Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining a Digital Health Approach for Advancing Schizophrenia Illness Self-Management and Provider Engagement: Protocol for a Feasibility Trial

Background In schizophrenia spectrum populations, adherence to treatment is poor, community-based supports are limited, and efforts to foster illness self-management have had limited success. These challenges contribute to frequent, lengthy, and costly hospital readmissions and poor functional outcomes. Digital health strategies, in turn, hold considerable promise in the effort to address these problems. Objective This feasibility trial will examine a digital health platform called App4Independence (A4i), which was designed to enhance illness self-management and treatment engagement for individuals with schizophrenia. Methods Feasibility metrics in this single-blind, randomized trial include study recruitment and retention, rate of technology use, safety, and utility in clinical interactions. Other outcome metrics include symptomatology, treatment adherence, patient-provider alliance, and quality of life. In this trial, 160 study participants with schizophrenia spectrum diagnoses will be randomized to either treatment or control conditions, with pretest-posttest outcomes measured over a 6-month period. Results This study was funded by the Canadian Institutes of Health Research in January 2020 and received Institutional Review Board approval on August 13, 2020. This study plans to begin recruiting in January 2021 and will be completed within 3 years. Data collection is projected to begin in January 2021. Conclusions This research will provide critical information for the development of this new technology in the larger effort to address a key problem in the schizophrenia field—how to leverage technology to enhance illness self-management and care engagement in resource-limited service contexts. International Registered Report Identifier (IRRID) PRR1-10.2196/24736
Social Aspects and How They Inﬂuence MSECO Developers

— Mobile software ecosystem (MSECO) is a new software development paradigm for mobile technologies, having three main dimensions, namely: Technical, Business and Social. The literature has a considerable number of studies on technical and business dimensions, but only a few studies focus on the social aspects of MSECOs. However, the literature has enough to provide evidence that the actors involved, such as developers, are crucial to an MSECO. This study aims to complement earlies studies by describing new social factors that inﬂuence developers to work in a MSECO. We conducted a systematic literature review in order to identify these new factors, and a ﬁeld study in which 20 developers were interviewed to understand how these factors can inﬂuence them to join or keep participating in a MSECO. We found that developer become more rigorous to continue participating then to adopt a MSECO.
Fire Sales in Finance and Macroeconomics

Fire sales are forced sales of assets in which high-valuation bidders are sidelined, typically due to debt overhang problems afflicting many specialist bidders simultaneously. We overview theoretical and empirical research on asset fire sales, which shows how they can arise, how they can lead to asset under-valuations, how contracts and bankruptcy regimes adjust to the risk of fire sales, how fire sales can lead to downward spirals or cascades in asset prices, how arbitrage fails in the presence of fire sales, and how fire sales can reduce productive investment. We conclude by showing how asset fire sales shed light on several aspects of the recent financial crisis, and can account for the success of the liquidity provision and asset purchase policies of the Federal Reserve.
Population Status and Characteristics of Machybopsis Gelida, Platygobio gracilis and Rhinicythys cataractae in the Missouri River Basin

Few sturgeon chub (Macrhybopsis gelida: Cyprinidae) have been collected in the last decade, therefore their status, habitat selection and morphology were investigated at 172 previous collection sites. Data on two ecologically similar species, Platygobio gracilis and Rhinichthys cataractae, were also collected. Macrhvbopsis gelida were collected at 28 sites on the Powder River (n = 158) in Wyoming and Montana, and at one site on the Yellowstone River (n = 1) in Montana. Age-I, Age-II and Age-III M. gelida were collected. Specimens were 37 to 95 mm in total length. Macrhvbopsis gelida were ripe in mid-June, at water temperatures of 18.3-22. 2C, and produced about 5, 000 eggs. Sites where M. gelida were collected in the Powder River typically had flows > 0.35 m/sec and substrates with� 40% gravel/rubble. Percent species composition of M, gelida at riffle sites on the Powder River declined from 28% in 1979-80 to about 3% in 1989-90. Morphometric characters of M. gelida were similar throughout the Powder River, except for head width, snout length and body depth. Counts of M. gelida anal, dorsal, pelvic and pectoral fin rays; lateral
Signal and noise analysis of a metal oxide transistor-based flat-panel detector

