Miracle-dz/UniFAR-FTUs huggingface dataset:
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG

Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91--0.94 in RBDB and 0.93 in SHDB, compared to 0.89--0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
Neural Ambisonics encoding for compact irregular microphone arrays

Ambisonics encoding of microphone array signals can enable various spatial audio applications, such as virtual reality or telepresence, but it is typically designed for uniformly-spaced spherical microphone arrays. This paper proposes a method for Ambisonics encoding that uses a deep neural network (DNN) to estimate a signal transform from microphone inputs to Ambisonics signals. The approach uses a DNN consisting of a U-Net structure with a learnable preprocessing as well as a loss function consisting of mean average error, spatial correlation, and energy preservation components. The method is validated on two microphone arrays with regular and irregular shapes having four microphones, on simulated reverberant scenes with multiple sources. The results of the validation show that the proposed method can meet or exceed the performance of a conventional signal-independent Ambisonics encoder on a number of error metrics.
Proximal observers for secure state estimation

This paper discusses a general framework for designing robust state estimators for a class of discrete-time nonlinear systems. We consider systems that may be impacted by impulsive (sparse but otherwise arbitrary) measurement noise sequences. We show that a family of state estimators, robust to this type of undesired signal, can be obtained by minimizing a class of nonsmooth convex functions at each time step. The resulting state observers are defined through proximal operators. We obtain a nonlinear implicit dynamical system in term of estimation error and prove, in the noise-free setting, that it vanishes asymptotically when the minimized loss function and the to-beobserved system enjoy appropriate properties. From a computational perspective, even though the proposed observers can be implemented via efficient numerical procedures, they do not admit closed-form expressions. The paper argues that by adopting appropriate relaxations, simple and fast analytic expressions can be derived.
AT-2FF: Adaptive Type-2 Fuzzy Filter for De-noising Images Corrupted with Salt-and-Pepper

Noise is inevitably common in digital images, leading to visual image deterioration. Therefore, a suitable filtering method is required to lessen the noise while preserving the image features (edges, corners, etc.). This paper presents the efficient type-2 fuzzy weighted mean filter with an adaptive threshold to remove the SAP noise. The present filter has two primary steps: The first stage categorizes images as lightly, medium, and heavily corrupted based on an adaptive threshold by comparing the M-ALD of processed pixels with the upper and lower MF of the type-2 fuzzy identifier. The second stage eliminates corrupted pixels by computing the appropriate weight using GMF with the mean and variance of the uncorrupted pixels in the filter window. Simulation results vividly show that the obtained denoised images preserve image features, i.e., edges, corners, and other sharp structures, compared with different filtering methods.
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings

Word embedding is one of the most important components in natural language processing, but interpreting high-dimensional embeddings remains a challenging problem. To address this problem, Independent Component Analysis (ICA) is identified as an effective solution. ICA-transformed word embeddings reveal interpretable semantic axes; however, the order of these axes are arbitrary. In this study, we focus on this property and propose a novel method, Axis Tour, which optimizes the order of the axes. Inspired by Word Tour, a one-dimensional word embedding method, we aim to improve the clarity of the word embedding space by maximizing the semantic continuity of the axes. Furthermore, we show through experiments on downstream tasks that Axis Tour constructs better low-dimensional embeddings compared to both PCA and ICA.
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting

Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not explicitly model some of the natural characteristics in traffic data, such as the multiscale structure that encompasses spatial and temporal variations at different levels of granularity or scale. To that end, we propose a Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) which combines multiscale analysis (MSA)-based method with Deep Learning (DL)-based method. In WavGCRN, the traffic data is decomposed into time-frequency components with Discrete Wavelet Transformation (DWT), constructing a multi-stream input structure; then Graph Convolutional Recurrent networks (GCRNs) are employed as encoders for each stream, extracting spatiotemporal features in different scales; and finally the learnable Inversed DWT and GCRN are combined as the decoder, fusing the information from all streams for traffic metrics reconstruction and prediction. Furthermore, road-network-informed graphs and data-driven graph learning are combined to accurately capture spatial correlation. The proposed method can offer well-defined interpretability, powerful learning capability, and competitive forecasting performance on real-world traffic data sets.
RBF Neural Network Adaptive Control Strategy based on Sub-Block Approximation Algorithm

