Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepOrigin: End-To-End Deep Learning For Detection Of New Malware Families

In this paper, we present a novel method of differentiating known from previously unseen malware families. We utilize transfer learning by learning compact file representations that are used for a new classification task between previously seen malware families and novel ones. The learned file representations are composed of static and dynamic features of malware files and are invariant to small modifications that do not change the malware functionality. Using an extensive dataset that consists of thousands of variants of malicious files, we were able to achieve 97.7% accuracy when classifying between seen and unseen malware families. Our method provides an important focalizing tool for cybersecurity researchers and greatly improves the overall ability to adapt to the fast-moving pace of the current threat landscape.
From Cumulative Strain to Available Resources: A Narrative Case Study of the Potential Effects of New Trauma Exposure on Recovery

This article presents a narrative case study of a client with a history of multiple traumas and severe symptomatology, coupled with an ongoing recovery process. A hermeneutical–phenomenological approach was used to analyze two interviews with the participant over a period of 1 year, following a trauma treatment program. Her husband’s suicide in between the two interviews allowed for an exploration of the possible effects of new trauma exposure on the process of recovery. Analysis of the data revealed how the participant’s early trauma experiences had resulted in escalating symptomatology. Through her relationship with her husband, she gradually became ready to engage in therapy in a way that allowed her to benefit from it. Her husband’s suicide forced her to reconsider her own part in her recovery, resulting in a strengthened feeling of inner security and self-efficacy parallel to what is seen in posttraumatic growth. The results contribute our understanding of individual processes of change and recovery, including processes of growth following cumulative trauma. Plausible mechanisms for growth in the present case was the ability to recognize and tolerate feelings, making sense of one’s own reactions, as well as a sense of control and trust in available resources.
Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification

We present an efficient algorithm for motion planning and controlling a robot system with a high number of degrees-of-freedom (DOF). These systems include high-DOF soft robots and articulated robots interacting with a deformable environment. We present a novel technique to accelerate the evaluations of the forward dynamics function by storing the results of costly computations in a hierarchical adaptive grid. Furthermore, we exploit the underactuated properties of the robot systems and build the grid in a low-dimensional space. Our approach approximates the forward dynamics function with guaranteed error bounds and can be used in optimization-based motion planning and reinforcement-learning-based feed-back control. We highlight the performance on two high-DOF robot systems: a line-actuated elastic robot arm and an underwater swimming robot in water. Compared to prior techniques based on exact dynamics evaluation, we observe one to two orders of magnitude improvement in the performance.
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning

Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven field. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifact-free or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning

We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learning as a concept. To realize this, we train sensory-motor data recorded during a tool-use task performed by a robot with deep learning. Experiments include four factors: (1) tools, (2) objects, (3) actions, and (4) effects, which the model considers simultaneously. For evaluation, the robot generates predicted images and motions given information of the effects of using unknown tools and objects. We confirm that the robot is capable of detecting features of tools, objects, and actions by learning the effects and executing the task.
Iodine Deficiency in Latvia: Current Status and Need for National Recommendations

Abstract In the absence of a mandatory salt iodisation programme, two nationwide cross-sectional cluster surveys revealed persisting iodine deficiency among Latvian schoolchildren during the spring season and a noteworthy iodine deficiency in pregnant women in Latvia; these deficiencies warrant intervention. The consequences of mild-to-moderate iodine deficiency during pregnancy and lactation can adversely affect foetal brain development. Data from a Latvian population survey revealed the consumption of approximately 100 μg of iodine per day through foods and iodised salt. Therefore, strategies to increase the consumption of iodine-containing products should be implemented, particularly for children. In addition, to meet the increased iodine requirement during pregnancy, pregnant women should take daily supplements containing 150 μg iodine from the earliest time possible. All women of childbearing age should be advised to increase their dietary iodine intake by using iodised table salt and iodine-rich products: seafood, milk and milk products. For women with pre-existing thyroid pathologies, the medical decision should be considered on a case-by-case basis. Urinary iodine concentration monitoring among schoolchildren and pregnant women and neonatal thyrotropin registry analysis every five years would be an appropriate strategy for maintaining iodine intake within the interval that prevents iodine deficiency disorders.
Parametric Synthesis of Text on Stylized Backgrounds using PGGANs

