Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of guidance on intervention adherence in computerised interventions for mental health problems: a meta-analysis

Abstract Web-based interventions are increasingly used for the prevention, treatment and aftercare of mental disorders. A crucial factor to the efficacy of such online programmes is adherence to the intervention content and procedure. It has been frequently suggested that adherence in web-based interventions is low and little is known about which factors influence adherence. To increase intervention uptake and completion, studies increasingly include interventions with some form of guidance. Guided interventions have been shown to have higher efficacy, however, evidence for the impact on adherence is limited and mixed. This meta-analysis explored the impact of human guidance on intervention completion in web-based mental health interventions. A total of 22 studies were included with interventions primarily targeting symptoms of depression and anxiety disorders. Results showed that guidance significantly increases the average amount of intervention completion [g = 0.29, 95% confidence interval (CI) 0.18–0.40] and the proportion of intervention completers [log odds ratio (OR) = 0.50, 95% CI 0.34–0.66] with small effects. On average, full completion rates were 12% higher in guided intervention groups. This meta-analysis demonstrated that guidance in web-based mental health interventions does increase adherence, but more research is required to better understand the specific mechanisms between guidance, adherence and outcomes.
Ground Temperature Variation around the Horizontal Collectors of a Heat Pump

The manuscript presents results of a 3 years experiment concerning the monitoring of temperature variation around a horizontal collector, coupled to a ground – water heat pump. Ground temperature was monitored in 7 (seven) measurement points: 1 (one) in the center of the collector area and 6 (six) at 15 cm, 30 cm and 60 cm of the collector boundary. The data processing methodology, described in the manuscript, allowed corrections, comparison with natural ground temperature variation and calculation of important characteristics of temperature variation in each measurement point. The main obtained results are: minimum, maximum and average temperatures, dates when minimum, maximum and average temperatures were reached, amplitude of temperatures variations and deviations comparing to natural ground temperature variation. The study of temperature field allowed the complete characterization of the ground temperature variation, including spatial and temporal variation. Some practical conclusions of the study are also presented in the manuscript. It was highlighted that due to a correct calculation of the collectors field size and to the presence of snow in each heating season of the 3 (three) years of the experiment, the ground temperature did not decreased below 2°C and the ground temperature recovered uniformly after each non heating season.
Florence: A New Foundation Model for Computer Vision

Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition. Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects. All of these properties are critical for our vision foundation model to serve general purpose vision tasks. Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.
Gradient-based estimation of linear Hawkes processes with general kernels

Linear multivariate Hawkes processes (MHP) are a fundamental class of point processes with selfexcitation. When estimating parameters for these processes, a difficulty is that the two main error functionals, the log-likelihood and the least squares error (LSE), as well as the evaluation of their gradients, have a quadratic complexity in the number of observed events. In practice, this prohibits the use of exact gradient-based algorithms for parameter estimation. We construct an adaptive stratified sampling estimator of the gradient of the LSE. This results in a fast parametric estimation method for MHP with general kernels, applicable to large datasets, which compares favourably with existing methods.
Using multiple modes to reconstruct conductor locations in a cylindrical model of a power transformer winding

