Miracle-dz/UniFAR-FTUs huggingface dataset:
Response styles and avoidance strategies in bipolar disorder and healthy controls: A comparative study

Objective: This study aimed to investigate stress appraisals, rumination, worry, and experiential avoidance (EA) in patients with bipolar disorder-I (BD-I) in the euthymic phase of the illness. The effects of these processes on functionality in individuals with BD-I are also investigated. Method: Using the Stress Appraisal Measure Dispositional Form (SAM-D), Ruminative Thinking Style Questionnaire (RTSQ), Penn State Worry Questionnaire (PSWQ), and Acceptance and Action Questionnaire-II (AAQ-II), we compared 67 BD-I patients with 70 healthy controls. Additionally, multiple linear regression analysis was used to determine the predictors of functionality levels measured by the Bipolar Disorder Functioning Questionnaire (BDFQ). Results: BD-I group showed significantly higher SAM-D “threat,” “uncontrollable by anyone,” and “control by others” scores. The BD-I group also reported higher EA (AAQ-II) than the control group. Rumination (RTSQ) and worry (PSWQ) did not show any significant differences between groups. BDFQ was negatively predicted “uncontrollable by anyone” and positively predicted by “control by others” subdimensions of SAM-D. Conclusion: Our findings suggested that patients with BD-I have differences in stress appraisals, even in the euthymic phase. Higher AAQ-II scores seemed to be a distinctive feature for patients with BD-I that may be related to perceiving stressful situations as threatening and uncontrollable. Our results present new perspectives for psychotherapeutic interventions both for the treatment and for the improvement of the functioning of these patients.
Future changes in North Atlantic winter cyclones in CESM-LE – Part 1: Cyclone intensity, potential vorticity anomalies, and horizontal wind speed

Abstract. Strong low-level winds associated with extratropical cyclones can have substantial impacts on society. The wind intensity and the spatial distribution of wind maxima may change in a warming climate; however, the involved changes in cyclone structure and dynamics are not entirely clear. Here, such structural changes of strong North Atlantic cyclones in a warmer climate close to the end of the current century are investigated with storm-relative composites based on Community Earth System Model Large Ensemble (CESM-LE) simulations. Furthermore, a piecewise potential vorticity inversion is applied to associate such changes in low-level winds to changes in potential vorticity (PV) anomalies at different levels. Projected changes in cyclone intensity are generally rather small. However, using cyclone-relative composites, we identify an extended wind footprint southeast of the center of strong cyclones, where the wind speed tends to intensify in a warmer climate. Both an amplified low-level PV anomaly driven by enhanced diabatic heating and a dipole change in upper-level PV anomalies contribute to this wind intensification. On the contrary, wind changes associated with lower- and upper-level PV anomalies mostly compensate for each other upstream of the cyclone center. Wind changes at upper levels are dominated by changes in upper-level PV anomalies and the background flow. Altogether, our results indicate that a complex interaction of enhanced diabatic heating and altered non-linear upper-tropospheric wave dynamics shape future changes in near-surface winds in North Atlantic cyclones.
A New Approach to Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute

Sparsity is becoming arguably the most critical dimension to explore for efficiency and scalability, as deep learning models grow significantly larger and more complex. After all, the biological neural networks, where deep learning draws inspirations, are naturally sparse and highly efficient. We advocate a new approach to model sparsity via a new abstraction called Tensor-with-Sparsity-Attribute (or TeSA ) to augment the default Tensor abstraction, which is fundamentally designed for dense models. TeSA enables the sparsity attributes and patterns (e.g., for pruning and quantization) to be specified, propagated forward and backward across stages, and used to create highly efficient, specialized operators, taking into account any special sparsity support from the underlying hardware. The resulting SparGen framework is flexible in accommodating more than 10 popular sparsity schemes, is efficient in delivering more than 8x speedup compared to existing solutions like TVM and cuSPARSE, and is extensible to incorporate new innovations through new sparsity attributes, new propagation rules, new optimized sparse operators, or new sparsity-aware accelerators.
Rehabilitation Nurse’s Perspective on Transitional Care: An Online Focus Group