Aiming at the uncertainties that existed in the dynamic model of robot may cause instability.This paper has proposed a RBF neural network adaptive control strategy based on sub-block approximation algorithm, in this strategy, sliding model control was used to control trajectory of the joints of robot, and utilize the RBF neural network to approximate the each uncertain in the dynamic model of robot. In order to verify the validity of the control algorithm, we realized the RBF neural network adaptive control strategy based on sub-block approximation algorithm in MATLAB. The simulation results show that compared with the RBF neural network adaptive control strategy based on integral approximation for uncertainness, the proposed control method has features with good position tracking, the smooth movement, effective approaches, etc.
What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems

In Conversational Recommendation Systems (CRS), a user can provide feedback on recommended items at each interaction turn, leading the CRS towards more desirable recommendations. Currently, different types of CRS offer various possibilities for feedback, i.e., natural language feedback, or answering clarifying questions. In most cases, a user simulator is employed for training as well as evaluating the CRS. Such user simulators typically critique the current retrieved items based on knowledge of a single target item. Still, evaluating systems in offline settings with simulators suffers from problems, such as focusing entirely on a single target item (not addressing the exploratory nature of a recommender system), and exhibiting extreme patience (consistent feedback over a large number of turns). To overcome these limitations, we obtain extra judgements for a selection of alternative items in common CRS datasets, namely Shoes and Fashion IQ Dresses. Going further, we propose improved user simulators that allow simulated users not only to express their preferences about alternative items to their original target, but also to change their mind and level of patience. In our experiments using the relative image captioning CRS setting and different CRS models, we find that using the knowledge of alternatives by the simulator can have a considerable impact on the evaluation of existing CRS models, specifically that the existing single-target evaluation underestimates their effectiveness, and when simulated users are allowed to instead consider alternatives, the system can rapidly respond to more quickly satisfy the user.
Device-Free Human State Estimation using UWB Multi-Static Radios

We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor environment without the requirement that they carry a specific devices with them. To achieve this"device free"localization we use a small number of low-cost Ultra-Wide Band (UWB) sensors distributed across the environment of interest. To achieve high quality estimation from the UWB signals merely reflected of people in the environment, we exploit a deep network that can learn to make inferences. The hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB modules for sensing, paired with Raspberry PI units for computational processing and data transfer. We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area. Additionally, we can also estimate the number of humans that occupy this region of interest. In our approach, first, we pre-process the CIR data which involves meticulous aggregation of measurements and extraction of key statistics. Afterwards, we leverage a convolutional deep neural network to map the CIRs into precise location estimates with sub-30 cm accuracy. Similarly, we achieve accurate human activity recognition and occupancy counting results. We show that we can quickly fine-tune our model for new out-of-distribution users, a process that requires only a few minutes of data and a few epochs of training. Our results show that UWB is a promising solution for adaptable smart-home localization and activity recognition problems.
Borrowing from historical control data in a Bayesian time-to-event model with flexible baseline hazard function

There is currently a focus on statistical methods which can use historical trial information to help accelerate the discovery, development and delivery of medicine. Bayesian methods can be constructed so that the borrowing is"dynamic"in the sense that the similarity of the data helps to determine how much information is used. In the time to event setting with one historical data set, a popular model for a range of baseline hazards is the piecewise exponential model where the time points are fixed and a borrowing structure is imposed on the model. Although convenient for implementation this approach effects the borrowing capability of the model. We propose a Bayesian model which allows the time points to vary and a dependency to be placed between the baseline hazards. This serves to smooth the posterior baseline hazard improving both model estimation and borrowing characteristics. We explore a variety of prior structures for the borrowing within our proposed model and assess their performance against established approaches. We demonstrate that this leads to improved type I error in the presence of prior data conflict and increased power. We have developed accompanying software which is freely available and enables easy implementation of the approach.
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. As foundation models are in their early stages, the design of foundation model based systems has not yet been systematically explored. There is limited understanding about the impact of introducing foundation models in software architecture. Therefore, in this paper, we propose a taxonomy of foundation model based systems, which classifies and compares the characteristics of foundation models and design options of foundation model based systems. Our taxonomy comprises three categories: the pretraining and adaptation of foundation models, the architecture design of foundation model based systems, and responsible-AI-by-design. This taxonomy can serve as concrete guidance for making major architectural design decisions when designing foundation model based systems and highlights trade-offs arising from design decisions.
Synoptic and quantitative attributions of the extreme precipitation leading to the August 2016 Louisiana flood

