Miracle-dz/UniFAR-FTUs huggingface dataset:
FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure

Automated image analysis of microscopic images such as protein crystallization images and cellular images is one of the important research areas. If objects in a scene appear at different depths with respect to the camera's focal point, objects outside the depth of field usually appear blurred. Therefore, scientists capture a collection of images with different depths of field. Focal stacking is a technique of creating a single focused image from a stack of images collected with different depths of field. In this paper, we introduce a novel focal stacking technique, FocusALL, which is based on our modified Harris Corner Response Measure. We also propose enhanced FocusALL for application on images collected under high resolution and varying illumination. FocusALL resolves problems related to the assumption that focus regions have high contrast and high intensity. Especially, FocusALL generates sharper boundaries around protein crystal regions and good in focus images for high resolution images in reasonable time. FocusALL outperforms other methods on protein crystallization images and performs comparably well on other datasets such as retinal epithelial images and simulated datasets.
Train&Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry. In this article, we present new work on the generation of tongue-twisters - a form of language that is required to be conditioned on a phoneme level to maximize sound overlap, whilst maintaining semantic consistency with an input topic and still being grammatically correct. We present TwisterLister, a pipeline for generating phonologically informed tongue-twisters from Large Language Models (LLMs) that we use to generate TwistList 2.0, the largest annotated dataset of tongue-twisters to date, consisting of 17K+ examples from a combination of human and LLM authors. Our generation pipeline involves the use of a phonologically constrained vocabulary alongside LLM prompting to generate novel, non-derivative tongue-twister examples. We additionally present the results of automatic and human evaluation of smaller models trained on our generated dataset to demonstrate the extent to which phonologically motivated language types can be generated without explicit injection of phonological knowledge. Additionally, we introduce a Phoneme-Aware Constrained Decoding module (PACD) that can be integrated into any causal language model and demonstrate that this method generates good quality tongue-twisters both with and without fine-tuning the underlying language model. We also design and implement a range of automatic metrics for the task of tongue-twister generation that is phonologically motivated and captures the unique essence of tongue-twisters based on Phonemic Edit Distance (PED).
Novel Complementary Resistive Switch Crossbar Memory Write and Read Schemes

Recent trends in emerging nonvolatile memory systems necessitate efficient read/write (R/W) schemes. Efficient solutions with zero sneak path current, nondestructive R/W operations, minimum area and low power are some of the key requirements. Toward this end, we propose a novel crossbar memory scheme using a configuration row of cells for assisting R/W operations. The proposed write scheme minimizes the overall power consumption compared to the previously proposed write schemes and reduces the state drift problem. We also propose two read schemes, namely, assisted-restoring and self-resetting read. In assisted-restoring scheme, we use the configuration cells which are used in the write scheme, whereas we implement additional circuitry for self-reset which addresses the problem of destructive read. Moreover, by formulating an analytical model of R/W operation, we compare the various schemes. The overhead for the proposed assisted-restoring write/read scheme is an extra redundant row for the given crossbar array. For a typical array size of 200 × 200 the area overhead is about 0.5%, however, there is a 4X improvement in power consumption compared to the recently proposed write schemes. Quantitative analysis of the proposed scheme is analyzed by using simulation and analytical models.
Designing Robust Linear Output Feedback Controller based on CLF-CBF framework via Linear~Programming(LP-CLF-CBF)

We consider the problem of designing output feedback controllers that use measurements from a set of landmarks to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. We propose two objectives for navigating in an environment, one to traverse the environment by making loops and one by converging to a stabilization point while smoothing the transition between consecutive cells. We test our algorithms in a simulation environment, evaluating the robustness of the approach to practical conditions, such as bearing-only measurements, and measurements acquired with a camera with a limited field of view.
An empirical appraisal of methods for the dynamic prediction of survival with numerous longitudinal predictors

Recently, the increasing availability of repeated measurements in biomedical studies has motivated the development of several statistical methods for the dynamic prediction of survival in settings where a large (potentially high-dimensional) number of longitudinal covariates is available. These methods differ in both how they model the longitudinal covariates trajectories, and how they specify the relationship between the longitudinal covariates and the survival outcome. Because these methods are still quite new, little is known about their applicability, limitations and performance when applied to real-world data. To investigate these questions, we present a comparison of the predictive performance of the aforementioned methods and two simpler prediction approaches to three datasets that differ in terms of outcome type, sample size, number of longitudinal covariates and length of follow-up. We discuss how different modelling choices can have an impact on the possibility to accommodate unbalanced study designs and on computing time, and compare the predictive performance of the different approaches using a range of performance measures and landmark times.
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network

Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitations, especially in scenarios where there is a lack of interaction data for new items. Knowledge graph (KG)-based recommendation systems have emerged as a promising solution. However, most KG-based methods adopt node embeddings, which do not provide personalized recommendations for different users and cannot generalize well to the new items. To address these limitations, we propose Knowledge-enhanced User-Centric subgraph Network (KUCNet), a subgraph learning approach with graph neural network (GNN) for effective recommendation. KUCNet constructs a U-I subgraph for each user-item pair that captures both the historical information of user-item interactions and the side information provided in KG. An attention-based GNN is designed to encode the U-I subgraphs for recommendation. Considering efficiency, the pruned user-centric computation graph is further introduced such that multiple U-I subgraphs can be simultaneously computed and that the size can be pruned by Personalized PageRank. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items. Experimental results demonstrate the superiority of KUCNet over state-of-the-art KG-based and collaborative filtering (CF)-based methods.
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey

Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of {\em Explainable Interfaces} (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.
p120-catenin controls contractility along the vertical axis of epithelial lateral membranes

ABSTRACT In vertebrate epithelia, p120-catenin (hereafter referred to as p120; also known as CTNND1) mediates E-cadherin stability and suppression of RhoA. Genetic ablation of p120 in various epithelial tissues typically causes striking alterations in tissue function and morphology. Although these effects could very well involve p120's activity towards Rho, ascertaining the impact of this relationship has been complicated by the fact that p120 is also required for cell–cell adhesion. Here, we have molecularly uncoupled p120's cadherin-stabilizing and RhoA-suppressing activites. Unexpectedly, removing p120's Rho-suppressing activity dramatically disrupted the integrity of the apical surface, irrespective of E-cadherin stability. The physical defect was tracked to excessive actomyosin contractility along the vertical axis of lateral membranes. Thus, we suggest that p120's distinct activities towards E-cadherin and Rho are molecularly and functionally coupled and this, in turn, enables the maintenance of cell shape in the larger context of an epithelial monolayer. Importantly, local suppression of contractility by cadherin-bound p120 appears to go beyond regulating cell shape, as loss of this activity also leads to major defects in epithelial lumenogenesis. Highlighted Article: The distinct activities of p120 towards E-cadherin and Rho are molecularly and functionally coupled, and this in turn enables the maintenance of cell shape in the larger context of an epithelial monolayer.
On the Concept Trustworthiness in Concept Bottleneck Models

Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck. However, despite the transparency of the concept-to-label prediction, the mapping from the input to the intermediate concept remains a black box, giving rise to concerns about the trustworthiness of the learned concepts (i.e., these concepts may be predicted based on spurious cues). The issue of concept untrustworthiness greatly hampers the interpretability of CBMs, thereby hindering their further advancement. To conduct a comprehensive analysis on this issue, in this study we establish a benchmark to assess the trustworthiness of concepts in CBMs. A pioneering metric, referred to as concept trustworthiness score, is proposed to gauge whether the concepts are derived from relevant regions. Additionally, an enhanced CBM is introduced, enabling concept predictions to be made specifically from distinct parts of the feature map, thereby facilitating the exploration of their related regions. Besides, we introduce three modules, namely the cross-layer alignment (CLA) module, the cross-image alignment (CIA) module, and the prediction alignment (PA) module, to further enhance the concept trustworthiness within the elaborated CBM. The experiments on five datasets across ten architectures demonstrate that without using any concept localization annotations during training, our model improves the concept trustworthiness by a large margin, meanwhile achieving superior accuracy to the state-of-the-arts. Our code is available at https://github.com/hqhQAQ/ProtoCBM.
Dynamic Weakening and the Depth Dependence of Earthquake Faulting

