Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel-based Translations of Convolutional Networks

Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translation of Convolutional Neural Networks (ConvNets) into their kernel-based counterparts, Convolutional Kernel Networks (CKNs), and demonstrate that this perception is unfounded both formally and empirically. We show that, given a Convolutional Neural Network, we can design a corresponding Convolutional Kernel Network, easily trainable using a new stochastic gradient algorithm based on an accurate gradient computation, that performs on par with its Convolutional Neural Network counterpart. We present experimental results supporting our claims on landmark ConvNet architectures comparing each ConvNet to its CKN counterpart over several parameter settings.
Information Set Decoding in the Lee Metric with Applications to Cryptography

<p style='text-indent:20px;'>We convert Stern's information set decoding (ISD) algorithm to the ring <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{Z}/4 \mathbb{Z} $\end{document}</tex-math></inline-formula> equipped with the Lee metric. Moreover, we set up the general framework for a McEliece and a Niederreiter cryptosystem over this ring. The complexity of the ISD algorithm determines the minimum key size in these cryptosystems for a given security level. We show that using Lee metric codes can substantially decrease the key size, compared to Hamming metric codes. In the end we explain how our results can be generalized to other Galois rings <inline-formula><tex-math id="M2">\begin{document}$ \mathbb{Z}/p^s\mathbb{Z} $\end{document}</tex-math></inline-formula>. </p>
Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations

With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information. Most algorithms cannot take information from multiple views into considerations and fail to achieve desirable performance in most situations. For many applications, such as image retrieval, face recognition, etc., an appropriate distance metric can better reflect the similarities between various samples. Therefore, how to construct a good distance metric learning methods which can deal with multiview data has been an important topic during the last decade. In this paper, we proposed a novel algorithm named Self-weighted Multiview Metric Learning (SM2L) which can finish this task by maximizing the cross correlations between different views. Furthermore, because multiple views have different contributions to the learning procedure of SM2L, we adopt a self-weighted learning framework to assign multiple views with different weights. Various experiments on benchmark datasets can verify the performance of our proposed method.
An Evaluation of Bitcoin Address Classification based on Transaction History Summarization

Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payments, investments, gambling, and even money laundering in the past decade. Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses. These features include various high orders of moments of transaction time (represented by block height) which summarizes the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labeling category data set. The experimental evaluation shows that these features have improved the performance of Bitcoin address classification significantly. We evaluate the results under eight classifiers and achieve the highest Micro-Fl /Macro-F1 of 87% /86% with LightGBM.
Category Decoding of Visual Stimuli From Human Brain Activity Using a Bidirectional Recurrent Neural Network to Simulate Bidirectional Information Flows in Human Visual Cortices

Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) has had many achievements with the rapid development of deep network computation. In the human vision system, when people process the perceived visual content, visual information flows from primary visual cortices to high-level visual cortices and also vice versa based on the bottom-up and top-down manners, respectively. Inspired by the bidirectional information flows, we proposed a bidirectional recurrent neural network (BRNN)-based method to decode the corresponding categories from fMRI data. The forward and backward directions in the BRNN module characterized the bottom-up and top-down manners, respectively. The proposed method regarded the selected voxels in each visual area (V1, V2, V3, V4, and LO) as one node of the space sequence and fed it into the BRNN module, then combined the output of the BRNN module to decode categories with the subsequent fully connected softmax layer. This new method can use the hierarchical information representations and bidirectional information flows in human visual cortices more efficiently. Experiments demonstrated that our method could improve the accuracy of the three-level category decoding. Comparative analysis validated and revealed that correlative representations of categories were included in visual cortices because of the bidirectional information flows, in addition to the hierarchical, distributed, and complementary representations that accorded with previous studies.
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels

Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions. PENCIL is independent of the backbone network structure and does not need an auxiliary clean dataset or prior information about noise, thus it is more general and robust than existing methods and is easy to apply. PENCIL outperformed previous state-of-the-art methods by large margins on both synthetic and real-world datasets with different noise types and noise rates. Experiments show that PENCIL is robust on clean datasets, too.
Dynamic Deep Networks for Retinal Vessel Segmentation

