Miracle-dz/UniFAR-FTUs huggingface dataset:
Contingency Management for Patients With Dual Disorders in Intensive Outpatient Treatment for Addiction

Objective: This quality improvement program evaluation investigated the effectiveness of contingency management for improving retention in treatment and positive outcomes among patients with dual disorders in intensive outpatient treatment for addiction. Methods: The effect of contingency management was explored among a group of 160 patients exposed to contingency management (n = 88) and not exposed to contingency management (no contingency management, n = 72) in a six-week partial hospitalization program. Patients referred to the partial hospitalization program for treatment of substance use and comorbid psychiatric disorders received diagnoses from psychiatrists and specialist clinicians according to the Diagnostic and Statistical Manual of the American Psychiatric Association. A unique application of the contingency management “fishbowl” method was used to improve the consistency of attendance at treatment sessions, which patients attended 5 days a week. Days attending treatment and drug-free days were the main outcome variables. Other outcomes of interest were depression, anxiety and psychological stress, coping ability, and intensity of drug cravings. Results: Patients in the contingency management group attended more treatment days compared to patients in the no contingency management group; M = 16.2 days (SD = 10.0) versus M = 9.9 days (SD = 8.5), respectively; t = 4.2, df = 158, p <. 001. No difference was found between the treatment groups on number of drug-free days. Psychological stress and drug craving were inversely associated with drug-free days in bivariate testing (r = −.18, p <.02; r = −.31, p <.001, respectively). Treatment days attended and drug craving were associated with drug-free days in multivariate testing (B =.05, SE =.01, β =.39, t = 4.9, p <.001; B = −.47; SE =.12, β = −.30, t = −3.9, p <.001, respectively; Adj. R2 =.21). Days attending treatment partially mediated the relationship between exposure to contingency management and self-reported drug-free days. Conclusions: Contingency management is a valuable adjunct for increasing retention in treatment among patients with dual disorders in partial hospitalization treatment. Exposure to contingency management increases retention in treatment, which in turn contributes to increased drug-free days. Interventions for coping with psychological stress and drug cravings should be emphasized in intensive dual diagnosis group therapy.
Learned D-AMP: A Principled CNN-based Compressive Image Recovery Algorithm

Sparse signal recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results. By exploiting massively parallel GPU processing architectures and oodles of training data, they are able to run orders of magnitude faster than existing methods. Unfortunately, these methods are difﬁcult to train, often-times speciﬁc to a single measurement matrix, and largely unprincipled blackboxes. It was recently demonstrated that iterative sparse-signal-recovery algorithms can be “unrolled” to form interpretable deep neural networks. Taking inspiration from this work, we develop novel neural network architectures that mimic the behavior of the denoising-based approximate message passing (D-AMP) and denoising-based vector approximate message passing (D-VAMP) algorithms. We call these new networks Learned D-AMP (LDAMP) and Learned D-VAMP (LDVAMP). The LDAMP/LDVAMP networks are easy to train, can be applied to a variety of different measurement matrices, and come with a state-evolution heuristic that accurately predicts their performance. Most importantly, our networks outperforms the state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and runtime. At high resolutions, and when used with matrices which have fast matrix multiply implementations, LDAMP runs over 50 × faster than BM3D-AMP and hundreds of times faster than NLR-CS.
An Improved Anisotropic Diffusion PDE for Noise Removal and Edge Preservation

In this paper, an improved anisotropic nonlinear diffusion equation for image restoration is presented. As a corner in an image is rounded when using degenerated diffusion equation to smooth the image, it proposed to use both the gradient and the second order directional derivative in the direction orthogonal to the gradient to control the diffusion velocity. It can remove noise and perform stably. It can also enhance step-like edges and keep the locality of them. Compared with other method, it is capable of keeping stronger peaks and thin edges. Due to these characteristics, not only the processed noisy image look much more clear and smooth, but also the details are kept, resulting in the naturalness. The experiment results are very satisfactory.
An inexact inverse iteration for large sparse eigenvalue problems

