Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach

Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice NLP techniques require large collections of relevant documents (corpus). Furthermore, NLP techniques using machine learning and deep learning techniques require labels grouping the articles based on user-defined criteria for a significant subset of a corpus in order to train the supervised model. Even labeling a few hundred documents with human subject-matter experts is a time-consuming process. To expedite this process, we developed a weak supervision-based NLP approach that leverages semantic similarity between categories and documents to (i) establish a topic-specific corpus by subsetting a large-scale open-access corpus and (ii) generate category labels for the topic-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in about 13 hours. The labeled climate and NCF corpus enable targeted, efficient identification of documents discussing a topic (or combination of topics) of interest and identification of various effects of climate change on critical infrastructure, improving the usability of scientific literature and ultimately supporting enhanced policy and decision making. To demonstrate this capability, we conduct topic modeling on pairs of climate hazards and NCFs to discover trending topics at the intersection of these categories. This method is useful for analysts and decision-makers to quickly grasp the relevant topics and most important documents linked to the topic.
1/f noise and spectral singularities in strongly disordered electronic systems

Discusses the common origin of 1/f noise in the power spectrum of random walks in a random environment and the 1/E-like spectral anomaly for tight-binding electronic systems with off-diagonal disorder. In one dimension the existence of the Dyson singularity, ( rho (E)) varies as mod ln-3 mod E mod mod / mod E mod , for the density of states of the random chain as mod E mod to 0 is inevitably linked to Sinai ultraslow diffusion with the law (x2(t)) varies as ln4t as t to 0 and the presence of S(f) varies as ln4f/f power spectral density for small frequencies f. Different forms of power-law singularities are expected instead for a correlated disorder model appropriate to describe random symmetric diffusion and magnon or phonon excitations. The two problems are discussed in terms of the averaged moments of the electronic wavefunction. The 1/E behaviour is shown to rely on the underlying very general validity of the log-normal distribution for strongly disordered electronic systems. Analytical arguments are given and numerical evidence reported for the asymptotic presence of these singularities in the dimensions d=2, 3 when the disorder is sufficiently strong.
A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images

Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.
Learning to Optimize for Reinforcement Learning

In recent years, by leveraging more data, computation, and diverse tasks, learned optimizers have achieved remarkable success in supervised learning, outperforming classical hand-designed optimizers. Reinforcement learning (RL) is essentially different from supervised learning and in practice these learned optimizers do not work well even in simple RL tasks. We investigate this phenomenon and identity three issues. First, the gradients of an RL agent vary across a wide range in logarithms while their absolute values are in a small range, making neural networks hard to obtain accurate parameter updates. Second, the agent-gradient distribution is non-independent and identically distributed, leading to inefficient meta-training. Finally, due to highly stochastic agent-environment interactions, the agent-gradients have high bias and variance, which increase the difficulty of learning an optimizer for RL. We propose gradient processing, pipeline training, and a novel optimizer structure with good inductive bias to address these issues. By applying these techniques, for the first time, we show that learning an optimizer for RL from scratch is possible. Although only trained in toy tasks, our learned optimizer can generalize to unseen complex tasks in Brax.
A low frequency sub-arcsecond view of powerful radio galaxies in rich-cluster environments: 3C 34 and 3C 320

Models of radio galaxy physics have been primarily based on high frequency (≥1 GHz) observations of their jets, hotspots, and lobes. Without highly resolved low frequency observations, which provide information on older plasma, our understanding of the dynamics of radio galaxies and their interaction with their environment is limited. Here, we present the first sub-arcsecond (0.3”) resolution images at 144 MHz of two powerful radio galaxies situated in rich cluster environments, namely 3C 34 and 3C 320, using the International Low Frequency Array Telescope. We detect for the first time at low frequencies a plethora of structures in these objects, including strikingly large filaments across the base of the lobes in both sources, which are spatially associated with dense regions in the ambient medium. For 3C 34 we report a spectral flattening in the region of the central filament, suggesting that the origin of the filaments is related to the presence of large-scale ordered magnetic fields. We also report periodic total intensity and spectral index banding of diffuse emission in the eastern lobe, seen for the first time in radio galaxy lobes. The hotspot complexes are resolved into multiple fragments of varying structure and spectral index; we discuss the implications for particle acceleration and jet termination models. We find at most smooth gradients in the spectral behaviour of the hotspot structure suggesting that particle acceleration, if present, may be occurring throughout the complex, in contrast to simple models, but different jet termination models may apply to both sources.
Relativistic analytical 
R
-matrix theory for strong-field ionization