In the present thesis, we propose and investigate a new approach to diagnose the effects of the various degradation mechanisms, including thermal degradation at hot spots, winding deformations due to the mechanical forces from short circuit currents, partial discharges due to local electric field surges, and increased moisture levels in the cellulose insulation due to decomposition, that affect electric power transformers during their normal operation in an electric power grid.Although the proposed diagnostics method can in principle be used to detect various degradation mechanisms mentioned above, we focus in the present thesis on mechanical deformations of transformer winding structures. Such mechanical deformations are most often caused by mechanical forces from short circuit currents, but they may also be caused by initial manufacturing errors and inconsistencies not detected by the power transformers’ suppliers quality assurance processes.We model a transformer winding surrounded by the transformer-tank wall and the magnetic core as a two-dimensional parallel plate waveguide or as a three-dimensional coaxial waveguide, where one metallic boundary (plate or cylinder) represents the wall of the transformer tank and the other metallic boundary (plate or cylinder) represents the iron core that conducts the magnetic flux. In between there is a set of parallel or coaxial conductors representing the winding segments.The new principle proposed in the present thesis is to insert a number of antennas into a transformer tank to radiate and measure microwave fields interacting with metallic structures and insulation. The responses from the emitted microwave radiation are expected to be sensitive to material properties that reflect the changes caused by any harmful deterioration processes mentioned above. Specifically, we investigate the mechanical deformations of transformer winding structures by determining the locations of the individual winding segments or turns, using measurements of the scattered fields at both ends of the winding structure. We solve the propagation problem using conventional waveguide theory, including mode-matching and cascading techniques.The inverse problem is solved using modified steepest-descent optimization methods. The optimization model is tested by comparing our calculated scattering data with synthetic measurement data generated by the commercial program HFSS.A good agreement is obtained between the calculated and measured positions of winding segments for a number of studied cases, which indicates that the diagnostics method proposed in the present thesis couldbe potentially useful as a basis for the design of a future commercial on-line winding monitoring device. However, further development of the theoretical analysis of a number of typical winding deformations, improvements of the optimization algorithms and a practical study with measurements on an actual power transformer structure are all needed to make an attempt to design a commercial winding monitoring device feasible.
Sandpile groups of supersingular isogeny graphs

Let $p$ and $q$ be distinct primes, and let $X_{p,q}$ be the $(q+1)$-regular graph whose nodes are supersingular elliptic curves over $\overline{\mathbb{F}}_p$ and whose edges are $q$-isogenies. For fixed $p$, we compute the distribution of the $\ell$-Sylow subgroup of the sandpile group (i.e.\ Jacobian) of $X_{p,q}$ as $q \to \infty$. We find that the distribution disagrees with the Cohen-Lenstra heuristic in this context. Our proof is via Galois representations attached to modular curves. As a corollary of our result, we give an upper bound on the probability that the Jacobian is cyclic, which we conjecture to be sharp.
Faecal haemoglobin concentrations in women and men diagnosed with colorectal cancer in a national screening programme

Objective There is evidence that colorectal cancer screening using faecal haemoglobin is less effective in women than men. The faecal haemoglobin concentrations were therefore examined in women and men with screen-detected colorectal cancer. Setting Scottish Bowel Screening Programme, following the introduction of a faecal immunochemical test from November 2017, to March 2020. Methods Data were collated on faecal haemoglobin concentrations, pathological stage and anatomical site of the main lesion in participants who had colorectal cancer detected. The data in women and men were compared. Results For the faecal haemoglobin concentrations studied (>80 µg Hb/g faeces), the distributions indicated lower concentrations in women. Marked differences were found between women and men diagnosed with colorectal cancer. The median faecal haemoglobin concentration for women (n = 720) was 408 µg Hb/g faeces compared to 473 µg Hb/g faeces for men (n = 959) (p = 0.004) and 50.6% of the results were >400 µg Hb/g faeces in women; in men, this was 57.8%. The difference in faecal haemoglobin concentrations in women and men became less statistically significant as stage advanced from stages I–IV. For right-sided, left-sided and rectal colorectal cancer, a similar gender difference persisted in all sites. Differences in faecal haemoglobin between the genders were significant for left-sided cancers and stage I and approached significance for rectal cancers and stage II, but all sites and stages showed lower median faecal haemoglobin concentrations for women. Conclusions To minimise gender inequalities, faecal immunochemical test-based colorectal cancer screening programmes should evaluate a strategy of using different faecal haemoglobin concentration thresholds in women and men.
A comparative study of high power IGBT model behaviour in voltage source inverter

The paper presents a voltage source inverter using IGBT modules capable of supplying 500 kW to a high speed (30000 rpm) induction motor. It compares the behaviour of two IGBT macromodels of the inverter structure. One is based on the Hefner model and the other is a linear model derived by combining simple experimental measurements with designer supplied data. Simulation and experimental results for a single inverter leg are presented in the case of an inductive load.
Scaling Law for Recommendation Models: Towards General-purpose User Representations

