Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of multiple whole-genome and Spike-only sequencing protocols for estimating variant frequencies via wastewater-based epidemiology

Sequencing of SARS-CoV-2 in wastewater provides a key opportunity to monitor the prevalence of variants spatiotemporally, potentially facilitating their detection simultaneously with, or even prior to, observation through clinical testing. However, there are multiple sequencing methodologies available. This study aimed to evaluate the performance of alternative protocols for detecting SARS-CoV-2 variants. We tested the detection of two synthetic RNA SARS-CoV-2 genomes in a wide range of ratios and at two concentrations representative of those found in wastewater using whole-genome and Spike-gene-only protocols utilising Illumina and Oxford Nanopore platforms. We developed a Bayesian hierarchical model to determine the predicted frequencies of variants and the error surrounding our predictions. We found that most of the sequencing protocols detected polymorphic nucleotide frequencies at a level that would allow accurate determination of the variants present at higher concentrations. Most methodologies, including the Spike-only approach, could also predict variant frequencies with a degree of accuracy in low-concentration samples but, as expected, with higher error around the estimates. All methods were additionally confirmed to detect the same prevalent variants in a set of wastewater samples. Our results provide the first quantitative statistical comparison of a range of alternative methods that can be used successfully in the surveillance of SARS-CoV-2 variant frequencies from wastewater.
A Split Questionnaire Survey Design

Abstract This article develops a survey design where the questionnaire is split into components and individuals are administered the varying subsets of the components. A multiple imputation method for analyzing data from this design is developed, in which the imputations are created by random draws from the posterior predictive distribution of the missing parts, given the observed parts by using Gibbs sampling under a general location scale model. Results from two simulation studies that investigate the properties of the inferences using this design are reported. In the first study several random split questionnaire designs are imposed on the complete data from an existing survey collected using a long questionnaire, and the corresponding data elements are extracted to form split data sets. Inferences obtained using the complete data and the split data are then compared. This comparison suggests that little is lost, at least in the example considered, by administering only parts of the questionnaire to ea.
Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery

The United States coastline spans 95,471 miles; a distance that cannot be effectively patrolled or secured by manual human effort alone. Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras and deep-learning based algorithms represent a more efficient alternative for identifying and segmenting objects of interest - namely, ships. However, standard approaches to training these algorithms require large-scale datasets of densely labeled infrared maritime images. Such datasets are not publicly available and manually annotating every pixel in a large-scale dataset would have an extreme labor cost. In this work we demonstrate that, in the context of segmenting ships in infrared imagery, weakly-supervising an algorithm with sparsely labeled data can drastically reduce data labeling costs with minimal impact on system performance. We apply weakly-supervised learning to an unlabeled dataset of 7055 infrared images sourced from the Naval Air Warfare Center Aircraft Division (NAWCAD). We find that by sparsely labeling only 32 points per image, weakly-supervised segmentation models can still effectively detect and segment ships, with a Jaccard score of up to 0.756.
MProtect: Operating System Memory Management without Access

Modern operating systems (OSes) have unfettered access to application data, assuming that applications trust them. This assumption, however, is problematic under many scenarios where either the OS provider is not trustworthy or the OS can be compromised due to its large attack surface. Our investigation began with the hypothesis that unfettered access to memory is not fundamentally necessary for the OS to perform its own job, including managing the memory. The result is a system called MProtect that leverages a small piece of software running at a higher privilege level than the OS. MProtect protects the entire user space of a process, requires only a small modification to the OS, and supports major architectures such as ARM, x86 and RISC-V. Unlike prior works that resorted to nested virtualization, which is often undesirable in mobile and embedded systems, MProtect mediates how the OS accesses the memory and handles exceptions. We report an implementation of MProtect called MGuard with ARMv8/Linux and evaluate its performance with both macro and microbenchmarks. We show MGuard has a runtime TCB 2~3 times smaller than related systems and enjoys competitive performance while supporting legitimate OS access to the user space.
Navigation Method Enhancing Music Listening Experience by Stimulating Both Neck Sides With Modulated Musical Vibration