The catastrophic August 2016 flood in the U.S. state of Louisiana was a result of intense precipitation produced by a slow‐moving tropical low‐pressure system interacting with an eastward traveling baroclinic trough to the north. While tropical‐midlatitude interactions of this nature are rare, they are not unprecedented. Analyses presented point toward the tendency for more and perhaps stronger upper level troughs propagating out of the western U.S. in summer; these then have an increasing potential to cross paths with low‐pressure systems that form around the Gulf Coast. Combined with the projected increase in precipitable water, resulting precipitation magnitude would increase. Large‐ensemble modeling indicates that the prospect of future tropical‐midlatitude interactions is a scenario that Louisiana will face in the future, while regional simulations estimate that the climate warming since 1985 may have increased the event precipitation (11–14 August 2016) on the order of 20%, all of which allude to a conceivable forecast of nonhurricane‐related, warm season extreme precipitation in the Gulf Coast states.
A multi-host mechanistic model of African swine fever emergence and control in Romania

African swine fever (ASF) has devastating effects on swine production, farmer livelihood, animal welfare, and biodiversity. Extremely difficult to control, epidemic management is further complicated when spillover between domestic pig and wild boar populations is suspected. To quantify ASF viral transmission between domestic pigs and wild boar, a spatially-explicit stochastic mechanistic model was constructed using village centroids to represent aggregated backyard pig herds and a hexagonal raster of forest coverage to represent wild boar abundance. The model was parameterized to the initial six months of the ongoing Romanian epidemic through approximate Bayesian computation. It was estimated that a median of 69.4% (interquartile range: 53.0–80.0%) of domestic pig herd cases came from other infected domestic pig herds while 20.4% (11.2–33.8%) originated from infected wild boar sources, and 8.4% (4.7–14.2%) stemmed from external sources not explicitly represented. Also, 31.9% of infected wild boar habitat (16.7–56.2%) originated from domestic pig herds and 68.1% (43.8–83.3%) came from neighboring infected wild boar populations. Furthermore, it was found that habitats with a forest coverage greater than 15% were 2.6 times more infectious and 5.3 times more susceptible than other habitats. All alternative control scenarios, including culling domestic pig herds upon local domestic pig or wild boar case detection, improved epidemic outcomes, with the greatest decrease in final epidemic size being observed from the reactive culling of entire villages following case detection. These results can be used to further inform policy recommendations in ASF-epidemic regions. Significance Statement The current African swine fever (ASF) pandemic is devastating to affected nations, and quantifying transmission parameters is critical to informing control strategies. Disease spillover between wild and domestic hosts further complicates control efforts, yet the influence of spillover events on epidemic propagation remains unknown. Using the context of Romania—one of the European nations with the most severe epidemic and where spillover transmission is strongly suspected—we show that targeting spillover mechanisms is critical for achieving holistic disease control, and then demonstrate the impact of alternative control scenarios had they been enacted. These results can inform control strategy policy decisions in the many nations at-risk for or actively experiencing ASF epidemics.
Single molecule delivery into living cells

Controlled manipulation of cultured cells by delivery of exogenous macromolecules is a cornerstone of experimental biology. Here we describe a platform which uses nanopipettes to deliver defined numbers of macromolecules into cultured cell lines and primary cells at single molecule resolution. In the nanoinjection platform the nanopipette is used as both a scanning ion conductance microscope (SICM) scanning probe and as an injection probe. The SICM is used to position the nanopipette above the cell surface, before the nanopipette is inserted into the cell into a defined location and to a predefined depth. We demonstrate that the nanoinjection platform enables the quantitative delivery of DNA, globular proteins and protein fibrils into cells with single molecule resolution and that delivery results in a phenotypic change in the cell that depends on the identity of the molecules introduced. Using experiments and computational modelling, we also show that macromolecular crowding in the cell increases the signal to noise ratio for the detection of translocation events, thus the cell itself enhances the detection of the molecules delivered.
Synthesis of gemini benzimidazolium surfactant and interaction of their micellar aggregates with antibacterial drug levofloxacin: A multi-technique approach