We describe a novel method of generating high-resolution real-world images of text where the style and textual content of the images are described parametrically. Our method combines text to image retrieval techniques with progressive growing of Generative Adversarial Networks (PGGANs) to achieve conditional generation of photo-realistic images that reflect specific styles, as well as artifacts seen in real-world images. We demonstrate our method in the context of automotive license plates. We assess the impact of varying the number of training images of each style on the fidelity of the generated style, and demonstrate the quality of the generated images using license plate recognition systems.
Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy

Cancer is one of the greatest threats facing our society, being the second leading cause of death globally. Currents strategies for cancer diagnosis consist of the extraction of a solid tissue from the affected area. This sample enables the study of specific biomarkers and the genetic nature of the tumor. However, the tissue extraction is risky and painful for the patient and in some cases is unavailable in inaccessible tumors. Moreover, a solid biopsy is expensive and time consuming and cannot be applied repeatedly. New alternatives that overcome these drawbacks are rising up nowadays, such as liquid biopsy. A liquid biopsy is the analysis of biomarkers in a non-solid biological tissue, mainly blood, which has remarkable advantages over the traditional method; it has no risk, it is non-invasive and painless, it does not require surgery and reduces cost and diagnosis time. The most studied cancer non-invasive biomarkers are circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. These circulating biomarkers play a key role in the understanding of metastasis and tumorigenesis, which could provide a better insight into the evolution of the tumor dynamics during treatment and disease progression. Improvements in isolation technologies, based on a higher grade of purification of CTCs, exosomes, and ctDNA, will provide a better characterization of biomarkers and give rise to a wide range of clinical applications, such as early detection of diseases, and the prediction of treatment responses due to the discovery of personalized tumor-related biomarkers.
Competence-induced protein Ccs4 facilitates pneumococcal invasion into brain tissue and virulence in meningitis

ABSTRACT Streptococcus pneumoniae is a major pathogen that causes pneumonia, sepsis, and meningitis. The candidate combox site 4 (ccs4) gene has been reported to be a pneumococcal competence-induced gene. Such genes are involved in development of S. pneumoniae competence and virulence, though the functions of ccs4 remain unknown. In the present study, the role of Ccs4 in the pathogenesis of pneumococcal meningitis was examined. We initially constructed a ccs4 deletion mutant and complement strains, then examined their association with and invasion into human brain microvascular endothelial cells. Wild-type and Ccs4-complemented strains exhibited significantly higher rates of association and invasion as compared to the ccs4 mutant strain. Deletion of ccs4 did not change bacterial growth activity or expression of NanA and CbpA, known brain endothelial pneumococcal adhesins. Next, mice were infected either intravenously or intranasally with pneumococcal strains. In the intranasal infection model, survival rates were comparable between wild-type strain-infected and ccs4 mutant strain-infected mice, while the ccs4 mutant strain exhibited a lower level of virulence in the intravenous infection model. In addition, at 24 hours after intravenous infection, the bacterial burden in blood was comparable between the wild-type and ccs4 mutant strain-infected mice, whereas the wild-type strain-infected mice showed a significantly higher bacterial burden in the brain. These results suggest that Ccs4 contributes to pneumococcal invasion of host brain tissues and functions as a virulence factor.
Pointing in the Right Direction - Securing Memory Accesses in a Faulty World

Reading and writing memory are, besides computation, the most common operations a processor performs. The correctness of these operations is therefore essential for the proper execution of any program. However, as soon as fault attacks are considered, assuming that the hardware performs its memory operations as instructed is not valid anymore. In particular, attackers may induce faults with the goal of reading or writing incorrectly addressed memory, which can have various critical safety and security implications. In this work, we present a solution to this problem and propose a new method for protecting every memory access inside a program against address tampering. The countermeasure comprises two building blocks. First, every pointer inside the program is redundantly encoded using a multiresidue error detection code. The redundancy information is stored in the unused upper bits of the pointer with zero overhead in terms of storage. Second, load and store instructions are extended to link data with the corresponding encoded address from the pointer. Wrong memory accesses subsequently infect the data value allowing the software to detect the error. For evaluation purposes, we implemented our countermeasure into a RISC-V processor, tested it on a FPGA development board, and evaluated the induced overhead. Furthermore, a LLVM-based C compiler has been modified to automatically encode all data pointers, to perform encoded pointer arithmetic, and to emit the extended load/store instructions with linking support. Our evaluations show that the countermeasure induces an average overhead of 10 % in terms of code size and 7 % regarding runtime, which makes it suitable for practical adoption.
Active image restoration