Recently, metal-oxide thin-film transistor (TFT)-based flat-panel x-ray detectors have attracted attention owing to their fast readout times and low noise characteristics. We empirically analyzed the signal and noise characteristics of an indium gallium zinc oxide (IGZO) TFT-based detector in comparison with those of a conventional hydrogenated amorphous silicon (a-Si:H) TFT-based detector. We compared the large-area signal transfer functions of the detectors as a function of air kerma at their entrance surfaces. Signal and noise performances were evaluated by measuring the modulation-transfer function, noise-power spectrum, and detective quantum efficiency (DQE). The low-dose imaging capability of the detectors was assessed by investigating the large-area or zero-frequency DQE as a function of air kerma. Herein, we evaluated the value of the IGZO detector in terms of dose efficiency in comparison to the conventional a-Si:H detector.
A Severe Challenge to the Modified Newtonian Dynamics Phenomenology in Our Galaxy

Modified Newtonian dynamics (MOND) is one of the most popular alternative theories of dark matter to explain the missing mass problem in galaxies. Although it remains controversial regarding MOND as a fundamental theory, MOND phenomenology has been shown to widely apply in different galaxies, which poses challenges to the standard Lambda cold dark matter model. In this article, we derive analytically the galactic rotation curve gradient in the MOND framework and present a rigorous analysis to examine the MOND phenomenology in our Galaxy. By assuming a benchmark baryonic disk density profile and two popular families of MOND interpolating functions, we show for the first time that the recent discovery of the declining Galactic rotation curve in the outer region (R ≈ 17–23 kpc) can almost rule out the MOND phenomenology at more than 5σ. This strongly supports some of the previous studies claiming that MOND is neither a fundamental theory nor a universal description of galactic properties.
First passage duality in $d>2$

In one and two dimensions, the first-passage time for a diffusing particle in the presence of a radial potential flow to hit a sphere, conditioned on actually hitting the sphere, is independent of the sign of the drift. Moreover, the first-passage distribution for an inward (negative) drift is identical to the first-passage distribution for outward (positive) drift when conditioned on hitting the sphere. This curious property was first observed in 2018 by Krapivsky and Redner, and in this work we extend this duality of first-passage times and their distributions to dimensions $d>2$. We show that the fundamental source of the symmetry is the fact that a reversal of the direction of the drift amounts to taking the adjoint of the spatial operator governing the evolution of the process, and the factor that converts between solutions of the forward and backward equations is precisely the non-transient behavior of the system with which to appropriately condition by.
Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias

Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-protein interactions but performance is highly dependent on training data; models have been shown to accurately predict interactions when the proteins involved are included in the training data, but achieve consistently poorer results when applied to previously unseen proteins. In addition, models that are trained using proteins that take part in multiple interactions can suffer from representation bias, where predictions are driven not by learned biological features but by learning of the structure of the interaction dataset. We present a method for extracting unique pairs from an interaction dataset, generating non-redundant paired data for unbiased machine learning. After applying the method to datasets containing _Arabidopsis thaliana_ and pathogen effector interations, we developed a convolutional neural network model capable of learning and predicting interactions from Chaos Game Representations of proteins' coding genes.
Boosting for Bounding the Worst-class Error

This paper tackles the problem of the worst-class error rate, instead of the standard error rate averaged over all classes. For example, a three-class classification task with class-wise error rates of 10\%, 10\%, and 40\% has a worst-class error rate of 40\%, whereas the average is 20\% under the class-balanced condition. The worst-class error is important in many applications. For example, in a medical image classification task, it would not be acceptable for the malignant tumor class to have a 40\% error rate, while the benign and healthy classes have 10\% error rates.We propose a boosting algorithm that guarantees an upper bound of the worst-class training error and derive its generalization bound. Experimental results show that the algorithm lowers worst-class test error rates while avoiding overfitting to the training set.
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate

Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a significant challenge for learning and establishing long-range dependencies. Additionally, gated RNNs tend to be over-parameterized, resulting in poor network generalization. To address these challenges, we propose a novel Delayed Memory Unit (DMU) in this paper, wherein a delay line structure, coupled with delay gates, is introduced to facilitate temporal interaction and temporal credit assignment, so as to enhance the temporal modeling capabilities of vanilla RNNs. Particularly, the DMU is designed to directly distribute the input information to the optimal time instant in the future, rather than aggregating and redistributing it over time through intricate network dynamics. Our proposed DMU demonstrates superior temporal modeling capabilities across a broad range of sequential modeling tasks, utilizing considerably fewer parameters than other state-of-the-art gated RNN models in applications such as speech recognition, radar gesture recognition, ECG waveform segmentation, and permuted sequential image classification.
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities

Multi-label text classification is a critical task in the industry. It helps to extract structured information from large amount of textual data. We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance by employing a Bi-Encoder Transformer architecture that utilizes concatenation, subtraction, and multiplication of embeddings on both text and topic. Text2Topic also supports zero-shot predictions, produces domain-specific text embeddings, and enables production-scale batch-inference with high throughput. The final model achieves accurate and comprehensive results compared to state-of-the-art baselines, including large language models (LLMs). In this study, a total of 239 topics are defined, and around 1.6 million text-topic pairs annotations (in which 200K are positive) are collected on approximately 120K texts from 3 main data sources on Booking.com. The data is collected with optimized smart sampling and partial labeling. The final Text2Topic model is deployed on a real-world stream processing platform, and it outperforms other models with 92.9% micro mAP, as well as a 75.8% macro mAP score. We summarize the modeling choices which are extensively tested through ablation studies, and share detailed in-production decision-making steps.
Physics informed neural networks learning a two-qubit Hamiltonian

Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN is the universal approximation theorem, which claims that any function can be approximate by a neural network if it contains enough complexity. Consequently, a neural network can be a solution of a physical model. Moreover, by means of extra data provided by the user, intrinsic physical parameters can be extracted from the approach called inverse-PINN. Here, we apply inverse-PINN with the goal of extracting all the physical parameters that constitutes a two qubit Hamiltonian. We find that this approach is very efficient. To probe the robustness of the inverse-PINN to learn the Hamiltonian of a two-qubit system, we use the IBM quantum computers as experimental platforms to obtain the data that is plugged in the PINN. We found that our method is able to predict the two-qubit parameters with 5% of accuracy on average.
The primacy bias in Model-based RL

The primacy bias in deep reinforcement learning (DRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of DRL algorithms. Previous studies have shown that employing simple techniques, such as resetting the agent's parameters, can substantially alleviate the primacy bias. However, we observe that resetting the agent's parameters harms its performance in the context of model-based reinforcement learning (MBRL). In fact, on further investigation, we find that the primacy bias in MBRL differs from that in model-free RL. In this work, we focus on investigating the primacy bias in MBRL and propose world model resetting, which works in MBRL. We apply our method to two different MBRL algorithms, MBPO and DreamerV2. We validate the effectiveness of our method on multiple continuous control tasks on MuJoCo and DeepMind Control Suite, as well as discrete control tasks on Atari 100k benchmark. The results show that world model resetting can significantly alleviate the primacy bias in model-based setting and improve algorithm's performance. We also give a guide on how to perform world model resetting effectively.
Interference Management by Harnessing Multi-Domain Resources in Spectrum-Sharing Aided Satellite-Ground Integrated Networks

A spectrum-sharing satellite-ground integrated network is conceived, consisting of a pair of non-geostationary orbit (NGSO) constellations and multiple terrestrial base stations, which impose the co-frequency interference (CFI) on each other. The CFI may increase upon increasing the number of satellites. To manage the potentially severe interference, we propose to rely on joint multi-domain resource aided interference management (JMDR-IM). Specifically, the coverage overlap of the constellations considered is analyzed. Then, multi-domain resources - including both the beam-domain and power-domain - are jointly utilized for managing the CFI in an overlapping coverage region. This joint resource utilization is performed by relying on our specifically designed beam-shut-off and switching based beam scheduling, as well as on long short-term memory based joint autoregressive moving average assisted deep Q network aided power scheduling. Moreover, the outage probability (OP) of the proposed JMDR-IM scheme is derived, and the asymptotic analysis of the OP is also provided. Our performance evaluations demonstrate the superiority of the proposed JMDR-IM scheme in terms of its increased throughput and reduced OP.
MGAS: Multi-Granularity Architecture Search for Effective and Efficient Neural Networks