We propose a method that stimulates musical vibration (generated from and synchronized with musical signals), modulated by the direction and distance to the target, on both sides of a user's neck with Hapbeat, a necklace-type haptic device. We conducted three experiments to confirm that the proposed method can achieve both haptic navigation and enhance the music-listening experience. Experiment 1 consisted of conducting a questionnaire survey to examine the effect of stimulating musical vibrations. Experiment 2 evaluated the accuracy (deg) of users' ability to adjust their direction toward a target using the proposed method. Experiment 3 examined the ability of four different navigation methods by performing navigation tasks in a virtual environment. The results of the experiments showed that stimulating musical vibration enhanced the music-listening experience, and that the proposed method is able to provide sufficient information to guide the users: accuracy in identifying directions was about 20$^{\circ }$, participants reached the target in all navigation tasks, and in about 80% of all trials participants reached the target using the shortest route. Furthermore, the proposed method succeeded in conveying distance information, and Hapbeat can be combined with conventional navigation methods without interfering with music listening.
A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning

— Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging current of EVs makes an accurate SOC estimation a challenge. This paper proposes a novel deep learning-based SOC estimation method for lithium-ion battery-supercapacitor HESS EV based on the nonlinear autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is utilized to capture and overcome the complex nonlinear behaviors of lithium-ion batteries and supercapacitors in EVs. The results show that the proposed method improved the SOC estimation accuracy by 91.5% on average with error values below 0.1% and reduced consumption time by 11.4%. Hence validating both the effectiveness and robustness of the proposed method.
Pressure Anisotropy in Polymer Brushes and Its Effects on Wetting

Polymer brushes, coatings consisting of densely grafted macromolecules, experience an intrinsic lateral compressive pressure, originating from chain elasticity and excluded volume interactions. This lateral pressure complicates a proper definition of the interface and, thereby, the determination and interpretation of the interfacial tension and its relation to the wetting behavior of brushes. Here, we study the link among grafting-induced compressive lateral pressure in polymer brushes, interfacial tension, and brush wettability using coarse-grained molecular dynamics simulations. We focus on grafting densities and polymer–liquid affinities such that the polymer and liquid do not tend to mix. For these systems, a central result is that the liquid contact angle is independent of the grafting density, which implies that the grafting-induced lateral compressive pressure in the brush does not influence its wettability. Although the definition of brush interfacial tensions is complicated by the grafting-induced pressure, the difference in the interfacial tension between wet and dry brushes is perfectly well-defined. We confirm explicitly from Young’s law that this difference offers an accurate description of the brush wettability. We then explore a method to isolate the grafting-induced contribution to the lateral pressure, assuming the interfacial tension is independent of grafting density. This scenario indeed allows disentanglement of interfacial and grafting effects for a broad range of parameters, except close to the mixing point. We separately discuss the latter case in light of autophobic dewetting.
Tunable quantum anomalous Hall effects in ferromagnetic van der Waals heterostructures

ABSTRACT The quantum anomalous Hall effect (QAHE) has unique advantages in topotronic applications, but it is still challenging to realize the QAHE with tunable magnetic and topological properties for building functional devices. Through systematic first-principles calculations, we predict that the in-plane magnetization induced QAHE with Chern numbers C = ±1 and the out-of-plane magnetization induced QAHE with high Chern numbers C = ±3 can be realized in a single material candidate, which is composed of van der Waals (vdW) coupled Bi and MnBi2Te4 monolayers. The switching between different phases of QAHE can be controlled in multiple ways, such as applying strain or (weak) magnetic field or twisting the vdW materials. The prediction of an experimentally available material system hosting robust, highly tunable QAHE will stimulate great research interest in the field. Our work opens a new avenue for the realization of tunable QAHE and provides a practical material platform for the development of topological electronics.
Adaptive Control of Surge Impedance for Electric Motors in Motor Drive Systems