The analytical R-matrix (ARM) theory has been known for an efficient description of the Coulomb effects of the atomic core in strong-field ionization in the nonrelativistic regime. We generalize the ARM theory into the relativistic domain aiming at the application to strong-field ionization of highly-charged ions in ultrastrong laser fields. Comparison with the relativistic Coulomb-corrected strong field approximations (SFA) is provided, highlighting the advantages and disadvantages. The weakly relativistic asymptotics and its accordance with the nondipole Coulomb-corrected SFA are examined. As an example of a physical application of the relativistic ARM, the Coulomb enhancement of tunneling ionization probability for highly-charged ions at the cutoff of the direct channel is discussed.
Resolving Left-Right Ambiguity During Bearing Only Tracking of an Underwater Target Using Towed Array

In bearing only tracking using a towed array, the array can sense the bearing angle of the target but is unable to differentiate whether the target is on the left or the right side of the array. Thus, the traditional tracking algorithm generates tracks in both the sides of the array which create difficulties when interception is required. In this paper, we propose a method based on likelihood of measurement which along with the estimators can resolve left-right ambiguity and track the target. A case study has been presented where the target moves (a) in a straight line with a near constant velocity, (b) maneuvers with a turn, and observer takes a `U'-like maneuver. The method along with the various estimators has been applied which successfully resolves the ambiguity and tracks the target. Further, the tracking results are compared in terms of the root mean square error in position and velocity, bias norm, \% of track loss and the relative execution time.
See or Hear? Exploring the Effect of Visual/Audio Hints and Gaze-assisted Instant Post-task Feedback for Visual Search Tasks in AR

Augmented reality (AR) is emerging in visual search tasks for increasingly immersive interactions with virtual objects. We propose an AR approach providing visual and audio hints along with gaze-assisted instant post-task feedback for search tasks based on mobile head-mounted display (HMD). The target case was a book-searching task, in which we aimed to explore the effect of the hints together with the task feedback with two hypotheses. H1: Since visual and audio hints can positively affect AR search tasks, the combination outperforms the individuals. H2: The gaze-assisted instant post-task feedback can positively affect AR search tasks. The proof-of-concept was demonstrated by an AR app in HMD and a comprehensive user study (n=96) consisting of two sub-studies, Study I (n=48) without task feedback and Study II (n=48) with task feedback. Following quantitative and qualitative analysis, our results partially verified H1 and completely verified H2, enabling us to conclude that the synthesis of visual and audio hints conditionally improves the AR visual search task efficiency when coupled with task feedback.
A Hybrid Training Algorithm for Continuum Deep Learning Neuro-Skin Neural Network

In this brief paper, a learning algorithm is developed for Deep Learning NeuroSkin Neural Network to improve their learning properties. Neuroskin is a new type of neural network presented recently by the authors. It is comprised of a cellular membrane which has a neuron attached to each cell. The neuron is the cells nucleus. A neuroskin is modelled using finite elements. Each element of the finite element represents a cell. Each cells neuron has dendritic fibers which connects it to the nodes of the cell. On the other hand, its axon is connected to the nodes of a number of different neurons. The neuroskin is trained to contract upon receiving an input. The learning takes place during updating iterations using sensitivity analysis. It is shown that while the neuroskin cannot present the desirable response, it improves gradually to the desired level.
Self-supervised enhancement of stimulus-evoked brain response data