The increasing incidence of chronic and dependence leads to the need for hospitalization and adaptation in the process of returning home, as well as transition between care levels to ensure continuity of care. The World Health Organization has been warning about this problem since 2016, and consider reorganizing the care model as one of the solutions. The present study aimed to analyse the nurses’ perspective on transitional care for dependent people with rehabilitation care needs after hospital discharge. Methods: A focus group was developed with the participation of Rehabilitation Nurses from the hospital and community context, and content analysis was defined a posteriori. Results: From the content analysis emerged four related categories: promotion of continuity of care, nurse of advanced practice as a care manager, capacitation of the person and caregiver, and promotion of the care coordination. Conclusions: The present study allowed the strategies identification that minimize fragmentation risk of care and promote the person participation in transitional care. Ensuring transitional care is imperative to increase the quality of care, the satisfaction of professionals, clients, and the development of a system of sustainable health.
Nutraceutical Role of Polyphenols and Triterpenes Present in the Extracts of Fruits and Leaves of Olea europaea as Antioxidants, Anti-Infectives and Anticancer Agents on Healthy Growth

There is currently a worldwide consensus and recognition of the undoubted health benefits of the so-called Mediterranean diet, with its intake being associated with a lower risk of mortality. The most important characteristics of this type of diet are based on the consumption of significant amounts of fruit, vegetables, legumes, and nuts, which provide, in addition to some active ingredients, fiber and a proportion of vegetable protein, together with extra virgin olive oil (EVOO) as the main sources of vegetable fat. Fish and meat from poultry and other small farm animals are the main sources of protein. One of the main components, as already mentioned, is EVOO, which is rich in monounsaturated fatty acids and to a lesser extent in polyunsaturated fatty acids. The intake of this type of nutrient also provides an important set of phytochemicals whose health potential is widely spread and agreed upon. These phytochemicals include significant amounts of anthocyanins, stilbenes, flavonoids, phenolic acids, and terpenes of varying complexities. Therefore, the inclusion in the diet of this type of molecules, with a proven healthy effect, provides an unquestionable preventive and/or curative activity on an important group of pathologies related to cardiovascular, infectious, and cancerous diseases, as well as those related to the metabolic syndrome. The aim of this review is therefore to shed light on the nutraceutical role of two of the main phytochemicals present in Olea europaea fruit and leaf extracts, polyphenols, and triterpenes, on healthy animal growth. Their immunomodulatory, anti-infective, antioxidant, anti-aging, and anti-carcinogenic capabilities show them to be potential nutraceuticals, providing healthy growth.
DESIGN OF A 2 3 2 TAPERED DIELECTRIC-LOADED HELICAL ANTENNA ARRAY FOR INMARSAT-M SATELLITE SYSTEM COMPARISON OF INTERPOLATION METHODS FOR PILOT AIDED ESTIMATION IN DIRECT-DETECTION OPTICAL OFDM SYSTEM

: In this paper, design and fabrication of an L-band axial mode 2 3 2 tapered dielectric- loaded helical antenna array for international maritime satellite organization (INMARSAT)-M mobile vehicles are presented. This array has compact size and light weight. By adding a tapered cylindrical dielectric resonator, for satellite communications, important parameters such as gain, side lobe level, impedance matching and axial ratio bandwidth have been improved simultaneously. This array has a gain of 15.1 dB, a radiation pattern with a side lobe level of –21 and a –3 dB axial ratio bandwidth is nearly 15%. These characteristics are suitable for INMARSAT-M antennas. A good agreement between the simulated and measured results has been achieved. V C 2013 Wiley Periodicals, Inc. Lett 55:2600–2604, ABSTRACT: We experimentally compare four commonly used interpolation algorithms including linear interpolation, spline interpolation, low-pass interpolation and time domain interpolation for channel estimation based on pilot in baseband direct-detected optical orthogonal frequency division multiplexing system. The results show that the time domain DFT interpolation method exhibits the best BER performance and the most compact constellation. V C 2013 Wiley Periodicals, Inc. Microwave
Approximating Longest Spanning Tree with Neighborhoods

