Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-cleaning cellulose acetate/crystalline nanocellulose/polyvinylidene fluoride/Mg0.975Ni0.025SiO3 membrane for removal of diclofenac sodium and methylene blue dye in water

Recalcitrant pollutants present in wastewater, without an effective treatment, have several effects on aquatic ecosystems and human health due to their chemical structure and persistence. Therefore, it is crucial the development of efficient technologies to eliminate such pollutants in water. Nano-photocatalysts are considered a promising technology for water remediation; however, one common drawback is the difficulty of recovering it after water processing. One effective strategy to overcome such problem is its immobilization into substrates such as polymeric membranes. In this study, a polymeric membrane with embedded Mg0.975Ni0. 025SiO3 is proposed to remove model pollutants diclofenac sodium and methylene blue dye by synergetic adsorption and photocatalytic processes. Mg0.975Ni0. 025SiO3 was synthesized by the combustion method. The matrix polymeric blend consisting of a blend of cellulose acetate, crystalline nanocellulose and polyvinylidene fluoride was obtained by the phase inversion method. The composite membranes were characterized by FTIR, x-ray diffraction, and scanning electron microscopy. With pollutant solutions at pH 7, the pollutant adsorption capacity of the membranes reached up to 30% and 45% removal efficiencies for diclofenac sodium and methylene blue, respectively. Under simulated solar irradiation photocatalytic removal performances of 70% for diclofenac sodium pH 7, and of 97% for methylene blue dye at pH 13, were reached. The membrane photocatalytic activity allows the membrane to avoid pollutant accumulation on its surface, given a self-cleaning property that allows the reuse of at least three cycles under sunlight simulator irradiation. These results suggest the high potential of photocatalytic membranes using suitable and economical materials such as cellulosic compounds and magnesium silicates for water remediation.
Progress in microsphere-based scaffolds in bone/cartilage tissue engineering

Bone/cartilage repair and regeneration have been popular and difficult issues in medical research. Tissue engineering is rapidly evolving to provide new solutions to this problem, and the key point is to design the appropriate scaffold biomaterial. In recent years, microsphere-based scaffolds have been considered suitable scaffold materials for bone/cartilage injury repair because microporous structures can form more internal space for better cell proliferation and other cellular activities, and these composite scaffolds can provide physical/chemical signals for neotissue formation with higher efficiency. This paper reviews the research progress of microsphere-based scaffolds in bone/chondral tissue engineering, briefly introduces types of microspheres made from polymer, inorganic and composite materials, discusses the preparation methods of microspheres and the exploration of suitable microsphere pore size in bone and cartilage tissue engineering, and finally details the application of microsphere-based scaffolds in biomimetic scaffolds, cell proliferation and drug delivery systems.
Analysis on Multi-robot Relative 6-DOF Pose Estimation Error Based on UWB Range

Relative pose estimation is the foundational requirement for multi-robot system, while it is a challenging research topic in infrastructure-free scenes. In this study, we analyze the relative 6-DOF pose estimation error of multi-robot system in GNSS-denied and anchor-free environment. An analytical lower bound of position and orientation estimation error is given under the assumption that distance between the nodes are far more than the size of robotic platform. Through simulation, impact of distance between nodes, altitudes and circumradius of tag simplex on pose estimation accuracy is discussed, which verifies the analysis results. Our analysis is expected to determine parameters (e.g. deployment of tags) of UWB based multi-robot systems.
Boosting High Resolution Image Classification with Scaling-up Transformers

We present a holistic approach for high resolution image classification that won second place in the ICCV/CVPPA2023 Deep Nutrient Deficiency Challenge. The approach consists of a full pipeline of: 1) data distribution analysis to check potential domain shift, 2) backbone selection for a strong baseline model that scales up for high resolution input, 3) transfer learning that utilizes published pretrained models and continuous fine-tuning on small sub-datasets, 4) data augmentation for the diversity of training data and to prevent overfitting, 5) test-time augmentation to improve the prediction's robustness, and 6)"data soups"that conducts cross-fold model prediction average for smoothened final test results.
Shape optimization in $W^{1,\infty}$ with geometric constraints: a study in distributed-memory systems

In this paper we present a shape optimization scheme which utilizes the alternating direction method of multipliers (ADMM) to approximate a direction of steepest descent in $W^{1,\infty}$. The followed strategy is a combination of the approaches presented in Deckelnick, Herbert, and Hinze, ESAIM: COCV 28 (2022) and M\"uller et al. SIAM SISC 45 (2023). This has appeared previously for relatively simple elliptic PDEs with geometric constraints which were handled using an ad-hoc projection. Here, however, the optimization problem is expanded to include geometric constraints, which are systematically fulfilled. Moreover, this results in a nonlinear system of equations, which is challenging from a computational perspective. Simulations of a fluid dynamics case study are carried out to benchmark the novel method. Results are given to show that, compared to other methods, the proposed methodology allows for larger deformations without affecting the convergence of the used numerical methods. The mesh quality is studied across the surface of the optimized obstacle, and is further compared to previous approaches which used descents in $W^{p,\infty}$. The parallel scalability is tested on a distributed-memory system to illustrate the potential of the proposed techniques in a more complex, industrial setting.
Prognostic value of early electrographic biomarkers of epileptogenesis in high‐risk ischaemic stroke patients

