Miracle-dz/UniFAR-FTUs huggingface dataset:
Expectation Maximization based algorithm applied to DNA sequence motif finder

Finding transcription factor binding sites plays an important role inside bioinformatics. Its correct identification in the promoter regions of co-expressed genes is a crucial step for understanding gene expression mechanisms and creating new drugs and vaccines. The problem of finding motifs consists in seeking conserved patterns in biological datasets of sequences, through using unsupervised learning algorithms. This problem is considered one of the open problems of computational biology, which in its simplest formulation has been proven to be np-hard. Moreover, heuristics and meta-heuristics algorithms have been shown to be very promising in solving combinatorial problems with very large search spaces. In this paper we propose a new algorithm called Biomapp (Biological Motif Application) based on canonical Expectation Maximization that uses the Kullback-Leibler divergence to re-estimate the parameters of statistical model. Furthermore, the algorithm is embedded in an Iterated Local Search, as the local search step and then, we use a hierarchical perturbation operator in order to escape from local optima. The results obtained by this new approach were compared with the state-of-the-art algorithm MEME (Multiple EM Motif Elicitation) showing that Biomapp outperformed this classical technique in several datasets.
A multitasking surrogate-assisted differential evolution method for solving bi-level optimization problems

Bi-level programming (BLP) is a hierarchical decision-making problem in which part of the constraints is determined by solving other optimization problems. Classic op-timization techniques cannot be applied directly, while standard metaheuristics often demand high computational costs. The transfer optimization paradigm uses the experience acquired when solving one optimization problem to speed up a distinct but related task. In particular, the multitasking technique ad-dresses two or more optimization tasks simultaneously to explore similarities and improve convergence. BLPs can benefit from multitasking as many (potentially similar) lower-level problems must be solved. Recently, several studies used surrogate methods to save expensive upper-level function evaluations in BLPs. This work proposes an algorithm based on Differential Evolution supported by transfer optimization and surrogate models to solve BLPs more efficiently. Experiments show a reduction of up to 86% regarding the number of function evaluations of the upper-level problem while achieving similar or superior accuracy when compared to state-of-the-art solvers.
IMar

Currently, WiFi-based user continuous action counting and recognition is limited to a single person. Being able to continuously analyze and record the different actions of multiple users in a device-free scene is one of the most challenging job to date. In this paper, we present a new WiFi-based multi-user action recognition system, called IMar, which can achieve decomposition of multi-user action information and retain action features as much as possible, i.e., simultaneously recognize and count the continuous and different actions of multiple people. Our main technical route is to design a Dynamic Propagation Delay Threshold Sanitization (DPDTS) algorithm to retain the path information that only passes through the target user body, in order to reduce the multipath effect and make the data as pure as possible, and then model the amplitude relationship of the multi-person action scene. After acquiring the individual data according to the model and tensor decomposition, we propose a Multiplayer Action Amplitude Decomposition and Completion (MAADC) algorithm to obtain more informative data for individual continuous action. Moreover, the single-person data of subcarrier-level obtained by tensor decomposition is extended to the data of stream-level, which brings great convenience to the single-person action recognition. Experimental results show that IMar can work with up to 6 people. The average recognition accuracy and counting accuracy are 78% and 91% respectively in the experimental group of continuous and natural actions by multiple users, and the average recognition accuracy of the experiments in all cases is 83.7%.
Traffic vehicle detection by fusion of millimeter wave radar and camera

Aiming at the defects of poor identification effect and prone to be disturbed by weather and illumination changes in vehicle detection using a single sensor, a multi-sensor fusion sensing system based on millimeter wave radar and camera was designed in this paper. Firstly, the spatial and temporal coordinates of millimeter wave radar and camera are unified through coordinate transformation and time alignment. Then, YOLOV5 deep neural network model is used to realize target detection of camera data, including cars, trucks and buses. Finally, data fusion is realized according to the detection results of the two sensors. Through field experiments, the vehicle detection accuracy reaches 95.3%. The results show that the proposed system overcomes the deficiency of single sensor in target detection, which can improve the reliability and effectiveness of vehicle detection.
Analysis and Simulation Research of Household Appliances for Non-intrusive Load Monitoring in Residential Buildings