Stimulus-evoked brain response data has a notoriously low signal-to-noise ratio (SNR) and high inter-subject variability. Multiple techniques have been proposed to alleviate this problem, such as averaging, denoising source separation (DSS) and (multiway) canonical correlation analysis ((M)CCA), but all these methods have significant limitations. We propose a novel paradigm for the self-supervised enhancement of stimulus-related brain response data. Different time-aligned stimulus-evoked brain responses to the same stimulus are randomly shifted in time and independently enhanced. Both enhanced brain responses are compared using a model that predicts the shift in time between the brain responses. Using a model based on a multi-view convolutional neural network as an enhancement module, we show the efficacy of our method for a downstream task of decoding the speech envelope from auditory EEG. A significant relative improvement of 32% (p<0.001) was found when using the enhanced EEG versus normal EEG. While the shown example concerns EEG in response to auditory stimulation, conceptually, our method applies to other modalities (such as MEG) and other tasks (such as visual stimulus-response modelling).
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer

Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Materials and methods: 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: The DSC, ASD, and HD95 values for the test dataset were 62.2%, 2.54mm, and 7mm, respectively. AI segmented contours were dosimetrically equivalent to the expert physician's contours. The observer study showed that expert physicians' scored AI contours (mean=3.7) higher than inexperienced physicians' contours (mean=3.1). When inexperienced physicians started with AI contours, the score improved to 3.7. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
Regularity and completeness of half-Lie groups

Half Lie groups exist only in infinite dimensions: They are smooth manifolds and topological groups such that right translations are smooth, but left translations are merely required to be continuous. The main examples are groups of $H^s$ or $C^k$ diffeomorphisms and semidirect products of a Lie group with kernel an infinite dimensional representation space. Here, we investigate mainly Banach half-Lie groups, the groups of their $C^k$-elements, extensions, and right invariant strong Riemannian metrics on them: surprisingly the full Hopf--Rinow theorem holds, which is wrong in general even for Hilbert manifolds.
Non-Hermitian zero-mode laser in a nanophotonic trimer

Symmetry-protected zero modes in arrays of coupled optical elements have attracted considerable attention because they are expected to be robust against coupling disorders. In the Hermitian limit, zero modes are dark ones, i.e. the intensity in one sublattice vanishes; yet, in a non-Hermitian counterpart, zero modes can be bright and feature {\pi}/2 phase difference between sublattices. In this work, we report on the direct observation of a lasing zero mode in a non-Hermitian three coupled nanocavity array. We show efficient excitation for nearly equal pump power in the two extreme cavities. Furthermore, its efficiency can be dynamically controlled by pumping the center cavity. The realization of zero mode lasing in large arrays of coupled nanolasers has potential applications in laser-mode engineering and it opens up promising avenues in optical computing.
DynaMIX: Resource Optimization for DNN-Based Real-Time Applications on a Multi-Tasking System

As deep neural networks (DNNs) prove their importance and feasibility, more and more DNN-based apps, such as detection and classification of objects, have been developed and deployed on autonomous vehicles (AVs). To meet their growing expectations and requirements, AVs should"optimize"use of their limited onboard computing resources for multiple concurrent in-vehicle apps while satisfying their timing requirements (especially for safety). That is, real-time AV apps should share the limited on-board resources with other concurrent apps without missing their deadlines dictated by the frame rate of a camera that generates and provides input images to the apps. However, most, if not all, of existing DNN solutions focus on enhancing the concurrency of their specific hardware without dynamically optimizing/modifying the DNN apps' resource requirements, subject to the number of running apps, owing to their high computational cost. To mitigate this limitation, we propose DynaMIX (Dynamic MIXed-precision model construction), which optimizes the resource requirement of concurrent apps and aims to maximize execution accuracy. To realize a real-time resource optimization, we formulate an optimization problem using app performance profiles to consider both the accuracy and worst-case latency of each app. We also propose dynamic model reconfiguration by lazy loading only the selected layers at runtime to reduce the overhead of loading the entire model. DynaMIX is evaluated in terms of constraint satisfaction and inference accuracy for a multi-tasking system and compared against state-of-the-art solutions, demonstrating its effectiveness and feasibility under various environmental/operating conditions.
Symbiosis of an artificial neural network and models of biological neurons: training and testing

