Miracle-dz/UniFAR-FTUs huggingface dataset:
Touch-input based continuous authentication using gesture-level and session-level features

Nowadays, touchscreen mobile devices make up a larger share in the market. Users often use these devices to store personal and sensitive data. This necessitate to find more effective and robust methods to continuously authenticate touch-based mobile device users. In this paper, we propose two levels of behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In an experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a touch-based tablet device using the most common gestures, i.e., scrolling in all directions; up, down, right and left, tapping, zoom-in and out etc. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able afterwards to authenticate users by monitoring their behavior in performing input touch actions. We build two models of features from two different perspectives to study which model will give us the higher accuracy in classification; the low level features model obtained through the stoke level data or the high level abstracted features model obtained through the session level data. Our results indicate the possibility of the continuous authentication for touch-input users with higher promises for session level features than gesture level features.
Non-Linear Compression: Gzip Me Not!

Most compression algorithms used in storage systems today are based on an increasingly outmoded sequential processing model. Systems wishing to decompress blocks out-of-order or in parallel must reset the compressor's state before each block, reducing adaptiveness and limiting compression ratios. To remedy this situation, we present Non-Linear Compression, a novel compression model enabling systems to impose an arbitrary partial order on inter-block dependencies. Mutually unordered blocks may be compressed and decompressed out-of-order or in parallel, and a compressor can adaptively compress each block based on all causally prior blocks. This graph structure captures the system's data dependencies explicitly and completely, enabling the compressor to adapt using long-lived state without the constraint of sequential processing. Preliminary experiences with a simple Huffman compressor suggest that non-linear compression fits a diverse set of storage applications.
Plant Disease Detection Using Machine Learning

In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their spread and minimize any damage. However, this task often requires a high amount of labor and experience. This project suggests using image processing techniques to extract characteristics from images of plant leaves and then utilizing machine learning algorithms to classify these leaves as either healthy or diseased. The evaluation of machine learning algorithms, such as Convolutional Neural Networks, was conducted to measure their performance, using their accuracy, precision, and recall. The proposed system displays a level of accuracy in detecting and classifying plant diseases. It focuses on categorizing images of types of plants into disease types as well as healthy ones. These findings highlight the potential benefits of employing machine learning techniques in the detection of plant diseases offering farmers a tool for managing and safeguarding their crops.
Maximum likelihood estimation and forecasting for GARCH, Markov switching, and locally stationary wavelet processes

Financial time series are frequently met both in daily life and the scientific world. It is clearly of importance to study the financial time series, to understand the mechanism giving rise to the data, and/or predict the future values of a series. This thesis is dedicated to statistical inferences of a number of models for financial time series. Financial time series often exhibit time-varying and clustering volatility (conditional variance), which were not handled well by traditional models, until the development of the autoregressive conditionally heteroscedastic (ARCH) and the generalized ARCH (GARCH) models. We prove the consistency and asymptotic normality of the quasi-maximum likelihood estimators for a GARCH(1,2) model with dependent innovations, which extends the results for the GARCH(1,1) model in the literature under weaker conditions. The regime-switching GARCH (RS-GARCH) model extends the GARCH models by incorporating a Markov switching into the variance structure. The statistical inferences for the RS-GARCH model are difficult due to the complex dependence structure. One alternative is to take average over all regimes at every step, and adapt the integrated conditional variances. Another one is to transform the GARCH into an ARCH model. The maximum likelihood (ML) estimation of these two cases is considered. Consistency of the ML estimators is proved, and the asymptotic normality is suggested by simulation studies. The results are further generalized to a general autoregressive model with Markov switching, in which the autoregression can be of infinite order. Consistency of the ML estimators is obtained and the asymptotic normality is conjectured. Time series analysis can also be conducted in frequency domain, i.e. to analyze their spectral values obtained by e.g. Fourier or wavelet transforms. Locally stationary wavelet (LSW) processes are a class of processes defined on a set of non-decimated wavelets. We first address the problem on how to select a wavelet in practice, and some guidelines are suggested by simulation studies. The existing forecasting algorithm for LSW processes is found vulnerable to outliers, and a new forecasting algorithm is proposed to overcome this weakness. The new algorithm is shown stable and outperforms the existing algorithm when applied to real financial data. The volatility forecasting ability of LSW model based on our new algorithm is then discussed and is shown to be competitive with GARCH models. Algorithms and functions for data generation, calculation and maximization of the likelihoods for RS-GARCH models, and for the new forecasting algorithm of LSW processes are appended.
A new progressive-iterative algorithm for multiple structure alignment