Distinguish the appliances from the electrical data on the power line has become a great challenge. However, the existing studies rarely focus on the structure of household appliances, which is not reliable in dealing with different requirements. To overcome this limitation, this paper presents a series of simulation circuits based on the power components of household appliances and capable to apply to Non-Intrusive Load Monitoring (NILM). Simulation results demonstrate that our methods can model these appliances well, which is critical to feature extraction and identification.
Gamification On Mobile Banking Application: A Literature Review

Gamification is a design strategy for applying game elements in a non-game context for the purpose of changing individual behavior. Gamification and its motivational potential are seen as a trend for enhance and promote user engagement in various contexts. Many application companies have adopted gamification, which aims to make consumers more attractive and loyal. Given the ambivalent evidence on the effectiveness of gamification Gamification's popularity has grown dramatically over the years, as seen by an increase in the number of gamification apps and more studies. Gamification might be personalized to people with various demographic and psychological characteristics, particularly in the banking industry. While the use of gamification in many underserved areas, such as banking, will provide amazing outcomes, most research investigating the role of gamification in banking have found that it has a significant influence on increasing consumer engagement and enhancing system performance. This research did a detailed review of the gamification literature and assessed the research methods and findings in gamification research in mobile banking in this study, which provides a more comprehensive overview of the topic. The goal of this research is to categorize and classify gamification in mobile banking, as well as to analyze the current state of the art to aid future research. For managerial and academic consequences, further research directions are also recommended.
Human Body Micro-Doppler Signal Detection Technology based on CW Doppler Radar

As a method of non-contact life sign detection, life detection radar is widely used in post-disaster rescue and patient monitoring. How to detect the human heartbeat and respiration signals from the complex environment and further improve the signal-to-noise ratio of the signals is a difficult problem. In this paper, under the CW Doppler radar system, a life detection radar signal processing algorithm based on LMS adaptive filtering is proposed. On the basis of the traditional IIR filtering processing, the LMS adaptive filtering algorithm is further used for processing. The MATLAB simulation results show that the algorithm can extract and separate the heartbeat signal and respiration signal of the human target from the echo signal. The software and hardware are designed on the Micro-Doppler signal processing platform with FPGA as the core, and the human cardiopulmonary signal detection experiment is curried out by using the CW Doppler radar. The simulation and experimental results demonstrate the effectiveness of the signal processing algorithm and the correctness of the related design.
Covariant KSGNS construction and quantum instruments

We study positive kernels on $X\times X$, where $X$ is a set equipped with an action of a group, and taking values in the set of $\mathcal A$-sesquilinear forms on a (not necessarily Hilbert) module over a $C^*$-algebra $\mathcal A$. These maps are assumed to be covariant with respect to the group action on $X$ and a representation of the group in the set of invertible ($\mathcal A$-linear) module maps. We find necessary and sufficient conditions for extremality of such kernels in certain convex subsets of positive covariant kernels. Our focus is mainly on a particular example of these kernels: a completely positive (CP) covariant map for which we obtain a covariant minimal dilation (or KSGNS construction). We determine the extreme points of the set of normalized covariant CP maps and, as a special case, study covariant quantum observables and instruments whose value space is a transitive space of a unimodular type-I group. As an example, we discuss the case of instruments that are covariant with respect to a square-integrable representation.
SAMoSA

Despite advances in audio- and motion-based human activity recognition (HAR) systems, a practical, power-efficient, and privacy-sensitive activity recognition system has remained elusive. State-of-the-art activity recognition systems often require power-hungry and privacy-invasive audio data. This is especially challenging for resource-constrained wearables, such as smartwatches. To counter the need for an always-on audio-based activity classification system, we first make use of power and compute-optimized IMUs sampled at 50 Hz to act as a trigger for detecting activity events. Once detected, we use a multimodal deep learning model that augments the motion data with audio data captured on a smartwatch. We subsample this audio to rates ≤ 1 kHz, rendering spoken content unintelligible, while also reducing power consumption on mobile devices. Our multimodal deep learning model achieves a recognition accuracy of 92.2% across 26 daily activities in four indoor environments. Our findings show that subsampling audio from 16 kHz down to 1 kHz, in concert with motion data, does not result in a significant drop in inference accuracy. We also analyze the speech content intelligibility and power requirements of audio sampled at less than 1 kHz and demonstrate that our proposed approach can improve the practicality of human activity recognition systems.
AdaMICA

