrease in antimicrobial effectiveness was observed when HOCl was unsealed and left in contact with the air. All the irrigants showed antibacterial activity and killed the majority of bacteria on the titanium alloy surfaces of biofilm-contaminated implants. Moreover, HOCl significantly lowered the LPS concentration of P. gingivalis when compared with NaOCl and CHX. Thus, a HOCl antiseptic may be effective for cleaning biofilm-contaminated implant surfaces.