In this paper, we propose an inverse inexact iteration method for the computation of the eigenvalue with the smallest modulus and its associated eigenvector for a large sparse matrix. The linear systems of the traditional inverse iteration are solved with accuracy that depends on the eigenvalue with the second smallest modulus and iteration numbers. We prove that this approach preserves the linear convergence of inverse iteration. We also propose two practical formulas for the accuracy bound which are used in actual implementation. © 1997 John Wiley & Sons, Ltd.
#PrayForDad: Learning the Semantics Behind Why Social Media Users Disclose Health Information

User-generated content in social media is increasingly acknowledged as a rich resource for research into health problems. One particular area of interest is in the semantics individuals evoke because they can influence when health-related information is disclosed. While there have been multiple investigations into why self-disclose occurs, much less is known about when individuals choose to disclose information about other people (e.g., a relative), which is a significant privacy concern. In this paper, we introduce a novel framework to investigate how semantics influence disclosure routines for 34 health issues. This framework begins with a supervised classification model to distinguish tweets that communicate personal health issues from confounding concepts (e.g., metaphorical statements that include a health-related keyword). Next, we annotate tweets for each health issue with linguistic and psychological categories (e.g. social processes, affective processes and personal concerns). Then, we apply a non-negative matrix factorization over a health issue-by-language category space. Finally, the factorized basis space is leveraged to group health issues into natural aggregations based around how they are discussed. We evaluate this framework with four months of tweets (over 200 million) and show that certain semantics correspond with whom a health mention pertains to. Our findings show that health issues related with family members, high medical cost and social support (e.g., Alzheimer's Disease, cancer, and Down syndrome) lead to tweets that are more likely to disclose another individual's health status, while tweets with more benign health issues (e.g., allergy, arthritis, and bronchitis) with biological processes (e.g., health and ingestion) and negative emotions are more likely to contain self-disclosures.
Versatile and efficient techniques for simulating cloth and other deformable objects

We are presenting techniques for simulating the motion and the deformation of cloth, fabrics or, more generally, deformable surfaces. Our main goal is to be able to simulate any kind of surface without imposing restrictions on shape or geometrical environment. In particular, we are considering difficult situations with respect to deformations and collisions, like wrinkled fabric falling on the ground. Thus, we have enhanced existing algorithms in order to cope with any possible situation. A mechanical model has been implemented to deal with any irregular triangular meshes, handle high deformations despite rough discretisation, and cope with complex interacting collisions. Thus, it should deal efficiently with situations where nonlinearities and discontinuities are really non marginal. Collision detection has also been improved to efficiently detect self-collisions, and also to correctly consider collision orientations despite the lack of surface orientation information from preset geometrical contexts, using consistency checking and correction. We illustrate these features through simulation examples.
Low-power adiabatic SRAM

This paper presents a new adiabatic static random access memory (SRAM). The proposed adiabatic SRAM uses two trapezoidal-wave pulses and resembles behavior of static CMOS 4T-SRAM. The elementary cell structure of proposed SRAM consists of two high load resistors which is constructed of PMOS, a cross-coupled NMOS pair and NMOS switch which is necessary to restrict short circuit current. From the simulation results, we show that the energy consumption of the proposed circuit is lower than that of conventional SRAM.
Decomposition of leaf and fine root residues of three different crop species in tropical peat under controlled condition.