Recent studies on intermittent computing target single-core processors and underestimate the efficient parallel execution of highly-parallelizable machine learning tasks. Even though general-purpose multicore processors provide a high degree of parallelism and programming flexibility, intermittent computing has not exploited them yet. Filling this gap, we introduce AdaMICA (Adaptive Multicore Intermittent Computing) runtime that supports, for the first time, parallel intermittent computing and provides the highest degree of flexibility of programmable general-purpose multiple cores. AdaMICA is adaptive since it responds to the changes in the environmental power availability by dynamically reconfiguring the underlying multicore architecture to use the power most optimally. Our results demonstrate that AdaMICA significantly increases the throughput (52% on average) and decreases the latency (31% on average) by dynamically scaling the underlying architecture, considering the variations in the unpredictable harvested energy.
ClenchClick

We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user's hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input.
Interacting With Curves: How to Validly Test and Probe Interactions in the Real (Non-linear) World

Hypotheses involving interactions in which one variable modifies the association between another two are very common. They are typically tested relying on models that assume effects are linear, for example, with a regression like y = a + b x + c z + d x × z. In the real world, however, few effects are linear, invalidating inferences about interactions. For instance, in realistic situations, the false-positive rate can be 100% for detecting an interaction, and a probed interaction can reliably produce estimated effects of the wrong sign. In this article, I propose a revised toolbox for studying interactions in a curvilinear-robust manner, giving correct answers “even” when effects are not linear. It is applicable to most study designs and produces results that are analogous to those of current—often invalid—practices. The presentation combines statistical intuition, demonstrations with published results, and simulations.
VECTOR

Ambient temperature distribution monitoring is desired in a variety of real-life applications including indoors temperature control and building energy management. Traditional temperature sensors have their limitations in the aspects of single point/item based measurements, slow response time and huge cost for distribution estimation. In this paper, we introduce VECTOR, a temperature-field monitoring system that achieves high temperature sensing accuracy and fast response time using commercial sound playing/recording devices. First, our system uses a distributed ranging algorithm to measure the time-of-flight of multiple sound paths with microsecond resolution. We then propose a dRadon transform algorithm that reconstructs the temperature distribution from the measured speed of sound along different paths. Our experimental results show that we can measure the temperature with an error of 0.25°C from single sound path and reconstruct the temperature distribution at a decimeter-level spatial resolution.
Phylogenetic annotation of Caenorhabditis elegans heat shock protein 70 genes

Annotation of the 70 kDa heat shock proteins (Hsp70s) has been chaotic especially in invertebrates. In this study, we validated an emerging nomenclature of Hsp70s, which can be potentially applied to all metazoan Hsp70s, by conducting a genome-wide annotation of Caenorhabditis elegans Hsp70s. Using the phylogenetic annotation, the seven canonical C. elegans Hsp70s were successfully classified into four known lineages, cytosolic A, cytosolic B, endoplasmic reticulum, and mitochondria. Motifs specific to each lineage were all conserved in the C. elegans Hsp70s. From these results, we propose new aliases of C. elegans Hsp70s that should help future annotation of this important molecular chaperone.
Methods for stabilizing high Reynolds number Lattice Boltzmann simulations

PAGE The Lattice Boltzmann Method (LBM) is a simple and highly efficient method for computing nearly incompressible fluid flow. However, it is well known to suffer from numerical instabilities for low values of the transport coefficients. This dissertation examines a number of methods for increasing the stability of the LBM over a wide range of parameters. First, we consider a simple transformation that renders the standard LB equation implicit. It is found that the stability is largely unchanged. Next, we consider a stabilization method based on introducing a Lyapunov function which is essentially a discrete-time H-function. The uniqueness of an H-function that appears in the literature is proven, and the method is extended to stabilize some of the more popular LB models. We also introduce a new method for implementing boundary conditions in the LBM. The hydrodynamic fields are imposed in a transformed moment space, whereas the non-hydrodynamic fields are shifted over from neighboring nodes. By minimizing population gradients, this method exhibits superior numerical stability over other widely employed schemes when tested on the widely-used benchmark of incompressible flow over a backwards-facing step.
Photorefractive amplification of dynamic light signals using photoconductive ferroelectric liquid crystals