We study the following maximization problem in the Euclidean plane: Given a collection of neighborhoods (polygonal regions) in the plane, the goal is to select a point in each neighborhood so that the longest spanning tree on selected points has maximum length. It is not known whether or not this problem is NP-hard. We present an approximation algorithm with ratio 0 . 548 for this problem. This improves the previous best known ratio of 0 . 511. The presented algorithm takes linear time after computing a diameter. Even though our algorithm itself is fairly simple, its analysis is rather involved. In some part we deal with a minimization problem with multiple variables. We use a sequence of geometric transformations to reduce the number of variables and simplify the analysis.
Double Descent in Random Feature Models: Precise Asymptotic Analysis for General Convex Regularization

We prove rigorous results on the double descent phenomenon in random features (RF) model by employing the powerful Convex Gaussian Min-Max Theorem (CGMT) in a novel multi-level manner. Using this technique, we provide precise asymptotic expressions for the generalization of RF regression under a broad class of convex regularization terms including arbitrary separable functions. We further compute our results for the combination of (cid:96) 1 and (cid:96) 2 regularization case, known as elastic net, and present numerical studies about it. We numerically demonstrate the predictive capacity of our framework, and show experimentally that the predicted test error is accurate even in the non-asymptotic regime.
Improving Zero-Shot Event Extraction via Sentence Simplification

The success of sites such as ACLED and Our World in Data have demonstrated the massive utility of extracting events in structured formats from large volumes of textual data in the formof news, social media, blogs and discussion forums.Event extraction can provide a window into ongoing geopolitical crises and yield actionable intelligence. In this work, we cast socio-political event extraction as a machine reading comprehension (MRC) task. % With the proliferation of large pretrained language models Machine Reading Comprehension (MRC) has emerged as a new paradigm for event extraction in recent times. In this approach, extraction of social-political actors and targets from a sentence is framed as an extractive question-answering problem conditioned on an event type. There are several advantages of using MRC for this task including the ability to leverage large pretrained multilingual language models and their ability to perform zero-shot extraction. Moreover, we find that the problem of long-range dependencies, i.e., large lexical distance between trigger and argument words and the difficulty of processing syntactically complex sentences plague MRC-based approaches.To address this, we present a general approach to improve the performance of MRC-based event extraction by performing unsupervised sentence simplification guided by the MRC model itself. We evaluate our approach on the ICEWS geopolitical event extraction dataset, with specific attention to ‘Actor’ and ‘Target’ argument roles. We show how such context simplification can improve the performance of MRC-based event extraction by more than 5{% for actor extraction and more than 10{% for target extraction.
Mean-Field Sparse Optimal Control of Systems with Additive White Noise

We analyze the problem of controlling a multi-agent system with additive white noise through parsimonious interventions on a selected subset of the agents (leaders). For such a controlled system with a SDE constraint, we introduce a rigorous limit process towards an infinite dimensional optimal control problem constrained by the coupling of a system of ODE for the leaders with a McKean-Vlasov-type SDE, governing the dynamics of the prototypical follower. The latter is, under some assumptions on the distribution of the initial data, equivalent with a (nonlinear parabolic) PDE-ODE system. The derivation of the limit mean-field optimal control problem is achieved by linking the mean-field limit of the governing equations together with the $\Gamma$-limit of the cost functionals for the finite dimensional problems.
Variability of the Sun’s Luminosity Places Constraints on the Thermal Equilibrium of the Convection Zone

