Miracle-dz/UniFAR-FTUs huggingface dataset:
Occupational Therapy in Mental Health via Telehealth during the COVID-19 Pandemic

The COVID-19 pandemic has brought about changes in mental health occupational therapy. Research into these changes and the associated risks of relapse is insufficient. To explore the changes that have taken place in forms of occupational intervention (face-to-face and online) during the pandemic, and to analyze their association with subsequent relapses, a multicenter retrospective cohort study was carried out of 270 patients with mental disorder diagnoses under follow-up in day hospitals during 2020. Our results show that the frequency of face-to-face occupational therapy interventions decreased during lockdown and subsequently recovered. Interventions via telehealth increased during lockdown and have since been continued to a greater extent than before lockdown. Patients who received occupational intervention via telehealth relapsed less in the following six months (10.7% vs. 26.3%; χ2 = 10.372; p = 0.001), especially those who received intervention via videoconferencing (4.2% vs. 22%; χ2 = 5.718; p = 0.017). In conclusion, lockdown subsequent to the COVID-19 outbreak led to a reduction in face-to-face occupational therapy interventions, putting people with prior mental disorders at risk, while the implementation of telehealth tools helped reduce relapses.
Application of Blockchain for Supply Chain Financing: Explaining the Drivers Using SEM

Owing to the lack of research in emerging Asian nations, this research aimed to unearth the determinants of blockchain acceptance for supply chain financing by a Bangladeshi financing company called IPDC. Centred on a technology acceptance framework called UTAUT (unified theory of acceptance and use of technology) and open innovation research, an expanded model with a mediating variable is developed for this study. This research work employs the deductive inference method in conjunction with the positivism paradigm. A structural questionnaire was used to gather data, which were then processed through Smart-PLS (partial least square) for SEM (structural equation modeling). The survey includes all the people who are directly or indirectly involved in the supply chain financing platform of IPDC. The study consists of seven direct hypotheses and one mediating hypothesis. The results show that all the direct hypotheses except the impact of social influence on the behavioural intention to use (BINTU) blockchain are significant. The mediating hypothesis indicating the role of BINTU in the relationship between facilitating conditions (FCON) and the actual use of blockchain is also supported. FCON and BINTU together explain 88.7% variation in blockchain use behaviour for supply chain financing. The research advances past findings by employing an expanded UTAUT framework and validating observations with the other relevant studies throughout the world.
Benefits of alternative evaluation methods for Automated Essay Scoring

Automated essay scoring (AES), where natural language processing is applied to score written text, can underpin educational resources in blended and distance learning. AES performance has typically been reported in terms of correlation coeﬃcients or agreement statistics calculated between a system and an expert human examiner. We describe the beneﬁts of alternative methods to evaluate AES systems and, more importantly, facilitate comparison between AES systems and expert human examiners. We employ these methods, together with multi-marked test data labelled by 5 expert human examiners, to guide machine learning model development and selection, resulting in models that outperform expert human examiners. We extend on previous work on a mature feature-based linear ranking perceptron model and also develop a new multi-task learning neural network model built on top of a pre-trained language model – DistilBERT. Combining these two models’ scores results in further improvements in performance (compared to that of each single model).
Efficacy and safety of Andrographis paniculata extract in patients with mild COVID-19: A randomized controlled trial

Objective: To assess the efficacy and safety of Andrographis paniculata extract (APE) in adults with mild COVID-19. Methods: The alcoholic extract of the aerial part of A. paniculata was used. In this randomized, double-blinded, placebo-controlled trial, adults with laboratory-confirmed COVID-19, and mild symptoms were randomized 1:1 to receive APE (60 mg andrographolide, t.i.d, for 5 days) versus placebo, plus standard supportive care. Blood tests for CRP, liver and renal assessment were performed on Days 1, 3, and 5. The outcomes were occurrence of pneumonia detected by chest X-ray, nasopharyngeal SARS-CoV-2 detection by rRT-PCR, changes of CRP levels, and adverse drug reactions. Results: Patients were randomized to receive APE (n=29) or placebo (n=28). Pneumonia occurrence during illness was 0/29 (0%) versus 3/28 (10.7%), (p=0.039); and patients with nasopharyngeal SARS-CoV-2 detection on Day 5 were 10/29 (34.5%) versus 16/28 (57.1%), (p=0.086), for those who received APE and placebo, respectively. All three patients with pneumonia had substantially rising serum CRP; and high CRP levels on Day 5. None had evidence of liver or renal impairment. Conclusion: This AP-extract treatment regimen was potentially effective and safe in adults with mild COVID-19. The rising of CRP suggested disease progression. Further studies are needed.
Consequences of Ontario P index recommendations for reduced manure and fertilizer phosphorus applications on corn yields and soil phosphorus