MOTIVATION Multiple structure alignments are becoming important tools in many aspects of structural bioinformatics. The current explosion in the number of available protein structures demands multiple structural alignment algorithms with an adequate balance of accuracy and speed, for large scale applications in structural genomics, protein structure prediction and protein classification. RESULTS A new multiple structural alignment program, MAMMOTH-mult, is described. It is demonstrated that the alignments obtained with the new method are an improvement over previous manual or automatic alignments available in several widely used databases at all structural levels. Detailed analysis of the structural alignments for a few representative cases indicates that MAMMOTH-mult delivers biologically meaningful trees and conservation at the sequence and structural levels of functional motifs in the alignments. An important improvement over previous methods is the reduction in computational cost. Typical alignments take only a median time of 5 CPU seconds in a single R12000 processor. MAMMOTH-mult is particularly useful for large scale applications. AVAILABILITY http://ub.cbm. uam.es/mammoth/mult.
Utilizing multimodal AI to improve genetic analyses of cardiovascular traits

Electronic health record (EHR) and biobank datasets contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower-dimensional representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome-wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.
The long-term impacts of domestic and international TB service improvements on TB trends within the United States: a mathematical modelling study

Background: For low TB incidence settings, disease elimination is a long-term goal. We investigated pathways to TB pre-elimination and elimination (incidence <1.0 and <0.1 per 100,000, respectively) in the United States. Methods: Using a mathematical modelling framework, we simulated how U.S. TB incidence could be affected by changes in TB services in the countries of origin for future migrants to the United States, as well as changes in TB services inside the United States. We constructed intervention scenarios representing improvements in TB services internationally and within the United States, individually and in combination, plus a base-case scenario representing continuation of current services. We simulated health and economic outcomes until 2100. Findings: Under the base-case, U.S. TB incidence rates were projected to decline to 1.8 (95% uncertainty interval: 1.5, 2.1) per 100,000 by 2050. Intervention scenarios produced substantial reductions in TB incidence, with the combination of all domestic and international interventions projected to achieve pre-elimination by 2033 (2031, 2037). Compared to the base-case, this combination could avert 101 (84, 120) thousand U.S. TB cases and 13 (11, 16) thousand U.S. TB deaths over 2025-2050; the total economic value of these TB incidence reductions was estimated as US$68 (33, 112) billion. TB elimination was not projected before 2100. Interpretation: Strengthening TB services domestically, promoting the development of more effective technologies and interventions, and supporting TB programs in high-burden countries are key strategies for accelerating progress towards TB elimination in the United States. Funding: U.S. Centers for Disease Control and Prevention.
DPOAEs and contralateral acoustic stimulation and their link to sound hypersensitivity in children with autism

Abstract Objective: The hypersensitivity of children with autism to sound is a relatively unexplained behavior. The goal of the current study was to investigate the DPOAE characteristics of children with autism compared to a control group. Design: DPOAEs with and without contralateral stimuli were measured in two groups in three different conditions. Study sample: The study employed 14 children with autism and a control group with 28 age-matched participants. Results: In the without-contralateral stimulus condition, the overall S/N of DPOAEs was greater for the control group compared to the autism group (p < 0.0005). For both groups, the DPOAE S/N increased as a function of frequency in both ears. In the with contralateral stimulus condition, group and ear effects were noticed, however, no age, frequency, or contralateral stimulus type (BBN vs. 1000 Hz) effect could be detected. Conclusions: Presence of reduced DPOAEs in the autism group does not support the hypothesis that sound hypersensitivity in children with autism may be related to overactive outer hair cells function; rather it may be due to early cochlear dysfunction. Also, sound hypersensitivity in the autism group may be due to abnormality of the efferent auditory pathway as shown by lack of sufficient contralateral suppression.
Simulating Liquidity Stress in the Derivatives Market