Deep learning has recently yielded impressive gains in retinal vessel segmentation. However, state-of-the-art methods tend to be conservative, favoring precision over recall. Thus, they tend to under-segment faint vessels, underestimate the width of thicker vessels, or even miss entire vessels. To address this limitation, we propose a stochastic training scheme for deep neural networks that robustly balances precision and recall. First, we train our deep networks with dynamic class weights in the loss function that fluctuate during each training iteration. This stochastic approach–which we believe is applicable to many other machine learning problems–forces the network to learn a balanced classification. Second, we decouple the segmentation process into two steps. In the first half of our pipeline, we estimate the likelihood of every pixel and then use these likelihoods to segment pixels that are clearly vessel or background. In the latter part of our pipeline, we use a second network to classify the ambiguous regions in the image. Our proposed method obtained state-of-the-art results on five retinal datasets—DRIVE, STARE, CHASE-DB, AV-WIDE, and VEVIO—by learning a robust balance between false positive and false negative rates. Our novel training paradigm makes a neural network more robust to inter-sample differences in class ratios, which we believe will prove particularly effective for settings with sparse training data, such as medical image analysis. In addition, we are the first to report segmentation results on the AV-WIDE dataset, and we have made the ground-truth annotations for this dataset publicly available. An implementation of this work can be found at https://github.com/sraashis/deepdyn.
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication

Future in-vehicular networks will be based on Ethernet. The IEEE Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time requirements in future car communication. Software-Defined Networking (SDN) extends the Ethernet control plane with a programming option that can add much value to the resilience, security, and adaptivity of the automotive environment. In this work, we derive a first concept for combining Software-Defined Networking with Time-Sensitive Networking along with an initial evaluation. Our measurements are performed via a simulation that investigates whether an SDN architecture is suitable for time-critical applications in the car. Our findings indicate that the control overhead of SDN can be added without a delay penalty for the TSN traffic when protocols are mapped properly.
The Simpler the Better: Constant Velocity for Pedestrian Motion Prediction

Pedestrian motion prediction is a fundamental task for autonomous robots and vehicles to operate safely. In recent years many complex models have been proposed to address this problem. While complex models can be justiﬁed, simple models should be preferred given the same or better performance. In this work we show that a simple Constant Ve-locity Model can achieve competitive performance on this task. We evaluate the Constant Velocity Model using two popular benchmark datasets for pedestrian motion prediction and show that it outperforms state-of-the-art models and several common baselines. The success of this model indicates that either neural networks are not able to make use of the additional information they are provided with, or it is not as relevant as commonly believed. Therefore, we analyze how neural networks process this information and how it impacts their predictions. Our analysis shows that neural networks implicitly learn environmental priors that have a negative impact on their generalization capability, most of the pedestrian’s motion history is ignored and interactions – while happening – are too complex to predict. These ﬁndings explain the success of the Constant Velocity Model and lead to a better understanding of the problem at hand.
Causal Discovery from Heterogeneous/Nonstationary Data

It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the `driving force' of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.
Bombyxin (Bombyx Insulin-Like Peptide) Increases the Respiration Rate Through Facilitation of Carbohydrate Catabolism in Bombyx mori

Bombyxin-II, an insulin-like peptide of the silkmoth Bombyx mori, has been shown to reduce both the trehalose concentration in the hemolymph and the glycogen content in some tissues of B. mori larvae. However, little is known about how these storage carbohydrates are utilized. To address this question, the effects of bombyxin-II injection into Bombyx larvae on the tissue lipid level, respiration rate, and glycolytic activity of tissues were investigated. Bombyxin-II did not affect lipid accumulation in the hemolymph and fat body, while it increased the rate of oxygen consumption and increased the content of fructose 2, 6-bisphosphate, a potent activator of glycolysis, in the gonads, imaginal discs, and midgut. These results suggest that bombyxin facilitates cellular energy production thereby supporting the tissue growth of insects.
Vitamin D and its low calcemic analogs modulate the anticancer properties of cisplatin and dacarbazine in the human melanoma A375 cell line