The Earth’s seismicity is largely confined to the upper crust, typically above the 600 °C isotherm [McKenzie et al., 2005], where rocks accommodate deformation by brittle failure. The maximum strength of the seismogenic crust is generally assumed to be the frictional resistance to sliding on an optimally oriented fault plane, which is well approximated by a frictional rupture envelope with a friction coefficient between 0.6 and 0.8 [e.g., Brace and Kohlstedt, 1980; Kohlstedt et al., 1995]. However, this strength estimate is local and static: it is a measure of whether a faulted rock can slide or not. As such, it bears no information about the stability of fault slip, which determines whether frictional deformation is steady and stable or whether earthquakes (i.e., unstable, fast, slip events) dominate the dynamics of faulting. Determining when earthquake nucleation occurs requires additional knowledge about the details of the frictional constitutive law on the fault. In the framework of rate and state constitutive friction laws, a number of authors [e.g., Dieterich, 1978; Ruina, 1983] have demonstrated that the key parameter controlling the nucleation of earthquakes is the rate dependency of friction: steady‐ state velocity‐strengthening faults slide stably while steady‐state velocity‐weakening faults are prone to dynamic instabilities. These theoretical considerations are sup­ ported by experimental and geological observations that show the depth distribution of seismicity in the crust and in subduction zones closely matches the depth depend­ ence of the velocity‐strengthening/weakening parameter of fault rocks, with shallow seismicity markedly decaying Dynamic Weakening and the Depth Dependence of Earthquake Faulting
Sitagliptin Prevents Inflammation and Apoptotic Cell Death in the Kidney of Type 2 Diabetic Animals

This study aimed to evaluate the efficacy of sitagliptin, a dipeptidyl peptidase IV (DPP-IV) inhibitor, in preventing the deleterious effects of diabetes on the kidney in an animal model of type 2 diabetes mellitus; the Zucker diabetic fatty (ZDF) rat: 20-week-old rats were treated with sitagliptin (10 mg/kg bw/day) during 6 weeks. Glycaemia and blood HbA1c levels were monitored, as well as kidney function and lesions. Kidney mRNA and/or protein content/distribution of DPP-IV, GLP-1, GLP-1R, TNF-α, IL-1β, BAX, Bcl-2, and Bid were evaluated by RT-PCR and/or western blotting/immunohistochemistry. Sitagliptin treatment improved glycaemic control, as reflected by the significantly reduced levels of glycaemia and HbA1c (by about 22.5% and 1.2%, resp.) and ameliorated tubulointerstitial and glomerular lesions. Sitagliptin prevented the diabetes-induced increase in DPP-IV levels and the decrease in GLP-1 levels in kidney. Sitagliptin increased colocalization of GLP-1 and GLP-1R in the diabetic kidney. Sitagliptin also decreased IL-1β and TNF-α levels, as well as, prevented the increase of BAX/Bcl-2 ratio, Bid protein levels, and TUNEL-positive cells which indicates protective effects against inflammation and proapoptotic state in the kidney of diabetic rats, respectively. In conclusion, sitagliptin might have a major role in preventing diabetic nephropathy evolution due to anti-inflammatory and antiapoptotic properties.
Synchronization in random networks of identical phase oscillators: A graphon approach

Networks of coupled nonlinear oscillators have been used to model circadian rhythms, flashing fireflies, Josephson junction arrays, high-voltage electric grids, and many other kinds of self-organizing systems. Recently, several authors have sought to understand how coupled oscillators behave when they interact according to a random graph. Here we consider interaction networks generated by a graphon model known as a $W$-random network, and examine the dynamics of an infinite number of identical phase oscillators, following an approach pioneered by Medvedev. We show that with sufficient regularity on $W$, the solution to the dynamical system over a $W$-random network of size $n$ converges in the $L^{\infty}$ norm to the solution of the continuous graphon system, with high probability as $n\rightarrow\infty$. This result suggests a framework for studying synchronization properties in large but finite random networks. In this paper, we leverage our convergence result in the $L^{\infty}$ norm to prove synchronization results for two classes of identical phase oscillators on Erd\H{o}s-R\'enyi random graphs. First, we show that the Kuramoto model on the Erd\H{o}s-R\'enyi graph $G(n, \alpha_n)$ achieves phase synchronization with high probability as $n$ goes to infinity, if the edge probability $\alpha_n$ exceeds $(\log n)/n$, the connectivity threshold of an Erd\H{o}s-R\'enyi random graph. Then we show that the Sakaguchi-Kuramoto model on the Erd\H{o}s-R\'enyi graph $G(n, p)$ achieves frequency synchronization with high probability as $n$ goes to infinity, assuming a fixed edge probability $p\in(0,1]$ and a certain regime for the model's phase shift parameter. A notable feature of the latter result is that it holds for an oscillator model whose dynamics are not simply given by a gradient flow.
Towards an Improved Performance Measure for Language Models