—Differentiable architecture search (DAS) revolution-izes neural architecture search (NAS) with time-efficient automation, transitioning from discrete candidate sampling and evaluation to differentiable super-net optimization and discretization. However, existing DAS methods either only conduct coarse-grained operation-level search or manually define the remaining ratios for fine-grained kernel-level and weight-level units, which fail to simultaneously optimize model size and model performance. Furthermore, these methods compromise search quality to reduce memory consumption. To tackle these issues, we introduce multi-granularity architecture search (MGAS), a unified framework which aims to comprehensively and memory-efficiently explore the multi-granularity search space to discover both effective and efficient neural networks. Specifically, we learn discretization functions specific to each granularity level to adaptively determine the remaining ratios according to the evolving architecture. This ensures an optimal balance among units of different granularity levels for different target model sizes. Considering the memory demands, we break down the super-net optimization and discretization into multiple sub-net stages. Nevertheless, the greedy nature of this approach may introduce bias in the early stages. To compensate for the bias, we propose progressive re-evaluation to allow for re-pruning and regrowing of previous units during subsequent stages. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate that MGAS outperforms other state-of-the-art methods in achieving a better trade-off between model performance and model size.
Intuitive Multilingual Audio-Visual Speech Recognition with a Single-Trained Model

We present a novel approach to multilingual audio-visual speech recognition tasks by introducing a single model on a multilingual dataset. Motivated by a human cognitive system where humans can intuitively distinguish different languages without any conscious effort or guidance, we propose a model that can capture which language is given as an input speech by distinguishing the inherent similarities and differences between languages. To do so, we design a prompt fine-tuning technique into the largely pre-trained audio-visual representation model so that the network can recognize the language class as well as the speech with the corresponding language. Our work contributes to developing robust and efficient multilingual audio-visual speech recognition systems, reducing the need for language-specific models.
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias

Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treating them as labeled examples. For neural networks, softmax prediction probabilities are often used as a confidence measure, although they are known to be overconfident, even for wrong predictions. This phenomenon is particularly intensified in the presence of sample selection bias, i.e., when data labeling is subject to some constraint. To address this issue, we propose a novel confidence measure, called $\mathcal{T}$-similarity, built upon the prediction diversity of an ensemble of linear classifiers. We provide the theoretical analysis of our approach by studying stationary points and describing the relationship between the diversity of the individual members and their performance. We empirically demonstrate the benefit of our confidence measure for three different pseudo-labeling policies on classification datasets of various data modalities. The code is available at https://github.com/ambroiseodt/tsim.
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis, the same piece of data is typically assigned to at least two human coders. Moreover, to produce meaningful and useful analysis, human coders develop and deepen their data interpretation and coding over multiple iterations, making TA labor-intensive and time-consuming. Recently the emerging field of large language models (LLMs) research has shown that LLMs have the potential replicate human-like behavior in various tasks: in particular, LLMs outperform crowd workers on text-annotation tasks, suggesting an opportunity to leverage LLMs on TA. We propose a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct TA with in-context learning (ICL). This framework provides the prompt to frame discussions with a LLM (e.g., GPT-3.5) to generate the final codebook for TA. We demonstrate the utility of this framework using survey datasets on the aspects of the music listening experience and the usage of a password manager. Results of the two case studies show that the proposed framework yields similar coding quality to that of human coders but reduces TA's labor and time demands.
Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations

Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct applicability is limited in models where observations possess nonlinear relationships with unknown parameters. Recently, a semi-analytical Bayesian framework known as nonlinear sparse Bayesian learning (NSBL) was introduced by the authors to induce sparsity among model parameters during the Bayesian inversion of nonlinear-in-parameter models. NSBL relies on optimally selecting the hyperparameters of sparsity-inducing Gaussian priors. It is inherently an approximate method since the uncertainty in the hyperparameter posterior is disregarded as we instead seek the maximum a posteriori (MAP) estimate of the hyperparameters (type-II MAP estimate). This paper aims to investigate the hierarchical structure that forms the basis of NSBL and validate its accuracy through a comparison with a one-level hierarchical Bayesian inference as a benchmark in the context of three numerical experiments: (i) a benchmark linear regression example with Gaussian prior and Gaussian likelihood, (ii) the same regression problem with a highly non-Gaussian prior, and (iii) an example of a dynamical system with a non-Gaussian prior and a highly non-Gaussian likelihood function, to explore the performance of the algorithm in these new settings. Through these numerical examples, it can be shown that NSBL is well-suited for physics-based models as it can be readily applied to models with non-Gaussian prior distributions and non-Gaussian likelihood functions. Moreover, we illustrate the accuracy of the NSBL algorithm as an approximation to the one-level hierarchical Bayesian inference and its ability to reduce the computational cost while adequately exploring the parameter posteriors.