We study active restoration of noise-corrupted images generated via the Gibbs probability of an Ising ferromagnet in external magnetic field. Ferromagnetism accounts for the prior expectation of data smoothness, i.e., a positive correlation between neighboring pixels (Ising spins), while the magnetic field refers to the bias. The restoration is actively supervised by requesting the true values of certain pixels after a noisy observation. This additional information improves restoration of other pixels. The optimal strategy of active inference is not known for realistic (two-dimensional) images. We determine this strategy for the mean-field version of the model and show that it amounts to supervising the values of spins (pixels) that do not agree with the sign of the average magnetization. The strategy leads to a transparent analytical expression for the minimal Bayesian risk, and shows that there is a maximal number of pixels beyond of which the supervision is useless. We show numerically that this strategy applies for two-dimensional images away from the critical regime. Within this regime the strategy is outperformed by its local (adaptive) version, which supervises pixels that do not agree with their Bayesian estimate. We show on transparent examples how active supervising can be essential in recovering noise-corrupted images and advocate for a wider usage of active methods in image restoration.
Metabolomics of Two Pecan Varieties Provides Insights into Scab Resistance

UHPLC-MS-based non-targeted metabolomics was used to investigate the biochemical basis of pecan scab resistance. Two contrasting pecan varieties, Kanza (scab-resistant) and Pawnee (scab-susceptible), were profiled and the metabolomics data analyzed using multivariate statistics. Significant qualitative and quantitative metabolic differences were observed between the two varieties. Both varieties were found to have some unique metabolites. Metabolites that were only present or more abundant in Kanza relative to Pawnee could potentially contribute to the scab resistance in Kanza. Some of these metabolites were putatively identified as quercetin derivatives using tandem mass spectrometry. This suggests that quercetin derivatives could be important to pecan scab resistance.
Solar Physics From Unconventional Viewpoints

We explore new opportunities for solar physics that could be realized by future missions providing sustained observations from vantage points away from the Sun-Earth line. These include observations from the far side of the Sun, at high latitudes including over the solar poles, or from near-quadrature angles relative to the Earth (e.g., the Sun-Earth L4 and L5 Lagrangian points). Such observations fill known holes in our scientific understanding of the three-dimensional, time-evolving Sun and heliosphere, and have the potential to open new frontiers through discoveries enabled by novel viewpoints.
Secure Authentication Protocol for Wireless Sensor Networks in Vehicular Communications

With wireless sensor networks (WSNs), a driver can access various useful information for convenient driving, such as traffic congestion, emergence, vehicle accidents, and speed. However, a driver and traffic manager can be vulnerable to various attacks because such information is transmitted through a public channel. Therefore, secure mutual authentication has become an important security issue, and many authentication schemes have been proposed. In 2017, Mohit et al. proposed an authentication protocol for WSNs in vehicular communications to ensure secure mutual authentication. However, their scheme cannot resist various attacks such as impersonation and trace attacks, and their scheme cannot provide secure mutual authentication, session key security, and anonymity. In this paper, we propose a secure authentication protocol for WSNs in vehicular communications to resolve the security weaknesses of Mohit et al.’s scheme. Our authentication protocol prevents various attacks and achieves secure mutual authentication and anonymity by using dynamic parameters that are changed every session. We prove that our protocol provides secure mutual authentication by using the Burrows–Abadi–Needham logic, which is a widely accepted formal security analysis. We perform a formal security verification by using the well-known Automated Validation of Internet Security Protocols and Applications tool, which shows that the proposed protocol is safe against replay and man-in-the-middle attacks. We compare the performance and security properties of our protocol with other related schemes. Overall, the proposed protocol provides better security features and a comparable computation cost. Therefore, the proposed protocol can be applied to practical WSNs-based vehicular communications.
Field Distortion and Optimization of a Vapor Cell in Rydberg Atom-Based Radio-Frequency Electric Field Measurement

Highly excited Rydberg atoms in a room-temperature vapor cell are promising for developing a radio-frequency (RF) electric field (E-field) sensor and relevant measurement standards with high accuracy and sensitivity. The all-optical sensing approach is based on electromagnetically-induced transparency and Autler-Townes splitting induced by the RF E-field. Systematic investigation of measurement uncertainty is of great importance for developing a national measurement standard. The presence of a dielectric vapor cell containing alkali atoms changes the magnitude, polarization, and spatial distribution of the incident RF field. In this paper, the field distortion of rubidium vapor cells is investigated, in terms of both field strength distortion and depolarization. Full-wave numerical simulation and analysis are employed to determine general optimization solutions for minimizing such distortion and validated by measuring the E-field vector distribution inside different vapor cells. This work can improve the accuracy of atom-based RF E-field measurements and contributes to the development of related RF quantum sensors.
Nicotine and alcohol: the role of midbrain dopaminergic neurons in drug reinforcement