Recent advancement of large-scale pretrained models such as BERT, GPT-3, CLIP, and Gopher, has shown astonishing achievements across various task domains. Unlike vision recognition and language models, studies on general-purpose user representation at scale still remain underexplored. Here we explore the possibility of general-purpose user representation learning by training a universal user encoder at large scales. We demonstrate that the scaling law is present in user representation learning areas, where the training error scales as a power-law with the amount of computation. Our Contrastive Learning User Encoder (CLUE), optimizes task-agnostic objectives, and the resulting user embeddings stretch our expectation of what is possible to do in various downstream tasks. CLUE also shows great transferability to other domains and companies, as performances on an online experiment shows significant improvements in Click-Through-Rate (CTR). Furthermore, we also investigate how the model performance is influenced by the scale factors, such as training data size, model capacity, sequence length, and batch size. Finally, we discuss the broader impacts of CLUE in general.
High‐speed directional relaying using adaptive neuro‐fuzzy inference system and fundamental component of currents

This paper proposes an adaptive neuro‐fuzzy approach for fault direction estimation in sectional transmission lines. The ANFIS (adaptive neuro‐fuzzy inference system) network is designed by selecting different input and output member functions and rules for training and testing of fault cases. The fundamental component of current obtained from three‐phase current employing discrete Fourier transform (DFT) is given as input to the ANFIS module. The trained ANFIS module is then tested for detecting the fault direction. The relay is located at middle section‐2, which is considered as the primary section to be protected. It takes section‐1 as reverse section and section‐3 as forward section. This method is not affected by the variation of fault type, fault inception angle, fault location, and fault resistance. The biggest advantage of the ANFIS method is that it can detect the fault direction within 1 ms in almost all cases, which is much less than the implemented distance relaying scheme. The second advantage of the method is that it takes less number of training samples to detect the direction accurately as compared to other training algorithms like ANN, SVM, etc. The third advantage of the proposed scheme is that it offers protection to 99% of line length in all the three sections. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Common Fundamentals of Psoriasis and Depression

Psoriasis is an inflammatory, immune-mediated disease that is frequently associated with psychological comorbidities such as depression. The stigma patients feel because of the appearance of their skin may contribute to the high psycho-social burden of psoriasis. However, there is emerging evidence that overlapping biological mechanisms are, to a substantial degree, responsible for the close interaction between psoriasis and depression. Increased proinflammatory mediators, such as C-reactive protein or interleukin-6, are present in both psoriasis and depression, indicating that inflammation may represent a pathophysiological link between the diseases. Anti-inflammatory biologic therapies treat the clinical manifestations of psoriasis, but might also play a significant role in reducing associated depressive symptoms in patients with psoriasis. Comparison between single studies focusing on the change in depressive symptoms in psoriasis is limited by inconsistency in the depression screening tools applied.
Effect of polyethylene bunch cover on fungal diseases control of banana (Musa acuminata L.) in Iran

Banana (Musa acuminata L.), is one of the most important fruit crops in South-East of Iran. In this study, the effects of bunch cover were studied as experiment in form of Randomized complete block design with 2 treatments and 4 replications (20 bunches with cover and 20 bunches without cover) on banana trees (M. acuminata L.) C.V. "Dwarf Cavendish" in Konarac region of Sistan & Bluchestan province, during 2009-2011. The results showed that the above-mentioned factors have significant effects on fungal diseases disorder damage, yield at 1% level probability. Finally, using polyethylene bag covering is recommended treatments for decreasing of Pitting, Tip end rot, Cigar end rot, Brown spot, Diamond spot diseases damage and improving the quality characters of fruits.
The Generalized Cascade Click Model: A Unified Framework for Estimating Click Models