Luminosity, which is the total amount of radiant energy emitted by an object, is one of the most critical quantities in astrophysics for characterizing stars. Equally important is the temporal evolution of a star’s luminosity because of its intimate connection with the stellar energy budget, large-scale convective motion, and heat storage in the stellar interior. The Sun’s luminosity and its variation have not been measured to date because current observations of the solar radiative output have been restricted to vantage points near the Earth. Here, we model the solar luminosity by extending a semiempirical total solar irradiance (TSI) model that uses solar-surface magnetism to reconstruct solar irradiance over the entire 4π solid angle around the Sun. This model was constrained by comparing its output to the irradiance in the Earth’s direction with the measured TSI. Comparing the solar luminosity to the TSI on timescales from days to solar cycles for cycles 23 and 24, we find poor agreement on short timescales (<solar rotation). This is not unexpected due to the Earth-centric viewing geometry and short-term irradiance dependence on surface features on the Earth-facing solar disk. On longer timescales, however, we find good agreement between the luminosity model and the TSI, which suggests that the extrapolation of luminosities to multicycle timescales based on TSI reconstructions may be possible. We show that the solar luminosity is not constant but varies in phase with the solar cycle. This variation has an amplitude of 0.14% from minimum to maximum for Solar Cycle 23. Considering the energetics in the solar convection zone, it is therefore obvious that a steady-state input from the radiative zone at the solar minimum level would lead to a gradual reduction in the energy content in the convection zone over multicentury timescales. We show that the luminosity at the base of the convection zone should be approximately 0.032% higher than that at the solar surface during solar minimum to maintain net energy equilibrium through the solar cycle. These different energy-input scenarios place constraints on the long-term evolution of the TSI and its impact on the solar forcing of climate variability. These results highlight the convection zone’s role as an energy reservoir on solar-cycle timescales and set constraints for dynamo models intending to understand the long-term evolution of the Sun and solar analogs.
Nearly Tight Spectral Sparsification of Directed Hypergraphs by a Simple Iterative Sampling Algorithm

Spectral hypergraph sparsification, an attempt to extend well-known spectral graph sparsification to hypergraphs, has been extensively studied over the past few years. For undirected hypergraphs, Kapralov, Krauthgamer, Tardos, and Yoshida~(2022) have proved an $\varepsilon$-spectral sparsifier of the optimal $O^*(n)$ size, where $n$ is the number of vertices and $O^*$ suppresses the $\varepsilon^{-1}$ and $\log n$ factors. For directed hypergraphs, however, the optimal sparsifier size has not been known. Our main contribution is the first algorithm that constructs an $O^*(n^2)$-size $\varepsilon$-spectral sparsifier for a weighted directed hypergraph. Our result is optimal up to the $\varepsilon^{-1}$ and $\log n$ factors since there is a lower bound of $\Omega(n^2)$ even for directed graphs. We also show the first non-trivial lower bound of $\Omega(n^2/\varepsilon)$ for general directed hypergraphs. The basic idea of our algorithm is borrowed from the spanner-based sparsification for ordinary graphs by Koutis and Xu~(2016). Their iterative sampling approach is indeed useful for designing sparsification algorithms in various circumstances. To demonstrate this, we also present a similar iterative sampling algorithm for undirected hypergraphs that attains one of the best size bounds, enjoys parallel implementation, and can be transformed to be fault-tolerant.
Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages

The Yunshan Cup 2020 track focused on creating a framework for evaluating different methods of part-of-speech (POS). There were two tasks for this track: (1) POS tagging for the Indonesian language, and (2) POS tagging for the Lao tagging. The Indonesian dataset is comprised of 10000 sentences from Indonesian news within 29 tags. And the Lao dataset consists of 8000 sentences within 27 tags. 25 teams registered for the task. The methods of participants ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. The best performing results achieve an accuracy of 95.82% for Indonesian and 93.03%, showing that neural sequence labeling models significantly outperform classic feature-based methods and rule-based methods.
Design Collaboration in Industry: When, Why, and How