Abstract We investigate whether margin calls on derivative counterparties could exceed their available liquid assets and, by preventing immediate payment of those calls, spread such liquidity shortfalls through the market. Using trade repository data on derivative portfolios, we simulate variation margin calls in a stress scenario and compare them with the cash buffers of the institutions facing the calls. Where buffers are insufficient we assume institutions take remedial action such as borrowing to cover the shortfalls, but only after waiting as long as possible to receive payments before making their own. Such delays can force recipients into more extensive remedial action than otherwise. Hence, liquidity shortfalls can grow in aggregate as they spread through the network. However, we find an aggregate liquidity shortfall equivalent to only a modest fraction of average daily cash borrowing in international repo markets. Moreover, we find that only a small part of this aggregate shortfall could be avoided if payments within the counterparty network were coordinated by an external authority.
A Two-Tiered Architecture for Real-Time Communications in Large-Scale Wireless Sensor Networks: Research Challenges

In wireless sensor networks (WSN), individual sensor nodes are inherently unreliable and have very limited capabilities to ensure real-time guarantees. In fact, one of the most predominant limitations in wireless sensor networks is energy consumption, which hinders the capacity of the network to provide real-time guarantees (e.g. low duty-cycles, low transmission range). Many approaches have been proposed to deal with energy/latency trade-offs, but they are likely to be insufficient for the applications where reduced delay guarantee is the main concern. This paper investigates the potential application of a decentralized two-tiered architecture, in large-scale wireless sensor networks, where an upper layer Wireless Local Area Network (WLAN), serves as a backbone to a wireless sensor network. Doing so, we target to provide more reliable services with reduced end-to-end delays, and lower energy consumption in the underlying sensor network. We are also assessing candidate technologies for implementing this architectural approach, namely to use the IEEE 802.11 WLAN protocol on the top of the IEEE 802.15. 4 protocol designed for low-rate wireless private area networks (LR-WPAN). A Two-Tiered Architecture for Real-Time Communications in Large-Scale Wireless Sensor Networks: Research Challenges
TurboBlom: A light and resilient key predistribution scheme with application to Internet of Things

In the Internet of Things (IoT), there are often devices that are computationally too constrained to establish a security key using traditional key distribution mechanisms such as those based on the Diffie-Hellman key exchange. To address this, current solution commonly rely on key predistribution schemes (KPSs). Among KPSs, the Blom scheme provably provides the highest resilience against node capture attacks. This, however, comes at high computational overhead, because the Blom scheme requires many multiplications over a large finite field. To overcome this computational overhead, we present TurboBlom, a novel amendment of the Blom scheme. TurboBlom circumvents the need for field multiplications by utilizing specialized generator matrices, such as random zero-one matrices. We demonstrate that, through this approach, TurboBlom can significantly reduce the computational overhead of the Blom scheme by orders of magnitude. In our next key finding, we demonstrate that TurboBlom offers a level of resilience against node capture that is virtually on par with the Blom scheme. Notably, we prove that the gap between the resilience of the two schemes is exponentially small. These features of TurboBlom (i.e., low computational overhead and high resilience) make it suitable for computationally constrained devices. Such devices exist in abundance in IoT, for example, as part of Low Power and Lossy Networks (LLNs). To demonstrate a sample application of TurboBlom, we show how to use it to enable sender authentication in the Routing Protocol for LLNs (RPL), a standard routing protocol for IoT.
Early effects of COVID-19 on physical activity and screen time use among elementary school students in Columbus, New Mexico Dialogues in Health

home con ﬁ nement. The mean number of days children were physically active for > 60 minutes signi ﬁ cantly decreased, while daily screen time use increased. Having parents born in Mexico, infrequent family meals ( < 3/week), and not having community spaces for physical activity close by protected children from decreases in their level of physical activity from 2019 to 2020. Home-based exercise may serve as a suitable method of physical activity when public health responses to COVID-19 restrict community spaces. Future interventions should also be mindful of the role that parental nativity and related cultural factors may play in children ’ s physical activity levels.
SUMO-dependent transcriptional repression by Sox2 inhibits the proliferation of neural stem cells

