Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Asthma Affect School Performance in Adolescents? Results From the Swedish Population–Based Birth Cohort BAMSE

S Nilsson, M Odling, N Andersson, A Bergstrom, I Kull. Pediatr Allergy Immunol. 2018;29(2):174–179 To examine whether asthma affects school performance in adolescents. In this study, 1715 adolescents were included from the Swedish population–based prospective birth cohort BAMSE, which consists of 4089 children born between 1994 and 1996 in the Stockholm area. These children were managed up to the age of 16 years with questionnaires and clinical examinations. The mean age of adolescents was 16.6 years at the time they answered their questionnaires and 15.9 years when receiving their final school grades. Asthma was defined as at least 4 episodes of wheeze or at least 1 episode …
A Deterministic Approach to Avoid Saddle Points

Loss functions with a large number of saddle points are one of the main obstacles to training many modern machine learning models. Gradient descent (GD) is a fundamental algorithm for machine learning and converges to a saddle point for certain initial data. We call the region formed by these initial values the "attraction region. " For quadratic functions, GD converges to a saddle point if the initial data is in a subspace of up to n-1 dimensions. In this paper, we prove that a small modification of the recently proposed Laplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317] contributes to avoiding saddle points without sacrificing the convergence rate of GD. In particular, we show that the dimension of the LSGD's attraction region is at most floor((n-1)/2) for a class of quadratic functions which is significantly smaller than GD's (n-1)-dimensional attraction region.
Enhancing Blood Glucose Prediction with Meal Absorption and Physical Exercise Information

Objective: Numerous glucose prediction algorithm have been proposed to empower type 1 diabetes (T1D) management. Most of these algorithms only account for input such as glucose, insulin and carbohydrate, which limits their performance. Here, we present a novel glucose prediction algorithm which, in addition to standard inputs, accounts for meal absorption and physical exercise information to enhance prediction accuracy. Methods: a compartmental model of glucose-insulin dynamics combined with a deconvolution technique for state estimation is employed for glucose prediction. In silico data corresponding from the 10 adult subjects of UVa-Padova simulator, and clinical data from 10 adults with T1D were used. Finally, a comparison against a validated glucose prediction algorithm based on a latent variable with exogenous input (LVX) model is provided. Results: For a prediction horizon of 60 minutes, accounting for meal absorption and physical exercise improved glucose forecasting accuracy. In particular, root mean square error (mg/dL) went from 26.68 to 23.89, p<0.001 (in silico data); and from 37.02 to 35.96, p<0.001 (clinical data - only meal information). Such improvement in accuracy was translated into significant improvements on hypoglycaemia and hyperglycaemia prediction. Finally, the performance of the proposed algorithm is statistically superior to that of the LVX algorithm (26.68 vs. 32.80, p<0.001 (in silico data); 37.02 vs. 49.17, p<0.01 (clinical data). Conclusion: Taking into account meal absorption and physical exercise information improves glucose prediction accuracy.
Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses

Recent research has shown that the integrity of sensor measurements can be violated through out-of-band signal injection attacks. These attacks target the conversion process from a physical quantity to an analog property—a process that fundamentally cannot be authenticated. Out-of-band signal injection attacks thus pose previously-unexplored security risks by exploiting hardware imperfections in the sensors themselves, or in their interfaces to microcontrollers. In response to the growing-yet-disjointed literature in the subject, this article presents the first survey of out-of-band signal injection attacks. It focuses on unifying their terminology and identifying commonalities in their causes and effects through a chronological, evolutionary, and thematic taxonomy of attacks. By highlighting cross-influences between different types of out-of-band signal injections, this paper underscores the need for a common language irrespective of the attack method. By placing attack and defense mechanisms in the wider context of their dual counterparts of side-channel leakage and electromagnetic interference, this study identifies common threads and gaps that can help guide and inform future research. Overall, the ever-increasing reliance on sensors embedded in everyday commodity devices necessitates that a stronger focus be placed on improving the security of such systems against out-of-band signal injection attacks.
The Asymptotic Complexity of Coded-BKW with Sieving Using Increasing Reduction Factors

The Learning with Errors problem (LWE) is one of the main candidates for post-quantum cryptography. At Asiacrypt 2017, coded-BKW with sieving, an algorithm combining the Blum-Kalai-Wasserman algorithm (BKW) with lattice sieving techniques, was proposed. In this paper, we improve that algorithm by using different reduction factors in different steps of the sieving part of the algorithm. In the Regev setting, where q = n2 and $\sigma = {n^{1.5}}/\left( {\sqrt {2\pi } \log _2^2n} \right)$, the asymptotic complexity is 20.8917n, improving the previously best complexity of 20.8927n. When a quantum computer is assumed or the number of samples is limited, we get a similar level of improvement.
A Lower Bound on the Error Exponent of Linear Block Codes over the Erasure Channel