Abstract. Environmental indices for soil P limit P applications when soil tests and risk of P losses exceed a given threshold. Producers' reluctance to reduce P inputs often stem from concerns regarding reduced crop production and soil fertility. Our objectives were to identify changes in soil P fractions after 4 yr of repeated manure or fertilizer P applications at rates ≤ crop removal by corn (Zea mays L.), and the impact of these applications on yields. Olsen P and soil P fractions extracted using a modified Hedley P fractionation procedure were measured. Corn yields were nonresponsive to P applications. After 4 yr, Olsen P was 16.6 and 24.6 mg kg-1 at the application rates of 0 and 33 kg P ha-1 yr-1, respectively, for the inorganic fertilizer treatment indicating that soil P drawdown was occurring. Only the most labile forms of Pi (resin and bicarbonate extractable) were affected by treatment, with greater values at higher P application rates. Adherence to Ontario's P index recommendations for P applications at or below crop removal should not be a crop production concern. Furthermore, given the rate of soil labile P drawdown, routine soil testing (every 3-5 yr) would identify agronomically significant changes in soil test P before the crop yield is impacted.
REX: Revisiting Budgeted Training with an Improved Schedule

Deep learning practitioners often operate on a computational and monetary budget. Thus, it is critical to design optimization algorithms that perform well under any budget. The linear learning rate schedule is considered the best budget-aware schedule, as it outperforms most other schedules in the low budget regime. On the other hand, learning rate schedules -- such as the \texttt{30-60-90} step schedule -- are known to achieve high performance when the model can be trained for many epochs. Yet, it is often not known a priori whether one's budget will be large or small; thus, the optimal choice of learning rate schedule is made on a case-by-case basis. In this paper, we frame the learning rate schedule selection problem as a combination of $i)$ selecting a profile (i.e., the continuous function that models the learning rate schedule), and $ii)$ choosing a sampling rate (i.e., how frequently the learning rate is updated/sampled from this profile). We propose a novel profile and sampling rate combination called the Reflected Exponential (REX) schedule, which we evaluate across seven different experimental settings with both SGD and Adam optimizers. REX outperforms the linear schedule in the low budget regime, while matching or exceeding the performance of several state-of-the-art learning rate schedules (linear, step, exponential, cosine, step decay on plateau, and OneCycle) in both high and low budget regimes. Furthermore, REX requires no added computation, storage, or hyperparameters.
A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification

The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers to identify Internet applications' traffic. Even with the aid of hardware accelerators (GPUs, TPUs), DL model training remains expensive, and limits the ability to operate frequent model updates necessary to fit to the ever evolving nature of Internet traffic, and mobile traffic in particular. To address this pain point, in this work we explore Incremental Learning (IL) techniques to add new classes to models without a full retraining, hence speeding up model's updates cycle. We consider iCarl, a state of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40 Android apps, aiming to understand"if there is a case for incremental learning in traffic classification". By dissecting iCarl internals, we discuss ways to improve its design, contributing a revised version, namely iCarl+. Despite our analysis reveals their infancy, IL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.
RGB Stream Is Enough for Temporal Action Detection

State-of-the-art temporal action detectors to date are based on two-stream input including RGB frames and optical flow. Although combining RGB frames and optical flow boosts performance significantly, optical flow is a hand-designed representation which not only requires heavy computation, but also makes it methodologically unsatisfactory that two-stream methods are often not learned end-to-end jointly with the flow. In this paper, we argue that optical flow is dispensable in high-accuracy temporal action detection and image level data augmentation (ILDA) is the key solution to avoid performance degradation when optical flow is removed. To evaluate the effectiveness of ILDA, we design a simple yet efficient one-stage temporal action detector based on single RGB stream named DaoTAD. Our results show that when trained with ILDA, DaoTAD has comparable accuracy with all existing state-of-the-art two-stream detectors while surpassing the inference speed of previous methods by a large margin and the inference speed is astounding 6668 fps on GeForce GTX 1080 Ti. Code is available at \url{https://github.com/Media-Smart/vedatad}.
Indirect Searches for Secluded Dark Matter

Dark matter is one of the most important open problems in particle physics and cosmology. Weakly interacting massive particles (WIMPs) appear as an appealing solution, providing the right relic density with a cross-section at the electroweak scale, however, no WIMP signals were observed until now. Secluded models are good alternatives to the standard ones. In this case, instead of a direct annihilation to the standard model (SM) particles, the dark matter annihilates into mediators which subsequently decay into SM particles. In this way, secluded models may avoid the stringent limits from direct searches, and, at the same time, be probed by indirect detection experiments. Motivated by the appearance of secluded dark matter in several model building endeavors, in this talk, we will present the sensitivity of several gamma-ray instruments (current and prospects), including Fermi-LAT, H.E. S.S. , CTA, and SWGO, to secluded dark matter annihilations in the inner galactic halo, and in the dwarf spheroidal galaxies, covering a wide range dark matter masses, from tens of GeV to hundreds of TeV.
Duality-symmetric axion electrodynamics and haloscopes of various geometries