Nicotine and alcohol addiction are leading causes of preventable death worldwide and continue to constitute a huge socio‐economic burden. Both nicotine and alcohol perturb the brain's mesocorticolimbic system. Dopamine (DA) neurons projecting from the ventral tegmental area (VTA) to multiple downstream structures, including the nucleus accumbens, prefrontal cortex, and amygdala, are highly involved in the maintenance of healthy brain function. VTA DA neurons play a crucial role in associative learning and reinforcement. Nicotine and alcohol usurp these functions, promoting reinforcement of drug taking behaviors. In this review, we will first describe how nicotine and alcohol individually affect VTA DA neurons by examining how drug exposure alters the heterogeneous VTA microcircuit and network‐wide projections. We will also examine how coadministration or previous exposure to nicotine or alcohol may augment the reinforcing effects of the other. Additionally, this review briefly summarizes the role of VTA DA neurons in nicotine, alcohol, and their synergistic effects in reinforcement and also addresses the remaining questions related to the circuit‐function specificity of the dopaminergic system in mediating nicotine/alcohol reinforcement and comorbidity.
Molecular Signals of Heterogeneous Terrestrial Environments Identified in Dissolved Organic Matter: A Comparative Analysis of Orbitrap and Ion Cyclotron Resonance Mass Spectrometers

Terrestrial dissolved organic matter (DOM) interlinks large carbon reservoirs of soils, sediments and marine environments but remains largely uncharacterized on the molecular level. Fourier Transform Mass Spectrometry (FTMS) has proven to be a powerful technique to reveal DOM chemodiversity and potential information encrypted therein. State-of the art FT-ICR MS (Ion Cyclotron Resonance) instruments are yet inaccessible for most researchers. To evaluate the performance of the most recent Orbitrap analyzer as a more accessible alternative, we compared our method to an established 15 Tesla FT-ICR MS on a diverse suite of 17 mainly terrestrial DOM samples regarding 1) ion abundance patterns, 2) differential effects of DOM type on information loss, and 3) derived biogeochemical information. We show that the Orbitrap provides similar information as FT-ICR MS, especially for compound masses below 400 m/z, and is mainly limited by its actual resolving power rather than its sensitivity. Ecosystems that are dominated by inputs of plant-derived material, like DOM from soil, bog, lake and rivers, showed remarkably low average mass to charge ratios, making them also suitable for Orbitrap measurements. The additional information gained from FT-ICR MS was highest in heteroatom-rich (N, S, P) samples from systems dominated by internal cycling, like DOM from groundwater and the deep sea. Here FT-ICR MS detected 37% more molecular formulae and 11% higher ion abundance. However, the overall information content, which was analyzed by multivariate statistical methods, was comparable for both data sets. Mass spectra-derived biogeochemical trends, for example the decrease of DOM aromaticity during the passage through terrestrial environments, were retrieved by both instruments. We demonstrate the growing potential of the Orbitrap as an alternative FTMS analyzer in the context of challenging analyses of DOM complexity, origin and fate.
A Byte-sized Approach to Named Entity Recognition

In biomedical literature, it is common for entity boundaries to not align with word boundaries. Therefore, effective identification of entity spans requires approaches capable of considering tokens that are smaller than words. We introduce a novel, subword approach for named entity recognition (NER) that uses byte-pair encodings (BPE) in combination with convolutional and recurrent neural networks to produce byte-level tags of entities. We present experimental results on several standard biomedical datasets, namely the BioCreative VI Bio-ID, JNLPBA, and GENETAG datasets. We demonstrate competitive performance while bypassing the specialized domain expertise needed to create biomedical text tokenization rules.
The intuitive in HCI: a critical discourse analysis

The concept of the intuitive has been central to HCI practices for a long time, as is evident in both textbooks and research articles. However, while the notion of the intuitive is so often referred to and used in the literature, relatively little work has been done with respect to what the notion actually means. We carried out a literature analysis of 76 articles published at the CHI conference between 2004 and 2016, all discussing or claiming to design for the intuitive. The articles were approached through the lens of discourse analysis, and an inductive reading was carried out to identify different perspectives on the notion. Six different perspectives on the intuitive were found. With few exceptions, the articles do not contain any discussions of functionally impaired users. We argue that the use of the term in the articles is exclusionary, rendering that which it does not consider (those with functional impairments) invisible.