Melanoma represents a significant challenge in cancer treatment due to the high drug resistance of melanomas and the patient mortality rate. This study presents data indicating that nanomolar concentrations of the hormonally active form of vitamin D, 1α,25-dihydroxyvitamin D3 [1α,25(OH)2D3], its non-calcemic analogues 20S-hydroxyvitamin D3 and 21-hydroxypregnacalciferol, as well as the low-calcemic synthetic analog calcipotriol, modulate the efficacy of the anticancer drugs cisplatin and dacarbazine. It was observed that vitamin D analogs sensitized melanoma A375 cells to hydrogen peroxide used as an inducer of oxidative stress. On the other hand, only 1α,25(OH)2D3 resulted in a minor, but significant effect on the proliferation of melanoma cells treated simultaneously with dacarbazine, but not cisplatin. Notably, cisplatin (300 µM) exhibited a higher overall antiproliferative activity than dacarbazine. Cisplatin treatment of melanoma cells resulted in an induction of apoptosis as demonstrated by flow cytometry (accumulation of cells at the subG1 phase of the cell cycle), whereas dacarbazine caused G1/G0 cell cycle arrest, with the effects being improved by pre-treatment with vitamin D analogs. Treatment with cisplatin resulted in an initial increase in the level of reactive oxygen species (ROS). Dacarbazine caused transient stimulation of ROS levels and the mitochondrial membrane potential (Δψm) (after 1 or 3 h of treatment, respectively), but the effect was not detectable following prolonged (24 h) incubation with the drug. Vitamin D exhibited modulatory effects on the cells treated with dacarbazine, decreasing the half maximal inhibitory concentration (IC50) for the drug, stimulating G1/G0 arrest and causing a marked decrease in Δψm. Finally, cisplatin, dacarbazine and 1α,25(OH)2D3 displayed modulatory effects on the expression of ROS and vitamin D-associated genes in the melanoma A375 cells. In conclusion, nanomolar concentrations of 1,25(OH)2D3 only had minor effects on the proliferation of melanoma cells treated with dacarbazine, decreasing the relative IC50 value. However, co-treatment with vitamin D analogs resulted in the modulation of cell cycle and ROS responses, and affected gene expression, suggesting possible crosstalk between the signaling pathways of vitamin D and the anticancer drugs used in this study.
Generalizability is not optional: insights from a cross-cultural study of social discounting

Current scientific reforms focus more on solutions to the problem of reliability (e.g. direct replications) than generalizability. Here, we use a cross-cultural study of social discounting to illustrate the utility of a complementary focus on generalizability across diverse human populations. Social discounting is the tendency to sacrifice more for socially close individuals—a phenomenon replicated across countries and laboratories. Yet, when adapting a typical protocol to low-literacy, resource-scarce settings in Bangladesh and Indonesia, we find no independent effect of social distance on generosity, despite still documenting this effect among US participants. Several reliability and validity checks suggest that methodological issues alone cannot explain this finding. These results illustrate why we must complement replication efforts with investment in strong checks on generalizability. By failing to do so, we risk developing theories of human nature that reliably explain behaviour among only a thin slice of humanity.
Investigating Fast Mapping Task Components: No Evidence for the Role of Semantic Referent nor Semantic Inference in Healthy Adults

Fast mapping (FM) is an incidental learning process that is hypothesized to allow rapid, cortical-based memory formation, independent of the normal, hippocampally dependent episodic memory system. It is believed to underlie the rapid vocabulary learning in infants that occurs separately from intentional memorisation strategies. Interest in adult FM learning was stimulated by a report in which adults with amnesia following hippocampal damage showed a normal ability to learn new object-name associations after an incidental FM task, despite their impaired memory under a conventional intentional memorization task. This remarkable finding has important implications for memory rehabilitation, and has led to a number of neuropsychological and neuroimaging studies in other patients and controls. Given this growing interest in adult FM, we conducted four behavioural experiments with healthy adults (N = 24 young or older adults in Experiments 1–3 using within-participant designs; N = 195 young adults in Experiment 4 using a between-participant design) that attempted to dissect which component(s) of the FM task are important for memory. Two key components of the FM task have been claimed to support FM learning: (1) provision of a known semantic referent and (2) requirement that the new association be inferred. Experiment 1 provided no evidence that removing the semantic referent impaired memory performance, while Experiment 2 provided no evidence that removing the semantic inference impaired performance. Experiment 3 was a replication of Experiment 2 with older participants, based on the hypothesis (from studies of amnesic individuals) that FM would be more effective following the hippocampal atrophy typical of increasing age, but again found no evidence that semantic inference is beneficial. Given potential concerns about contamination between tasks when each participant performed multiple variants of the FM task, we ran a final between-participant design in which each participant only ever did one condition. Despite 80% power and despite being able to detect better memory following intentional memorization in the explicit encoding (EE) control condition than in each of the FM conditions, we again found no evidence of differences between any FM conditions. We conclude that there is no evidence that the components hypothesized to be critical for FM are relevant to healthy adults.
Language structure is influenced by the number of speakers but seemingly not by the proportion of non-native speakers