Given the vital importance of search engines to find digital information, there has been much scientific attention on how users interact with search engines, and how such behavior can be modeled. Many models on user - search engine interaction, which in the literature are known as click models, come in the form of Dynamic Bayesian Networks. Although many authors have used the resemblance between the different click models to derive estimation procedures for these models, in particular in the form of expectation maximization (EM), still this commonly requires considerable work, in particular when it comes to deriving the E-step. What we propose in this paper, is that this derivation is commonly unnecessary: many existing click models can in fact, under certain assumptions, be optimized as they were Input-Output Hidden Markov Models (IO-HMMs), for which the forward-backward equations immediately provide this E-step. To arrive at that conclusion, we will present the Generalized Cascade Model (GCM) and show how this model can be estimated using the IO-HMM EM framework, and provide two examples of how existing click models can be mapped to GCM. Our GCM approach to estimating click models has also been implemented in the gecasmo Python package.
AN OPERAD OF 3-MANIFOLDS

A coloured operad whose operadic operations are 3-cobor-disms is introduced in this paper. This operad is presented purely com-binatorially through diagrams in handlebodies together with a calculus, which is a generalization of Kirby’s calculus. Both integral and rational calculi are developed and a calculus consisting of ﬁnite set of local moves is presented in the paper.
Computation of the self-diffusion coefficient with low-rank tensor methods: application to the simulation of a cross-diffusion system

Cross-diffusion systems arise as hydrodynamic limits of lattice multi-species interacting particle models. The objective of this work is to provide a numerical scheme for the simulation of the cross-diffusion system identified in [J. Quastel, Comm. Pure Appl. Math., 45 (1992), pp. 623–679]. To simulate this system, it is necessary to provide an approximation of the so-called self-diffusion coefficient matrix of the tagged particle process. Classical algorithms for the computation of this matrix are based on the estimation of the long-time limit of the average mean square displacement of the particle. In this work, as an alternative, we propose a novel approach for computing the self-diffusion coefficient using deterministic low-rank approximation techniques, as the minimum of a high-dimensional optimization problem. The computed self-diffusion coefficient is then used for the simulation of the cross-diffusion system using an implicit finite volume scheme.
Improved Model Based Deep Learning Using Monotone Operator Learning (Mol)

Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery. In this work, we introduce a novel monotone operator learning framework to overcome some of the challenges associated with current unrolled frameworks, including high memory cost, lack of guarantees on robustness to perturbations, and low interpretability. Unlike current unrolled architectures that use finite number of iterations, we use the deep equilibrium (DEQ) framework to iterate the algorithm to convergence and to evaluate the gradient of the convolutional neural network blocks using Jacobian iterations. This approach significantly reduces the memory demand, facilitating the extension of MoDL algorithms to high dimensional problems. We constrain the CNN to be a monotone operator, which allows us to introduce algorithms with guaranteed convergence properties and robustness guarantees. We demonstrate the utility of the proposed scheme in the context of parallel MRI.
Automorphisms of B-free Toeplitz systems

Let B be an infinite subset of t2, 3, . u. We present sufficient conditions for the triviality of the automorphism group of minimalB-free subshifts and give a broad class of examples satisfying them, extending [9]. On the other hand we provide an example of a minimalB-free subshift whose automorphism group has elements of arbitrarily large finite order, answering Question 11 in [12].
Low energy effective theory for axion strings

We consider aspects of the low energy (classical) effective field theory description of global cosmic strings. In the non-relativistic limit, we study the extent to which these are described by the combination of the Nambu-Gotto and Kalb-Ramond actions. While in a formal sense this is the case for infinitely long strings, for more realistic situations, the collective coordinates of the system do not provide a complete description for more than brief intervals; as time evolves it becomes necessary to include low momentum goldstone excitations as well.
The Impact of Main Content Extraction on Near-Duplicate Detection

Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages. We revisit the risks and potential of near-duplicates with an information retrieval focus, motivating that current efforts toward an open and independent European web search infrastructure should maintain metadata on duplicate and near-duplicate documents in its index. Near-duplicate detection implemented in an open web search infrastructure should provide a suitable similarity threshold, a difficult choice since identical pages may substantially differ in parts of a page that are irrelevant to searchers (templates, advertisements, etc.). We study this problem by comparing the similarity of pages for five (main) content extraction methods in two studies on the ClueWeb crawls. We find that the full content of pages serves precision-oriented near-duplicate-detection, while main content extraction is more recall-oriented.