In this paper a first attempt at deriving an improved performance measure for language models, the probability ratio measure (PRM) is described. In a proof of concept experiment, it is shown that PRM correlates better with recognition accuracy and can lead to better recognition results when used as the optimisation criterion of a clustering algorithm. Inspite of the approximations and limitations of this preliminary work, the results are very encouraging and should justify more work along the same lines.
Identification and Genomic Analysis of a Novel Group C Orthobunyavirus Isolated from a Mosquito Captured near Iquitos, Peru

Group C orthobunyaviruses are single-stranded RNA viruses found in both South and North America. Until very recently, and despite their status as important vector-borne human pathogens, no Group C whole genome sequences containing all three segments were available in public databases. Here we report a Group C orthobunyavirus, named El Huayo virus, isolated from a pool of Culex portesi mosquitoes captured near Iquitos, Peru. Although initial metagenomic analysis yielded only a handful of reads belonging to the genus Orthobunyavirus, single contig assemblies were generated for L, M, and S segments totaling over 200,000 reads (~0.5% of sample). Given the moderately high viremia in hamsters (>107 plaque-forming units/ml) and the propensity for Cx. portesi to feed on rodents, it is possible that El Huayo virus is maintained in nature in a Culex portesi/rodent cycle. El Huayo virus was found to be most similar to Peruvian Caraparu virus isolates and constitutes a novel subclade within Group C.
A Control Barrier Function Composition Approach for Multi-Agent Systems in Marine Applications

The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints. A nominal controller designed for reference tracking may not guarantee the marine MAS stays safe w.r. t. these constraints. To modify the nominal input as one that enforces safety, we introduce a framework to systematically encode the relative-pose constraints as nonsmooth control barrier functions (NCBFs) and combine them as a single NCBF using Boolean composition, which enables a simplified verification process compared to using the NCBFs individually. While other relative-pose constraint functions have explicit derivatives, the challenging line-of-sight constraint is encoded with the minimum distance function between the line-of-sight set and other agents, whose derivative is not explicit. Hence, existing safe control design methods that consider composite NCBFs cannot be applied. To address this challenge, we propose a novel quadratic program formulation based on the dual of the minimum distance problem and develop a new theory to ensure the resulting control input guarantees constraint satisfaction. Lastly, we validate the effectiveness of our proposed framework on a simulated large-scale marine MAS and a real-world marine MAS comprising one Unmanned Surface Vehicle and two Unmanned Underwater Vehicles.
Spanning Multi-Asset Payoffs With ReLUs

We propose a distributional formulation of the spanning problem of a multi-asset payoff by vanilla basket options. This problem is shown to have a unique solution if and only if the payoff function is even and absolutely homogeneous, and we establish a Fourier-based formula to calculate the solution. Financial payoffs are typically piecewise linear, resulting in a solution that may be derived explicitly, yet may also be hard to numerically exploit. One-hidden-layer feedforward neural networks instead provide a natural and efficient numerical alternative for discrete spanning. We test this approach for a selection of archetypal payoffs and obtain better hedging results with vanilla basket options compared to industry-favored approaches based on single-asset vanilla hedges.
From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation

Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evolution, QE has yielded a wealth of results. This article provides a comprehensive overview of QE datasets, annotation methods, shared tasks, methodologies, challenges, and future research directions. It begins with an introduction to the background and significance of QE, followed by an explanation of the concepts and evaluation metrics for word-level QE, sentence-level QE, document-level QE, and explainable QE. The paper categorizes the methods developed throughout the history of QE into those based on handcrafted features, deep learning, and Large Language Models (LLMs), with a further division of deep learning-based methods into classic deep learning and those incorporating pre-trained language models (LMs). Additionally, the article details the advantages and limitations of each method and offers a straightforward comparison of different approaches. Finally, the paper discusses the current challenges in QE research and provides an outlook on future research directions.