A study examining the decomposition rates of leaf and fine root residues of oil palm (Elaeis guineensis), sago (Metroxylon sagu) and pineapple (Ananas comosus) crops in peatland soils was conducted under controlled conditions. The fourteen- month study showed that sago leaf residue was the most resistant to decomposition with only 30 % mass loss, while pineapple leaf residue was found to be the easiest to decompose, accounting for 90 % of the mass loss. In contrast, the highest (70 %) mass loss of fine roots was observed in sago, while the lowest (50 %) was in pineapple. Nutrient concentration in plant tissues correlated significantly with mass losses of leaf and fine root residues. The high C:N in plant tissues, resulted in slow decomposition of sago leaves and pineapple fine roots. Decomposition of the different plants were in the order: pineapple > oil palm > sago drained = sago undrained for leaf residues, and, sago drained = sago undrained > oil palm > pineapple for fine root residues.
Social Support, Caregiver Burden, and Life Satisfaction in a Sample of Rural African American and White Caregivers of Older Persons With Dementia

This article reports the findings of a study of African American and White dementia caregivers (n = 141) living in rural areas of Alabama that examined the relations between the participants' receipt of informal social support, and their levels of caregiver burden and life satisfaction. The sample, as a whole, reported high levels of social support with no reported differences in social support by race. Female caregivers reported higher mean scores on 3 of the 4 dimensions of social support than their male counterparts. Two of the 4 dimensions of social support accounted for 32% of the variance of the caregivers' reported level of life satisfaction.
Applied Optimal Control Lecture 9 : Numerical Methods for Deterministic Optimal Control October 28 , 2019

Lecturer: Marin Kobilarov Consider the general setting where we are minimizing J = φ(x(t f), t f) + t f t 0 L(x(t), u(t), t)dt, (1) subject to: x(t 0) = x 0 , x(t f) and t f free (2) ˙ x(t) = f (x(t), u(t), t) (3) c(x(t), u(t), t) ≤ 0, for all t ∈ [t 0 , t f ] (4) ψ(x(t f), t f) ≤ 0, (5) We will consider three families of numerical methods for solving the optimal control problem: • dynamic programming: i.e. solution to the HJB equations • indirect methods-based on calculus of variations, and Pontryagin's principle • direct methods-based on a finite-dimensional representation, e.g. using a discrete-time formulation or using a parametrized control signal The distinction between indirect and direct methods lies in whether one first derives continuous optimality conditions and then solves them numerically (indirect) as opposed to first discretiz-ing/parametrizing the problem and then solving it numerically (direct). Solving the HJB is equivalent to finding a value function V (x, t) defined over the whole domain X × [t 0 , t f ] where x ∈ X and t ∈ [t 0 , t f ]. The control law is then found through the optimization: u * (x(t), t) = min u H(x(t), u, ∂ x V (x(t), t), t) and constitutes a global solution to the optimal control problem from any starting pair (x(t), t). In general, it is extremely difficult to compute V for any nonlinear system. Various approximation methods are applicable. 1.1 Discrete space-time methods As we already discussed one possible way to solve the HJB equations is to discretize both space and time, convert the problem into a path-finding problem on a graph. Then we can apply the value function expansion methods (value iteration) starting from the goal set backwards. This works for low-order problems with simple dynamics.
Open GADTs and declaration-site variance: a problem statement

Generalized algebraic data types (GADTs) allow embedding extensible typed ASTs and transformations on them. Such transformations on typed ASTs are useful for code optimization in deeply embedded DSLs, for instance when using Lightweight Modular Staging (LMS). However, in Scala it is hard to make transformations for typed ASTs type-safe. Therefore, AST transformations in LMS are often not fully typechecked, preventing bugs from being caught early and without extensive testing. We show that writing type-safe transformations in such embeddings is in fact not just hard, but impossible without using unsafe casts or significantly restricting extensibility: Declaration-site variance opens GADTs representing typed ASTs not only to desirable extensions, but also to extensions that introduce exotic terms. We make the problem concrete on an embedding of λ<: through covariant GADTs. We discuss solution approaches, and sketch a Scala extension to address this problem without either introducing unsafe casts or restricting extensibility. We believe a complete solution would significantly ease writing transformations by allowing type-checking to verify them, and thus would ease their development.
Preference-based Teaching