Within the dual symmetric point of view, the theory for seeking axion dark matter via haloscope experiments is derived by exactly solving the dual symmetric axion electrodynamics equation. Notwithstanding that the conventional theory of axion electrodynamics presented in [9,11] is more commonly used in haloscope theory, we show that the dual symmetric axion electrodynamics has more advantages to apply into haloscope theory. First, the dual symmetric and conventional perspective of axion electrodynamics coincide under long-wavelength approximation. Moreover, dual symmetric theory can obtain an exact analytical expression of the axion-induced electromagnetic field for any states of axion. This solution has been used in conventional theory for long-wavelength approximation. The difference between two theories can occur in directional axion detection or electric sensing haloscopes. For illustrative purposes, we consider the various type of resonant cavities: cylindrical solenoid, spherical solenoid, two-parallel-sheet cavity, toroidal solenoid with a rectangular cross-section, and with a circular cross-section. The resonance of the axion-induced signal as well as the ratio of the energy difference over the stored energy inside the cavity are investigated in these types of cavity.
Well-posedness of an inverse problem for two- and three-dimensional convective Brinkman-Forchheimer equations with the final overdetermination

<p style='text-indent:20px;'>In this article, we study an inverse problem for the following convective Brinkman-Forchheimer (CBF) equations:</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{align*} \boldsymbol{u}_t-\mu \Delta\boldsymbol{u}+(\boldsymbol{u}\cdot\nabla)\boldsymbol{u}+\alpha\boldsymbol{u}+\beta|\boldsymbol{u}|^{r-1}\boldsymbol{u}+\nabla p = \boldsymbol{F}: = \boldsymbol{f} g, \ \ \ \nabla\cdot\boldsymbol{u} = 0, \end{align*} $\end{document} </tex-math></disp-formula></p><p style='text-indent:20px;'>in bounded domains <inline-formula><tex-math id="M1">\begin{document}$ \Omega\subset\mathbb{R}^d $\end{document}</tex-math></inline-formula> (<inline-formula><tex-math id="M2">\begin{document}$ d = 2, 3 $\end{document}</tex-math></inline-formula>) with smooth boundary, where <inline-formula><tex-math id="M3">\begin{document}$ \alpha, \beta, \mu>0 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M4">\begin{document}$ r\in[1, \infty) $\end{document}</tex-math></inline-formula>. The CBF equations describe the motion of incompressible fluid flows in a saturated porous medium. The inverse problem under our consideration consists of reconstructing the vector-valued velocity function <inline-formula><tex-math id="M5">\begin{document}$ \boldsymbol{u} $\end{document}</tex-math></inline-formula>, the pressure gradient <inline-formula><tex-math id="M6">\begin{document}$ \nabla p $\end{document}</tex-math></inline-formula> and the vector-valued function <inline-formula><tex-math id="M7">\begin{document}$ \boldsymbol{f} $\end{document}</tex-math></inline-formula>. We prove the well-posedness result (existence, uniqueness and stability) of an inverse problem for 2D and 3D CBF equations with the final overdetermination condition using Schauder's fixed point theorem for arbitrary smooth initial data. The well-posedness results hold for <inline-formula><tex-math id="M8">\begin{document}$ r\geq 1 $\end{document}</tex-math></inline-formula> in two dimensions and for <inline-formula><tex-math id="M9">\begin{document}$ r \geq 3 $\end{document}</tex-math></inline-formula> in three dimensions. The global solvability results available in the literature helped us to obtain the uniqueness and stability results for the model with fast growing nonlinearities. </p>
Private Graph Data Release: A Survey

The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph data, especially in light of the many privacy breaches in real-world graph data that were supposed to preserve sensitive information. This article provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms are natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that address some of the limitations of Differential Privacy. We also provide a wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, and health care. This article should benefit practitioners and researchers alike in the increasingly important area of private analytics and data release.
Learning to Delegate for Large-scale Vehicle Routing

Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions on small problem instances of up to 100 cities, their performance deteriorates in large problems. This article presents a novel learning-augmented local search framework to solve large-scale VRP. The method iteratively improves the solution by identifying appropriate subproblems and $\textit{delegating}$ their improvement to a black box subsolver. At each step, we leverage spatial locality to consider only a linear number of subproblems, rather than exponential. We frame subproblem selection as regression and train a Transformer on a generated training set of problem instances. Our method accelerates state-of-the-art VRP solvers by 10x to 100x while achieving competitive solution qualities for VRPs with sizes ranging from 500 to 3000. Learned subproblem selection offers a 1.5x to 2x speedup over heuristic or random selection. Our results generalize to a variety of VRP distributions, variants, and solvers.
Embedded particle size distribution and its effect on detonation in composite explosives

This paper discusses the effects of stochastically varying inert particle parameters on the long-term behaviour of detonation front propagation. The simulation model involves a series of cylindrical high explosive unit cells, each embedded with an inert spherical particle. Detonation shock dynamics theory postulates that the velocity of the shock front in the explosive fluid is related to its curvature. In our previous work, we derived a series of partial differential equations that govern the propagation of the shock front passing over the inert particles and developed a computationally efficient simulation environment to study the model over extremely long timescales. We expand upon that project by randomising several properties of the inert particles to represent experimental designs better. First, we randomise the particle diameters according to the Weibull distribution. Then we discuss stochastic particle spacing methods and their effects on the predictability of the shock wave speed. Finally, we discuss mixtures of plastic and metal particles and material inconsistency among the particles.
Higher Order Imprecise Probabilities and Statistical Testing

We generalize standard credal set models for imprecise probabilities to include higher order credal sets – confidences about confidences. In doing so, we specify how an agent’s higher order confidences (credal sets) update upon observing an event. Our model begins to address standard issues with imprecise probability models, like Dilation and Belief Inertia. We conjecture that when higher order credal sets contain all possible probability functions, then in the limiting case the highest order confidences converge to form a uniform distribution over the first order credal set, where we define uniformity in terms of the statistical distance metric (total variation distance). Finite simulation supports the conjecture. We further suggest that this convergence presents the total-variation-uniform distribution as a natural, privileged prior for statistical hypothesis testing.
Beyond Farthest Point Sampling in Point-Wise Analysis

Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling and downstream applications jointly. Our key insight is that uniform sampling methods like FPS are not always optimal for different tasks: sampling more points around boundary areas can make the point-wise classification easier for segmentation. Towards the end, we propose a novel sampler learning strategy that learns sampling point displacement supervised by task-related ground truth information and can be trained jointly with the underlying tasks. We further demonstrate our methods in various point-wise analysis architectures, including semantic part segmentation, point cloud completion, and keypoint detection. Our experiments show that jointly learning of the sampler and task brings remarkable improvement over previous baseline methods.
Can Deep Neural Networks Predict Data Correlations from Column Names?

Recent publications suggest using natural language analysis on database schema elements to guide tuning and profiling efforts. The underlying hypothesis is that state-of-the-art language processing methods, so-called language models, are able to extract information on data properties from schema text. This paper examines that hypothesis in the context of data correlation analysis: is it possible to find column pairs with correlated data by analyzing their names via language models? First, the paper introduces a novel benchmark for data correlation analysis, created by analyzing thousands of Kaggle data sets (and available for download). Second, it uses that data to study the ability of language models to predict correlation, based on column names. The analysis covers different language models, various correlation metrics, and a multitude of accuracy metrics. It pinpoints factors that contribute to successful predictions, such as the length of column names as well as the ratio of words. Finally, \rev{the study analyzes the impact of column types on prediction performance. } The results show that schema text can be a useful source of information and inform future research efforts, targeted at NLP-enhanced database tuning and data profiling.
The distribution and origin of C2H in NGC 253 from ALCHEMI

Context. Observations of chemical species can provide insights into the physical conditions of the emitting gas however it is important to understand how their abundances and excitation vary within different heating environments. C2H is a molecule typically found in PDR regions of our own Galaxy but there is evidence to suggest it also traces other regions undergoing energetic processing in extragalactic environments. Aims. As part of the ALCHEMI ALMA large program, we map the emission of C2H in the central molecular zone of the nearby starburst galaxy NGC 253 at 1.6′′ (28 pc) resolution and characterize it to understand its chemical origins. Methods. We used spectral modeling of the N = 1−0 through N = 4−3 rotational transitions of C2H to derive the C2H column densities towards the dense clouds in NGC 253. We then use chemical modeling, including photodissociation region (PDR), dense cloud, and shock models to investigate the chemical processes and physical conditions that are producing the molecular emission. Results. We find high C2H column densities of ∼1015 cm−2 detected towards the dense regions of NGC 253. We further find that these column densities cannot be reproduced if it is assumed that the emission arises from the PDR regions at the edge of the clouds. Instead, we find that the C2H abundance remains high even in the high visual extinction interior of these clouds and that this is most likely caused by a high cosmic-ray ionization rate.