Sox2 is known for its roles in maintaining the stem cell state of embryonic stem cells and neural stem cells. In particular, it has been shown to slow the proliferation of these cell types. It is also known for its effects as an activating transcription factor. Despite this, analysis of published studies shows that it represses as many genes as it activates. Here, we identify a new set of target genes that Sox2 represses in neural stem cells. These genes are associated with centrosomes, centromeres and other aspects of cell cycle control. In addition, we show that SUMOylation of Sox2 is necessary for the repression of these genes and for its repressive effects on cell proliferation. Together, these data suggest that SUMO-dependent repression of this group of target genes is responsible for the role of Sox2 in regulating the proliferation of neural stem cells.
Factor XIII Val 34 Leu: a novel association with primary intracerebral hemorrhage.

BACKGROUND AND PURPOSE A common G-to-T point mutation (Val 34 Leu) in exon 2 of the alpha-subunit of the factor XIII is strongly negatively associated with the development of myocardial infarction. This result suggests that factor XIII Val 34 Leu is interfering with the formation of cross-linked fibrin. The role of factor XIII Val 34 Leu in the pathogenesis of cerebral infarction and primary intracerebral hemorrhage is unknown. METHODS Six hundred twelve patients with acute stroke, defined by World Health Organization criteria and cranial CT, and 436 age-matched control subjects free of cerebrovascular disease were genotyped for the factor XIII Val 34 Leu mutation. Venous blood was drawn for the determination of hemostatic variables and lipids. Factor XIII genotype was determined through a single-stranded conformational polymorphism technique and plasminogen activator inhibitor (PAI)-1 4G/5G promoter genotype by allele-specific polymerase chain reaction. RESULTS The mutation was more frequent in patients with primary intracerebral hemorrhage (n=62) (54.8%; P=.05) than in control subjects (41.7%) or in patients with cerebral infarction (n=529) (46.5%; P=.22). There was no relationship between PAI-1 levels and the PAI-1 4G/5G genotype. CONCLUSIONS There was a slightly higher incidence of factor XIII Val 34 Leu in patients with PICH. This may be related to impaired cross-linking of fibrin and/or coagulation proteins.
What explains phytoplankton dynamics? An analysis of the Helgoland Roads Time Series data sets

Phytoplankton is a diverse group of organisms that account for almost 50% of the global primary production. This thesis aims to investigate the role of environmental variables in structuring phytoplankton communities at Helgoland Roads, North Sea. It contains a detailed description of long-term changes in nutrients, calculates the phytoplankton-carrying capacity of the German Bight, and explores the response of phytoplankton diversity to environmental changes. Moreover, the importance of different biotic and abiotic factors in explaining the variability of phytoplankton abundance is discussed here. This thesis, for the first time, estimates the phytoplankton K of the German Bight, showing a high degree of variability over time. It also establishes a causal relationship between ecosystem variability and biodiversity; it explains the coexistence of the phytoplankton species in a system limited by multiple resources. In addition, this thesis establishes a pattern of seasonal phytoplankton dynamics in relation to biotic and abiotic factors. Overall, the results of this thesis will expand our understanding of the effect of long-term environmental changes on the dynamics of the phytoplankton community in the North Sea.
User-centric RRM and Optimizable Protocol Design for beyond-4 G RANs

We argue that achieving very high data rates can be accomplished by moving away from system-centric radio resource management (RRM), used in the cellular radio acces s network (RAN) architecture, and toward advanced user-cent ric RRM, obtained with top-down RAN protocol design. Systemcentric RRM uses a divide-and-conquer approach, which assi gns resources to cells first and to users second. On the other hand , user-centric RRM assigns resources to users first and then fin ds radio ports to provide these resources, making it potential ly more efficient. Top-down optimizable RAN protocol design provides a way to design RAN protocols, which support a wide range of RRM algorithms, ensuring that RRM is not restricted by RAN protocols. Combining the user-centric RRM with the top-down optimizable RAN protocol design approach seems to provide promising approach for advanced RRM in beyond-4G RANs.