A lower bound on the maximum likelihood (ML) decoding error exponent of linear block code ensembles, on the erasure channel, is developed. The lower bound turns to be positive, over an ensemble-specific interval of erasure probabilities, when the ensemble weight spectral shape function tends to a negative value as the fractional codeword weight tends to zero. For these ensembles we can therefore lower bound the block-wise ML decoding threshold. Two examples are presented, namely, linear random parity-check codes and fixed-rate Raptor codes with linear random precoders. While for the former a full analytical solution is possible, for the latter we can lower bound the ML decoding threshold on the erasure channel by simply solving a 2 × 2 system of nonlinear equations.
MSO+nabla is undecidable

This paper is about an extension of monadic second-order logic over the full binary tree, which has a quantifier saying ``almost surely a branch {\pi} \in {0, 1}^w satisfies a formula {\phi}({\pi})''. This logic was introduced by Michalewski and Mio; we call it MSO+nabla following notation of Shelah and Lehmann. The logic MSO+nabla subsumes many qualitative probabilistic formalisms, including qualitative probabilistic CTL, probabilistic LTL, or parity tree automata with probabilistic acceptance conditions. We show that it is undecidable to check if a given sentence of MSO+nabla is true in the full binary tree.
New Family of Stream Ciphers as Physically Clone-Resistant VLSI-Structures

A concept for creating a large class of lightweight stream ciphers as Key Stream Generators KSGs is presented. The resulting class-size exceeds 2323 possible different KSGs. If one unknown cipher from the KSG-class is randomly picked-up and stored irreversibly within a VLSI device, the device becomes physically hard-to-clone. The selected cipher is only usable by the device itself, therefore cloning it requires an invasive attack on that particular device. Being an unknown selection out of 2323 possible KSGs, the resulting cipher is seen as a Secret Unknown Cipher (SUC). The SUC concept was presented a decade ago as a digital alternative to the inconsistent traditional analog Physically Unclonable Functions (PUFs). This work presents one possible practical self-creation technique for such PUFs as hard-to-clone unknown KSGs usable to re-identify VLSI devices. The proposed sample cipher-structure is based on non-linear merging of randomly selected 16 Nonlinear Feedback Shift Registers (NLFSRs). The created KSGs exhibit linear complexities exceeding 281 and a period exceeding 2161. The worst-case device cloning time complexity approaches 2162. A simple lightweight identification protocol for physically identifying such SUC structures in FPGA-devices is presented. The required self-reconfiguring FPGAs for embedding such SUCs are not yet available, however, expected to emerge in the near future. The security analysis and hardware complexities of the resulting clone-resistant structures are evaluated and shown to offer scalable security levels to cope even with the post-quantum cryptography.
CommunityGAN: Community Detection with Generative Adversarial Nets

Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.
Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets

We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech. We lay the foundation of a universal model that is trained on multiple languages at once. As affect is shared across all languages, we are able to leverage shared information between languages and improve the overall performance for each one. We obtained an average improvement of 12.8% on emotion and 10.1% on personality when compared with the same model trained on each language only. It is end-to-end because we directly take narrow-band raw waveforms as input. This allows us to accept as input audio recorded from any source and to avoid the overhead and information loss of feature extraction. It outperforms a similar CNN using spectrograms as input by 12.8% for emotion and 6.3% for personality, based on F-scores. Analysis of the network parameters and layers activation shows that the network learns and extracts significant features in the first layer, in particular pitch, energy and contour variations. Subsequent convolutional layers instead capture language-specific representations through the analysis of supra-segmental features. Our model represents an important step for the development of a fully universal affect recognizer, able to recognize additional descriptors, such as stress, and for the future implementation into affective interactive systems.
Sky of blue, sea of green: a semiotic reading of the film Yellow Submarine

Abstract The Beatles’ film Yellow Submarine (1968) reflects conflicts between conventional society, represented by classical music, and rebellious youth culture, represented by other musical types, such as folk and pop (subsumed under the term ‘vernacular’). Taking their inspiration from the song ‘Yellow Submarine’ (Revolver, 1966), the filmmakers created a narrative for a psychedelic ‘hero’s journey’ from existing Beatles songs. This article discusses how the musical codes that symbolise different groups are used to mediate between divergent elements in both the film and contemporary society, by referring to such elements beyond the film as the Beatles’ comprehensive body of songs (which in itself forms a kind of mythology) and cultural events of the time. In Yellow Submarine, the Blue Meanies imprison Pepperland by immobilising all producers of music, whether ‘classical’ (the string quartet led by the elderly Lord Mayor) or ‘vernacular’ (Sgt. Pepper’s Lonely Hearts Club Band). The Beatles are able to free Pepperland by manipulating and ultimately uniting the musical codes – an idealistic message for the ‘real world’ to heed.
Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features