— This article studies the possibility of controlling the surge impedance of the electric motor in motor drives. The existing solution to suppress (or eliminate) the reflected wave impact on the motor insulation run by a Si-IGBT or SiC-MOSFET-based drive is to use either a sinewave filter or 𝒅𝒗/𝒅𝒕 filter. The alternative solution suggested in this article is to implement a high-bandwidth electronic circuit at the end of the cable or at the motor terminals to match the surge impedance of the cable and motor. The high-frequency voltage ringing due to the reflected waves in motor drives is around 𝟏 𝑴𝑯𝒛 , depending on the cable parameters and the length of the cable. In the proposed method, the electronic circuit can quickly detect the 𝒅𝒗 / 𝒅𝒕 rise and fall edges and adjust the electronic circuit equivalent impedance when pulses arrive the motor terminals. Thus, the cable and motor surge impedances can be matched over a short time to prevent reflected waves. As a result, the leakage currents passing through the ball bearing and overvoltage stress on the motor insulation can be suppressed significantly.
Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question Alignment

Complex knowledge base question answering can be achieved by converting questions into sequences of predefined actions. However, there is a significant semantic and structural gap between natural language and action sequences, which makes this conversion difficult. In this paper, we introduce an alignment-enhanced complex question answering framework, called ALCQA, which mitigates this gap through question-to-action alignment and question-to-question alignment. We train a question rewriting model to align the question and each action, and utilize a pretrained language model to implicitly align the question and KG artifacts. Moreover, considering that similar questions correspond to similar action sequences, we retrieve top-k similar question-answer pairs at the inference stage through question-to-question alignment and propose a novel reward-guided action sequence selection strategy to select from candidate action sequences. We conduct experiments on CQA and WQSP datasets, and the results show that our approach outperforms state-of-the-art methods and obtains a 9.88% improvements in the F1 metric on CQA dataset. Our source code is available at https://github.com/TTTTTTTTy/ALCQA.
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer

—Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their size and diversity. To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors. We evaluate six contemporary stylization methods on four publicly-available IR datasets – the ﬁrst comparison of its kind - and ﬁnd that CMST is highly effective for DNN-based detectors. Surprisingly, we ﬁnd that existing data-driven methods are outperformed by a simple grayscale stylization (an average of the color channels). Our analysis reveals that existing data-driven methods are either too simplistic or introduce signiﬁcant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We ﬁnd that MLST leads to more complex stylizations without introducing signiﬁcant im- age artifacts and achieves the best overall detector performance on our benchmark datasets.
A Taxonomy for Inference in Causal Model Families

Neurally-parameterized Structural Causal Models in the Pearlian notion to causality, referred to as NCM, were recently introduced as a step towards next-generation learning systems. However, said NCM are only concerned with the learning aspect of causal inference but totally miss out on the architecture aspect. That is, actual causal inference within NCM is intractable in that the NCM won’t return an answer to a query in polynomial time. This insight follows as corollary to the more general statement on the intractability of arbitrary SCM parameterizations, which we prove in this work through classical 3-SAT reduction. Since future learning algorithms will be required to deal with both high dimensional data and highly complex mechanisms governing the data, we ultimately believe work on tractable inference for causality to be decisive. We also show that not all “causal” models are created equal. More speciﬁcally, there are models capable of answering causal queries that are not SCM, which we refer to as partially causal models (PCM). We provide a tabular taxonomy in terms of tractability properties for all of the different model families, namely correlation-based, PCM and SCM. To conclude our work, we also provide some initial ideas on how to overcome parts of the intractability of causal inference with SCM by showing an example of how parameterizing an SCM with SPN modules can at least allow for tractable mechanisms. We hope that our impossibility result alongside the taxonomy for tractability in causal models can raise awareness for this novel research direction since achieving success with causality in real world downstream tasks will not only depend on learning correct models as we also require having the practical ability to gain access to model inferences.
Deep Learning Based Proactive Optimization for Mobile LiFi Systems with Channel Aging

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing delay at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and time, as well as its application in a practical LiFi optimization problem.
Concentration of the Langevin Algorithm's Stationary Distribution