In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh-Nagumo (FHN) system is used as a paradigmatic model demonstrating basic neuron activities. First, in order to reveal how biological neurons can be embedded within an ANN, we train the ANN with nonlinear neurons to solve a basic image recognition problem with an MNIST database; next, we describe how FHN systems can be introduced into this trained ANN. After all, we show that an ANN with FHN systems inside can be successfully trained with improved accuracy comparing with first trained ANN and then with inserted FHN systems. This approach opens up great opportunities in terms of the direction of analog neural networks, in which artificial neurons can be replaced by more appropriate biological ones.
Adversarial blockchain queues and trading on a CFMM

We describe a plausible probabilistic model for a blockchain queueing environment in which rational, profit-maximising schedulers impose adversarial disciplines on incoming messages containing a payload that encodes a state transition in a machine. The model can be specialised to apply to chains with fixed or variable block times, traditional priority queue disciplines with `honest' schedulers, or adversarial public mempools. We find conditions under which the model behaves as a bulk-service queue with priority discipline and derive practical expressions for the relative block and message number of a transaction. We study this setup in the context of orders to a CFMM DEX where the execution price a user receives may be quite sensitive to its positioning in the chain -- in particular, to a string of transactions scheduled for prior execution which is not knowable at the time of order creation. We derive statistical models for the price impact of this order flow both in the presence and absence of MEV extraction activity.
DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

Online social media platforms offer access to a vast amount of information, but sifting through the abundance of news can be overwhelming and tiring for readers. personalised recommendation algorithms can help users find information that interests them. However, most existing models rely solely on observations of user behaviour, such as viewing history, ignoring the connections between the news and a user's prior knowledge. This can result in a lack of diverse recommendations for individuals. In this paper, we propose a novel method to address the complex problem of news recommendation. Our approach is based on the idea of dual observation, which involves using a deep neural network with observation mechanisms to identify the main focus of a news article as well as the focus of the user on the article. This is achieved by taking into account the user's belief network, which reflects their personal interests and biases. By considering both the content of the news and the user's perspective, our approach is able to provide more personalised and accurate recommendations. We evaluate the performance of our model on real-world datasets and show that our proposed method outperforms several popular baselines.
Transport and noise of hot electrons in GaAs using a semianalytical model of two-phonon polar optical phonon scattering

Recent ab-initio studies of electron transport in GaAs have reported that electron-phonon (e-ph) interactions beyond the lowest order play a fundamental role in charge transport and noise phenomena. Inclusion of the next-leading-order process in which an electron scatters with two phonons was found to yield good agreement for the high-field drift velocity, but the characteristic non-monotonic trend of the power spectral density of current fluctuations (PSD) with electric field was not predicted. The high computational cost of the ab-initio approach necessitated various approximations to the two-phonon scattering term, which were suggested as possible origins of the discrepancy. Here, we report a semi-analytical transport model of two-phonon electron scattering via the Fr\"ohlich mechanism, allowing a number of the approximations in the ab-initio treatment to be lifted while retaining the accuracy to within a few percent. We compare the calculated and experimental transport and noise properties as well as scattering rates measured by photoluminescence experiments. We find quantitative agreement within 15% for the drift velocity and 25% for the $\Gamma$ valley scattering rates, and agreement with the $\Gamma-L$ intervalley scattering rates within a factor of two. Considering these results and prior studies of current noise in GaAs, we conclude that the most probable origin of the non-monotonic PSD trend versus electric field is the formation of space charge domains rather than intervalley scattering as has been assumed.
Topological fundamental groupoid. I

We show that the fundamental groupoid~\(\Pi_1(X)\) of a locally path connected semilocally simply connected space~\(X\) can be equipped with a \emph{natural} topology so that it becomes a topological groupoid; we also justify the necessity and minimality of these two hypotheses on~\(X\) in order to topologise the fundamental groupoid. We find that contrary to a belief -- especially among the Operator Algebraists -- the fundamental groupoid is not {\etale}. Further, we prove that the fundamental groupoid of a topological group, in particular a Lie group, is a \emph{transformation groupoid}; again, this result disproves a standard belief that the fundamental groupoids are \emph{far} away from being transformation groupoids. We also discuss the point-set topology on the fundamental groupoid with the intention of making it a locally compact groupoid.