As the foundation of driverless vehicle and intelligent robots, Simultaneous Localization and Mapping(SLAM) has attracted much attention these days. However, non-geometric modules of traditional SLAM algorithms are limited by data association tasks and have become a bottleneck preventing the development of SLAM. To deal with such problems, many researchers seek to Deep Learning for help. But most of these studies are limited to virtual datasets or specific environments, and even sacrifice efficiency for accuracy. Thus, they are not practical enough. We propose DF-SLAM system that uses deep local feature descriptors obtained by the neural network as a substitute for traditional hand-made features. Experimental results demonstrate its improvements in efficiency and stability. DF-SLAM outperforms popular traditional SLAM systems in various scenes, including challenging scenes with intense illumination changes. Its versatility and mobility fit well into the need for exploring new environments. Since we adopt a shallow network to extract local descriptors and remain others the same as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
Pareto optimal coalitions of fixed size

We tackle the problem of partitioning players into groups of fixed size, such as allocating eligible students to shared dormitory rooms. Each student submits preferences over the other individual students. We study several settings, which differ in the size of the rooms to be filled, the orderedness or completeness of the preferences, and the way of calculating the value of a coalition---based on the best or worst roommate in the coalition. In all cases, we determine the complexity of deciding the existence, and then finding a Pareto optimal assignment, and the complexity of verifying Pareto optimality for a given assignment.
Horizontal transfer between loose compartments stabilizes replication of fragmented ribozymes

The emergence of replicases that can replicate themselves is a central issue in the origin of life. Recent experiments suggest that such replicases can be realized if an RNA polymerase ribozyme is divided into fragments short enough to be replicable by the ribozyme and if these fragments self-assemble into a functional ribozyme. However, the continued self-replication of such replicases requires that the production of every essential fragment be balanced and sustained. Here, we use mathematical modeling to investigate whether and under what conditions fragmented replicases achieve continued self-replication. We first show that under a simple batch condition, the replicases fail to display continued self-replication owing to positive feedback inherent in these replicases. This positive feedback inevitably biases replication toward a subset of fragments, so that the replicases eventually fail to sustain the production of all essential fragments. We then show that this inherent instability can be resolved by small rates of random content exchange between loose compartments (i.e., horizontal transfer). In this case, the balanced production of all fragments is achieved through negative frequency-dependent selection operating in the population dynamics of compartments. The horizontal transfer also ensures the presence of all essential fragments in each compartment, sustaining self-replication. Taken together, our results underline compartmentalization and horizontal transfer in the origin of the first self-replicating replicases.
Population pharmacokinetics of imatinib and the role of alpha-acid glycoprotein.

AIMS The aims of this observational study were to assess the variability in imatinib pharmacokinetics and to explore the relationship between its disposition and various biological covariates, especially plasma alpha1-acid glycoprotein concentrations. METHODS A population pharmacokinetic analysis was performed using NONMEM based on 321 plasma samples from 59 patients with either chronic myeloid leukaemia or gastrointestinal stromal tumours. The influence of covariates on oral clearance and volume of distribution was examined. Furthermore, the in vivo intracellular pharmacokinetics of imatinib was explored in five patients. RESULTS A one-compartment model with first-order absorption appropriately described the data, giving a mean (+/-SEM) oral clearance of 14.3 l h-1 (+/-1.0) and a volume of distribution of 347 l (+/-62). Oral clearance was influenced by body weight, age, sex and disease diagnosis. A large proportion of the interindividual variability (36% of clearance and 63% of volume of distribution) remained unexplained by these demographic covariates. Plasma alpha1-acid glycoprotein concentrations had a marked influence on total imatinib concentrations. Moreover, we observed an intra/extracellular ratio of 8, suggesting substantial uptake of the drug into the target cells. CONCLUSION Because of the high pharmacokinetic variability of imatinib and the reported relationships between its plasma concentration and efficacy and toxicity, the usefulness of therapeutic drug monitoring as an aid to optimizing therapy should be further investigated. Ideally, such an approach should take account of either circulating alpha1-acid glycoprotein concentrations or free imatinib concentrations.
Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding

Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as image captioning and retrieval were designed to test this ability, but come with complex evaluation measures that gauge various other abilities and biases simultaneously. This paper presents an alternative evaluation task for visual-grounding systems: given a caption the system is asked to select the image that best matches the caption from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task is not only interpretable, but also measures the ability to relate fine-grained text content in the caption to visual content in the images. We gathered a BISON dataset that complements the COCO Captions dataset and used this dataset in auxiliary evaluations of captioning and caption-based retrieval systems. While captioning measures suggest visual grounding systems outperform humans, BISON shows that these systems are still far away from human performance.
MOROCO: The Moldavian and Romanian Dialectal Corpus

In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.