A canonical algorithm for log-concave sampling is the Langevin Algorithm, aka the Langevin Diffusion run with some discretization stepsize $\eta>0$. This discretization leads the Langevin Algorithm to have a stationary distribution $\pi_{\eta}$ which differs from the stationary distribution $\pi$ of the Langevin Diffusion, and it is an important challenge to understand whether the well-known properties of $\pi$ extend to $\pi_{\eta}$. In particular, while concentration properties such as isoperimetry and rapidly decaying tails are classically known for $\pi$, the analogous properties for $\pi_{\eta}$ are open questions with direct algorithmic implications. This note provides a first step in this direction by establishing concentration results for $\pi_{\eta}$ that mirror classical results for $\pi$. Specifically, we show that for any nontrivial stepsize $\eta>0$, $\pi_{\eta}$ is sub-exponential (respectively, sub-Gaussian) when the potential is convex (respectively, strongly convex). Moreover, the concentration bounds we show are essentially tight. Key to our analysis is the use of a rotation-invariant moment generating function (aka Bessel function) to study the stationary dynamics of the Langevin Algorithm. This technique may be of independent interest because it enables directly analyzing the discrete-time stationary distribution $\pi_{\eta}$ without going through the continuous-time stationary distribution $\pi$ as an intermediary.
Particle Transport and the lambda Bootis Phenomenon. I. The Diffusion/Mass-Loss Model Revisited

Recent observational work has cast doubt on the validity of the diffusion-based model originally developed to account for the peculiar characteristics of the λ Bootis stars. Among some of the difficulties facing the model, the potential effects of rotation stand most prominently. The original diffusion model neglects all forms of rotationally induced mixing, be it in the form of turbulence or large-scale meridional circulation. It was already realized then that this may well be a critical omission, as meridional circulation proved to play an important role in the evolution of chemical abundances in other types of chemically peculiar stars
Investigation of the aptness of newly developed epoxy-based equivalent tissues for newborn and 5-years old in paediatric radiology

The varied radiological applications of tissue equivalent (TE) materials encompass quality checks, diagnostic imaging and dose evaluations. Nevertheless, the availability of compounds representative of paediatric patient tissues for scientific use in lower diagnostic photon energy spectra is limited. In this study, several TE substitutes were developed which replicate the radiographic characteristics of human tissue within these energy ranges, i.e. TE materials for neonatal soft tissue (ESST-NB), neonatal skeletal tissue (ESTB-NB), and the equivalent tissue types representative of a 5 year old child (ESST and ESBT, respectively). The ORNL stylised computational model series was used as a source for the desired elemental proportions. The density, effective atomic number, CT numbers and electron densities calculated for the developed tissue substitutes approximated those of the phantom system used as a reference. Additionally, in keeping with the material choice and production limitations, as close correlations as possible were achieved for all the materials in relation to the reference data for mass densities, mass attenuation coefficients and mass energy-absorption coefficients. The TE substitutes for the newborn over an energy range of 47 keV to 66 keV exhibited maximum discrepancies for µ/ρ of +1.6% to –3.01%, and for µ ! " /ρ of +1.15% to –1.4% in relation to the ORNL reference samples. The respective equivalent data ranges were +1.09 % to – 3.02% and +1.92% to –2.53% for the TE materials representative of a 5-year-old. Given the excellent concordance achieved between the newly constructed TE materials and the reference data, these compounds can subsequently be utilised to create physical phantoms representative of tissue types in neonates and children aged 5 years.
Neural Transition-based Parsing of Library Deprecations

This paper tackles the challenging problem of automating code updates to fix deprecated API usages of open source libraries by analyzing their release notes. Our system employs a three-tier architecture: first, a web crawler service retrieves deprecation documentation from the web; then a specially built parser processes those text documents into tree-structured representations; finally, a client IDE plugin locates and fixes identified deprecated usages of libraries in a given codebase. The focus of this paper in particular is the parsing component. We introduce a novel transition-based parser in two variants: based on a classical feature engineered classifier and a neural tree encoder. To confirm the effectiveness of our method, we gathered and labeled a set of 426 API deprecations from 7 well-known Python data science libraries, and demonstrated our approach decisively outperforms a non-trivial neural machine translation baseline.