Large-scale empirical evidence indicates a fascinating statistical relationship between the estimated number of language users and its linguistic and statistical structure. In this context, the linguistic niche hypothesis argues that this relationship reflects a negative selection against morphological paradigms that are hard to learn for adults, because languages with a large number of speakers are assumed to be typically spoken and learned by greater proportions of adults. In this paper, this conjecture is tested empirically for more than 2000 languages. The results question the idea of the impact of non-native speakers on the grammatical and statistical structure of languages, as it is demonstrated that the relative proportion of non-native speakers does not significantly correlate with either morphological or information-theoretic complexity. While it thus seems that large numbers of adult learners/speakers do not affect the (grammatical or statistical) structure of a language, the results suggest that there is indeed a relationship between the number of speakers and (especially) information-theoretic complexity, i.e. entropy rates. A potential explanation for the observed relationship is discussed.
Melatonin and Leishmania amazonensis Infection Altered miR-294, miR-30e, and miR-302d Impacting on Tnf, Mcp-1, and Nos2 Expression

Leishmaniases are neglected diseases that cause a large spectrum of clinical manifestations, from cutaneous to visceral lesions. The initial steps of the inflammatory response involve the phagocytosis of Leishmania and the parasite replication inside the macrophage phagolysosome. Melatonin, the darkness-signaling hormone, is involved in modulation of macrophage activation during infectious diseases, controlling the inflammatory response against parasites. In this work, we showed that exogenous melatonin treatment of BALB/c macrophages reduced Leishmania amazonensis infection and modulated host microRNA (miRNA) expression profile, as well as cytokine production such as IL-6, MCP-1/CCL2, and, RANTES/CCL9. The role of one of the regulated miRNA (miR-294-3p) in L. amazonensis BALB/c infection was confirmed with miRNA inhibition assays, which led to increased expression levels of Tnf and Mcp-1/Ccl2 and diminished infectivity. Additionally, melatonin treatment or miR-30e-5p and miR-302d-3p inhibition increased nitric oxide synthase 2 (Nos2) mRNA expression levels and nitric oxide (NO) production, altering the macrophage activation state and reducing infection. Altogether, these data demonstrated the impact of melatonin treatment on the miRNA profile of BALB/c macrophage infected with L. amazonensis defining the infection outcome.
Phylogeny matters: revisiting ‘a comparison of bats and rodents as reservoirs of zoonotic viruses’

Diseases emerging from wildlife have been the source of many major human outbreaks. Predicting key sources of these outbreaks requires an understanding of the factors that explain pathogen diversity in reservoir species. Comparative methods are powerful tools for understanding variation in pathogen diversity and rely on correcting for phylogenetic relatedness among reservoir species. We reanalysed a previously published dataset, examining the relative effects of species' traits on patterns of viral diversity in bats and rodents. We expanded on prior work by using more highly resolved phylogenies for bats and rodents and incorporating a phylogenetically controlled principal components analysis. For rodents, sympatry and torpor use were important predictors of viral richness and, as previously reported, phylogeny had minimal impact in models. For bats, in contrast to prior work, we find that phylogeny does have an effect in models. Patterns of viral diversity in bats were related to geographical distribution (i.e. latitude and range size) and life history (i.e. lifespan, body size and birthing frequency). However, the effects of these predictors were marginal relative to citation count, emphasizing that the ability to accurately assess reservoir status largely depends on sampling effort and highlighting the need for additional data in future comparative studies.