Post‐stroke epilepsy (PSE) is frequent. Better prediction of PSE would enable individualized management and improve trial design for epilepsy prevention. The aim was to assess the complementary value of continuous electroencephalography (EEG) data during the acute phase compared with clinical risk factors currently used to predict PSE.
Beyond $\Lambda$CDM with $f(z)$CDM: criticalities and solutions of Pad\'e Cosmography

Recently, cosmography emerged as a valuable tool to effectively describe the vast amount of astrophysical observations without relying on a specific cosmological model. Its model-independent nature ensures a faithful representation of data, free from theoretical biases. Indeed, the commonly assumed fiducial model, the $\Lambda$CDM, shows some shortcomings and tensions between data at late and early times that need to be further investigated. In this paper, we explore an extension of the standard cosmological model by adopting the $f(z)$CDM approach, where $f(z)$ represents the cosmographic series characterizing the evolution of recent universe driven by dark energy. To construct $f(z)$, we take into account the Pad\'e series, since this rational polynomial approximation offers a better convergence at high redshifts than the standard Taylor series expansion. Several orders of such an approximant have been proposed in previous works, here we want to answer the questions: What is the impact of the cosmographic series choice on the parameter constraints? Which series is the best for the analysis? So, we analyse the most promising ones by identifying which order is preferred in terms of stability and goodness of fit. Theoretical predictions of the $f(z)$CDM model are obtained by the Boltzmann solver code and the posterior distributions of the cosmological and cosmographic parameters are constrained by a Monte Carlo Markov Chains analysis. We consider a joint data set of cosmic microwave background temperature measurements from the Planck collaboration, type Ia supernovae data from the latest Pantheon+ sample, baryonic acoustic oscillations and cosmic chronometers data. In conclusions, we state which series can be used when only late time data are used, while which orders has to be considered in order to achieve the necessary stability when large redshifts are considered.
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights

Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil the explicit equations that govern the intricate relationship between precipitation patterns and terrain characteristics in regions marked by complex terrain. Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns. These equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. Ultimately, the unveiled equations have practical applications, particularly in fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science.
Sluggish news reactions: A combinatorial approach for synchronizing stock jumps

Stock prices often react sluggishly to news, producing gradual jumps and jump delays. Econometricians typically treat these sluggish reactions as microstructure effects and settle for a coarse sampling grid to guard against them. Synchronizing mistimed stock returns on a fine sampling grid allows us to automatically detect noisy jumps and better approximate the true common jumps in related stock prices.
Detection of dominant large-scale coherent structures in turbulent pipe flow

Large-scale coherent structures are identified in turbulent pipe flow at $Re_\tau=181$ by having long lifetimes, living on large scales and travelling with a certain group velocity. A Characteristic Dynamic Mode Decomposition (CDMD) is used to detect events which meet these criteria. To this end, a temporal sequence of state vectors from Direct Numerical Simulations are rotated in space-time such that persistent dynamical modes on a hyper-surface are found travelling along its normal in space-time, which serves as the new time-like coordinate. Reconstruction of the candidate modes in physical space gives the low rank model of the flow. The modes within this subspace are highly aligned, but are separated from the remaining modes by larger angles. We are able to capture the essential features of the flow like the spectral energy distribution and Reynolds stresses with a subspace consisting of about 10 modes. The remaining modes are collected in two further subspaces, which distinguish themselves by their axial length scale and degree of isotropy.
Open Research

Background: The global population is aging, leading to shifts in health care needs. In addition to developing technology to support physical health, there is an increasing recognition of the need to consider how technology can support emotional health. This raises the question of how to design devices that older adults can interact with to log their emotions. Objective: We designed and developed 2 novel tangible devices, inspired by existing paper-based scales of emotions. The findings from a field trial of these devices with older adults are reported. Methods: Using interviews, field deployment, and fixed logging tasks, we assessed the developed devices. Results: Our results demonstrate that the tangible devices provided data comparable with standardized psychological scales of emotion. The participants developed their own patterns of use around the devices, and their experience of using the devices uncovered a variety of design considerations. We discuss the difficulty of customizing devices for specific user needs while logging data comparable to psychological scales of emotion. We also highlight the value of reflecting on sparse emotional data. Conclusions: Our work demonstrates the potential for tangible emotional logging devices. It also supports further research on whether such devices can support the emotional health of older adults by encouraging reflection of their emotional state.
Graph Neural Prompting with Large Language Models