The photorefractive effect in photoconductive ferroelectric liquid crystal blends containing photoconductive chiral compounds was investigated. Terthiophene compounds possessing chiral structures were mixed with an achiral smectic C liquid crystal mixture. The blends exhibit the ferroelectric chiral smectic C phase. The photorefractivity of the liquid crystal blends was investigated by two-beam coupling experiments. The photoconductive ferroelectric liquid crystal blends prepared in this study exhibited a large gain coefficient of over 1200 cm-1 and a fast response time shorter than 1 ms. Amplification of an moving optical image signal of over 30 fps using the photorefractive ferroelectric liquid crystal was demonstrated.
Constructions at Work: The Nature of Generalization in Language

Part One: Constructions 1. Overview 2. Surface Generalizations 3. Item Specific Knowledge and Generalizations Part Two: Learning Generalizations 4. How Generalizations are Learned 5. How Generalizations are Constrained 6. Why Generalizations are Learned Part Three: Explaining Generalizations 7. Island Constraints and Scope 8. Grammatical Categorization: Subject Auxiliary Inversion 9. Cross-linguistic Generalizations in Argument Realization 10. Variations on a Constructionist Theme 11. Conclusion References Index
Optimal $W^{1,\infty}$-estimates for an isoparametric finite element discretization of elliptic boundary value problems

We consider an elliptic boundary value problem on a domain with regular boundary and discretize it with isoparametric ﬁnite elements of order k ≥ 1 . We show optimal order of convergence of the isoparametric ﬁnite element solution in the W 1 , ∞ -norm. As an intermediate step, we derive stability and convergence estimates of optimal order k for a (generalized) Ritz map.
Genetic documentation of snake species using non-invasive sampling and non-toxic DNA isolation method

Despite the knowledge of the evolution of snakes worldwide, snake phylogeny requires a more detailed approach in South India. Molecular taxonomic approaches using DNA barcoding are a molecular tool frequently in species identification as well as studies of phylogenetics. Here, a non-invasive genetic sampling method using skin exuviates was used. This method is often overlooked for molecular studies of reptiles. We isolated DNA using a non-toxic method from skin exuviates collected from Chennai Snake Park and screened for the cytochrome oxidase subunit I (COI) region of mitochondria. Samples that were amplified successfully were barcoded. A total of seven species of snakes were identified which belonged to 5 families. We combined and compared sequences of these seven-snake species from other countries to construct a phylogenetic tree and examined the genetic distance within species and families. This depiction and analysis showed a high degree of genetic variability intra-specifically between the South Indian samples to the samples from other parts of the world. This study documents how skin exuviates of snakes and the polymerase chain reaction of the COI region can be used for DNA barcoding and estimating phylogenetic relationships among snake species. Overall, this method is very versatile, inexpensive, and non-toxic which can help in understanding the evolution and phylogeny of snakes to formulate proper strategies for the conservation of snake species.
Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems

A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (R&S) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses and are applicable over domains with mixed variables (continuous, discrete numeric, and discrete categorical) to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use of modern R&S procedures designed to provide efficient sampling strategies and the use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance.
17 β-estradiol ( E 2 ) in membranes : Orientation and dynamic properties

Abbreviations: E2, 17β-Estradiol; DMPC, 1,2-dimyristoy DHPC, 1,2-dihexanoyl-sn-glycero-3-phosphocholine; DPPC 3-phosphocholine; PSPC, 1-palmitoyl-2-stearoyl-sn-gl molecular dynamics. ⁎ Correspondence to: J. J. Guo, Center for DrugDiscovery Sciences, and Department of Chemistry and Chemical Bi 360 Huntington Avenue, Boston, MA 02115-5000, USA. ⁎⁎ Correspondence to: A. Makriyannis, Center for Dr Pharmaceutical Sciences, and Department of Chem Northeastern University, 360 Huntington Avenue, Boston E-mail addresses: j.guo@neu.edu (J.J. Guo), a.makriyan 1 Present address: Dr. D. Yin, Institute of Materia Medic