Abstract The cationic gemini 1,3-bis-(1-tetradecylbenzimidazolium) propane bromide (BG14) surfactant has been synthesized and the influence of the levofloxacin (Lvf), a potential antibacterial drug on the micellar properties of BG14 surfactant was observed. Hence, it would be exciting to calculate the state of micelle-formation for designing a system that would be helpful in pharmaceutical formulations. Thus, various techniques like tensiometry, cyclic voltammetry, conductivity, UV-visible, fluorescence, and time-resolved fluorescence were utilized to investigate the interaction between BG14 surfactant with Lvf. In addition, Rubingh’s model was used to evaluate various mixed micellar parameters like X1, Xideal, cmc*, β, and GE. FTIR study confirmed the existence of inter-molecular interactions among Lvf+BG14 over the studied concentration range. Lastly, this study can prove to be impactful in measuring and developing gemini-surfactant immobilized Lvf for improved biological action. GRAPHICAL ABSTRACT
Home Range of Male White-tailed Deer in Hunted and Non-hunted Populations

In many areas, hunting season coincides with rut, and movements asso- ciated with breeding activities may confound interpretation of hunting-related deer movements. This study provided an opportunity to evaluate the respective influ- ences of separate rut and hunting seasons on home range sizes of adult male deer. Home ranges of 54 radio-collared male white-tailed deer (Odocoileus virginianus) were compared between Big Cypress National Preserve (BCNP; hunted) and Ever- glades National Park (ENP; non-hunted), Florida, during 1989-1991. Annual home range size of males was larger (P = 0.001) in BCNP (7.0 km2) than in ENP (2.9 km2) and exceeded those reported for other non-migratory populations. Home ranges of adult males in BCNP were larger (P < 0.05) than those in ENP for 2 6- month hydrological and 4 3-month biological seasons. Impact of hunting season disturbances on home range sizes of BCNP deer was minor, and home ranges were similar (1990, P = 0.85; 1991, P = 0.77) among biological seasons within years. Home range sizes of deer in ENP were largest during rut and smallest during hunting and posthunting seasons. High water levels, which coincided with rut, did not appear to limit movements of males. Adult males in BCNP did not move to ENP to avoid hunting activities. Mean harvest rate in BCNP was 53%, including 67% in 1991, the wettest year of the study. High harvest rates during wet years may diminish herd productivity in subsequent years. Proc. Annu. Conf. Southeast. Assoc. Fish and Wildl. Agencies 49:389-398
Using gene complementation to identify a SulP-family bicarbonate transporter in an N2-fixing cyanobacterial endosymbiont of an open ocean diatom

Diatom-Diazotrophic Associations (DDAs) contribute significantly to new and primary production in the world’s oceans, yet the understanding of how production is sustained is poorly resolved. These symbioses involve diatoms and N2-fixing, heterocyst-forming cyanobacteria of the genus Richelia, both partners being photosynthetic. Richelia euintracellularis resides in the cytoplasm of Hemiaulus hauckii, whereas Richelia intracellularis is periplasmic in Rhizosolenia clevei. In the ocean, bicarbonate is taken up by phytoplankton to provide CO2 for photosynthesis. The genomes of both Richelia endobionts (ReuHH01 and RintRC01, respectively) contain genes encoding SulP-family proteins, which are oxyanion transporters. To study the possible involvement of these transporters in bicarbonate uptake, we used complementation of a Synechocystis sp. PCC 6803 mutant with its five CO2 uptake systems inactivated, which is unable to grow in air levels of CO2. Three genes from RintRC01 and one gene and a DNA fragment containing four partial gene sequences from ReuHH01 were chemically synthesized, cloned under the control of a strong gene promoter and incorporated in the chromosome of the Synechocystis mutant. One gene from RintRC01, RintRC_3892, complemented the Synechocystis mutant to grow with air levels of CO2 or with low bicarbonate concentrations. The complemented strain showed strong sodium-dependent, low affinity bicarbonate uptake, which, together with phylogenetic analyses, identified RintRC_3892 as a BicA protein. Additionally, RintRC_3892 transcripts were consistently detected in environmental samples from three ocean basins. No evidence for a bicarbonate transporter was found, however, for ReuHH01, suggesting different strategies for inorganic carbon uptake in the periplasmic and cytoplasmic endobionts.