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented generation, remains an open question. In this work, we propose Graph Neural Prompting (GNP), a novel plug-and-play method to assist pre-trained LLMs in learning beneficial knowledge from KGs. GNP encompasses various designs, including a standard graph neural network encoder, a cross-modality pooling module, a domain projector, and a self-supervised link prediction objective. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks across different LLM sizes and settings. Code is available at https://github.com/meettyj/GNP.
AI in Software Engineering: Case Studies and Prospects

Artificial intelligence (AI) and software engineering (SE) are two important areas in computer science. In recent years, researchers are trying to apply AI techniques in various stages of software development to improve the overall quality of software products. Moreover, there are also some researchers focus on the intersection between SE and AI. In fact, the relationship between SE and AI is very weak; however, methods and techniques in one area have been adopted in another area. More and more software products are capable of performing intelligent behaviour like human beings. In this paper, two cases studies which are IBM Watson and Google AlphaGo that use different AI techniques in solving real world challenging problems have been analysed, evaluated and compared. Based on the analysis of both case studies, using AI techniques such as deep learning and machine learning in software systems contributes to intelligent systems. Watson adopts 'decision making support' strategy to help human make decisions; whereas AlphaGo uses 'self-decision making' to choose operations that contribute to the best outcome. In addition, Watson learns from man-made resources such as paper; AlphaGo, on the other hand, learns from massive online resources such as photos. AlphaGo uses neural networks and reinforcement learning to mimic human brain, which might be very useful in medical research for diagnosis and treatment. However, there is still a long way to go if we want to reproduce human brain in machine and view computers as thinkers, because human brain and machines are intrinsically different. It would be more promising to see whether computers and software systems will become more and more intelligent to help with real world challenging problems that human beings cannot do.
Fair Canonical Correlation Analysis

This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps

We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unrolling. In contrast to many existing learned MR image reconstruction techniques that necessitate coil-sensitivity map (CSM) estimation as a distinct network component, our proposed approach avoids explicit CSM estimation. Instead, it implicitly captures and learns to exploit the inter-coil relationships of the images. Our method consists of a series of novel learned image and k-space blocks with shared latent information and adaptation to the acquisition parameters by feature-wise modulation (FiLM), as well as coil-wise data-consistency (DC) blocks. Our method achieved PSNR values of 34.89 and 35.56 and SSIM values of 0.920 and 0.942 in the cine track and mapping track validation leaderboard of the MICCAI STACOM CMRxRecon Challenge, respectively, ranking 4th among different teams at the time of writing. Code will be made available at https://github.com/fzimmermann89/CMRxRecon
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences

One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.
A multi-modal approach for identifying schizophrenia using cross-modal attention

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs to the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multi-modal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN) with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score.
Noise propagation in hybrid tensor networks

The hybrid tensor network (HTN) method is a general framework allowing for the construction of an effective wavefunction with the combination of classical tensors and quantum tensors, i.e., amplitudes of quantum states. In particular, hybrid tree tensor networks (HTTNs) are very useful for simulating larger systems beyond the available size of the quantum hardware. However, while the realistic quantum states in NISQ hardware are highly likely to be noisy, this framework is formulated for pure states. In this work, as well as discussing the relevant methods, i.e., Deep VQE and entanglement forging under the framework of HTTNs, we investigate the noisy HTN states by introducing the expansion operator for providing the description of the expansion of the size of simulated quantum systems and the noise propagation. This framework enables the general tree HTN states to be explicitly represented and their physicality to be discussed. We also show that the expectation value of a measured observable exponentially vanishes with the number of contracted quantum tensors. Our work will lead to providing the noise-resilient construction of HTN states.
Impedance-Readout Integrated Circuits for Electrical Impedance Spectroscopy: Methodological Review

This review article provides a comprehensive overview of impedance-readout integrated circuits (ICs) for electrical impedance spectroscopy (EIS) applications. The readout IC, a crucial component of on-chip EIS systems, significantly affects key performance metrics of the entire system, such as frequency range, power consumption, accuracy, detection range, and throughput. With the growing demand for portable, wearable, and implantable EIS systems in the Internet-of-Things (IoT) era, achieving high energy efficiency while maintaining a wide frequency range, high accuracy, wide dynamic range, and high throughput has become a focus of research. Furthermore, to enhance the miniaturization and convenience of EIS systems, many emerging systems utilize two-electrode or dry electrode configurations instead of the conventional four-electrode configuration with wet electrodes for impedance measurement. In response to these trends, various technologies have been developed to ensure reliable operations even at two- or dry-electrode interfaces. This article reviews the principles, advantages, and disadvantages of techniques employed in state-of-the-art impedance-readout ICs, aiming to achieve high energy efficiency, wide frequency range, high accuracy, wide dynamic range, low noise, high throughput, and/or high input impedance. The thorough review of these advancements will provide valuable insights into the future development of impedance-readout ICs and systems for IoT and biomedical applications.