II DEDICATION . III ACKNOWLEDGEMENTS . IV TABLE OF CONTENTS . V LIST OF TABLES . VIII LIST OF FIGURES . X CHAPTER 1: INDUSTRY COLLABORATION RESEARCH MOTIVATION. 1 1.1 Textbook Review of Design Processes . 1 1.2 Tools of the Design Process . 5 1.3 Collaborative Design . 11 1.4 Research Questions . 15 1.5 Thesis Outline . 16 CHAPTER 2: CASE STUDY RESEARCH THROUGH INTERVIEWING . 18 2.1 Case Studies in Engineering Research . 18 2.2 Interviewing as a Data Collection Method . 19 2.3 Interview Planning . 21 2.4 Interview Question Structure . 23 CHAPTER 3: INTERVIEW DESIGN . 26 3.1 Company Profiles . 26 3.2 Interviewee Profiles . 28 3.3 Interview Questions, Topics, and Triangulation . 32
A simulated annealing algorithm for solving multi-depot location routing problem with backhaul

This paper presents a method for solving a multi-depot location-routing problem with backhaul (MDLRPB). In the suggested problem, there are two subsets of customers including linehaul and backhaul customers. Linehaul customers require a delivery of stock from the depot, whereas backhaul customers need to deliver stock from their location to the depot. A mathematical model and a heuristic method based on the simulated annealing (SA) algorithm have been proposed for solving the problem. Test problems from the literature have been used for generating a new test problem for the MDLRPB problem. Computational results show that the CPU time for the SA is reasonable while this value for the LINGO increase dramatically in large size problems. In the worst case, the gap between objective function value of SA algorithm and optimal solution is equal 3%. The results indicate that suggested algorithm can reach the optimal or near optimal solution in small and medium size problems in a reasonable computational time.
Inferring Rewards from Language in Context

In classic instruction following, language like “I’d like the JetBlue flight” maps to actions (e.g., selecting that flight). However, language also conveys information about a user’s underlying reward function (e.g., a general preference for JetBlue), which can allow a model to carry out desirable actions in new contexts. We present a model that infers rewards from language pragmatically: reasoning about how speakers choose utterances not only to elicit desired actions, but also to reveal information about their preferences. On a new interactive flight–booking task with natural language, our model more accurately infers rewards and predicts optimal actions in unseen environments, in comparison to past work that first maps language to actions (instruction following) and then maps actions to rewards (inverse reinforcement learning).
The peripheral vortex biome of confined quantum fluids and its influence on vortex dipole annihilation

The self-annihilation of a pair of oppositely charged optical vortices (vortex dipole) in a quantum fluid is hindered by nonlinearity and promoted by radial confinement, resulting in rich life-cycle dynamics of such pairs. The competing effects generate a biome of peripheral vortices that can directly interact with the original pair to produce a sequence of surrogation events. Numerical simulation is used to elucidate the role of the vortex biome as a function of nonlinearity strength and the initial spacing between the engineered vortices. The results apply directly to other nonlinear quantum fluids as well and may be useful in the control of complex condensates in which vortex dynamics produce topologically protected phases.
Black-box min-max continuous optimization using CMA-ES with worst-case ranking approximation

In this study, we investigate the problem of min-max continuous optimization in a black-box setting minx maxy f (x,y). A popular approach updates x and y simultaneously or alternatingly. However, two major limitations have been reported in existing approaches. (I) As the influence of the interaction term between x and y (e.g., xTBy) on the Lipschitz smooth and strongly convex-concave function f increases, the approaches converge to an optimal solution at a slower rate. (II) The approaches fail to converge if f is not Lipschitz smooth and strongly convex-concave around the optimal solution. To address these difficulties, we propose minimizing the worst-case objective function F(x) = maxy f (x, y) directly using the covariance matrix adaptation evolution strategy, in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation (WRA) mechanism. Compared with existing approaches, numerical experiments show two important findings regarding our proposed method. (1) The proposed approach is eficient in terms of f-calls on a Lipschitz smooth and strongly convex-concave function with a large interaction term. (2) The proposed approach can converge on functions that are not Lipschitz smooth and strongly convex-concave around the optimal solution, whereas existing approaches fail.