We introduce a new model of teaching named "preference-based teaching" and a corresponding complexity parameter--the preference-based teaching dimension (PBTD)--representing the worstcase number of examples needed to teach any concept in a given concept class. Although the PBTD coincides with the well-known recursive teaching dimension (RTD) on finite classes, it is radically different on infinite ones: the RTD becomes infinite already for trivial infinite classes (such as half-intervals) whereas the PBTD evaluates to reasonably small values for a wide collection of infinite classes including classes consisting of so-called closed sets w.r. t. a given closure operator, including various classes related to linear sets over N0 (whose RTD had been studied quite recently) and including the class of Euclidean half-spaces. On top of presenting these concrete results, we provide the reader with a theoretical framework (of a combinatorial flavor) which helps to derive bounds on the PBTD.
Neuroergonomics: a review of applications to physical and cognitive work

Neuroergonomics is an emerging science that is defined as the study of the human brain in relation to performance at work and in everyday settings. This paper provides a critical review of the neuroergonomic approach to evaluating physical and cognitive work, particularly in mobile settings. Neuroergonomics research employing mobile and immobile brain imaging techniques are discussed in the following areas of physical and cognitive work: (1) physical work parameters; (2) physical fatigue; (3) vigilance and mental fatigue; (4) training and neuroadaptive systems; and (5) assessment of concurrent physical and cognitive work. Finally, the integration of brain and body measurements in investigating workload and fatigue, in the context of mobile brain/body imaging (“MoBI”), is discussed.
Language recognition with support vector machines

Support vector machines (SVMs) have become a popular tool for discriminative classification. Powerful theoretical and computational tools for support vector machines have enabled significant improvements in pattern classification in several areas. An exciting area of recent application of support vector machines is in speech processing. A key aspect of applying SVMs to speech is to provide a SVM kernel which compares sequences of feature vectors–a sequence kernel. We propose the use of sequence kernels for language recognition. We apply our methods to the NIST 2003 language evaluation task. Results demonstrate the potential of the new SVM methods.
MR volume segmentation of gray matter and white matter using manual thresholding: dependence on image brightness.

PURPOSE To describe a quantitative MR imaging segmentation method for determination of the volume of cerebrospinal fluid, gray matter, and white matter in living human brain, and to determine the method's reliability. METHODS We developed a computer method that allows rapid, user-friendly determination of cerebrospinal fluid, gray matter, and white matter volumes in a reliable manner, both globally and regionally. This method was applied to a large control population (N = 57). RESULTS Initially, image brightness had a strong correlation with the gray-white ratio (r = .78). Bright images tended to overestimate, dim images to underestimate gray matter volumes. This artifact was corrected for by offsetting each image to an approximately equal brightness. After brightness correction, gray-white ratio was correlated with age (r = -.35). The age-dependent gray-white ratio was similar to that for the same age range in a prior neuropathology report. Interrater reliability was high (.93 intraclass correlation coefficient). CONCLUSIONS The method described here for gray matter, white matter, and cerebrospinal fluid volume calculation is reliable and valid. A correction method for an artifact related to image brightness was developed.
Design of a Transmission With Gear Trains for Underactuated Mechanisms

This paper proposes a design method of transmissions for underactuated mechanisms. This transmission consists of active and passive gear trains that align a series of gears over several joints. The transmission has no kinematic singular point that restricts the workspace. Thus, arbitrary joint-connected motions of the underactuated mechanisms can be designed by combining the gear trains. We investigate the kinematics and statics of underactuated mechanisms to find a transmission matrix that realizes the desired connected motions and adaptive grasps. We also develop a method to design gear trains that corresponds to the transmission matrix. We extend this design method with stepped gears and belt-gear mechanisms to increase the design flexibility. The proposed method was experimentally validated using the two- and three-jointed mechanisms.
Detection of Positive Gross Primary Production Extremes in Terrestrial Ecosystems of China During 1982–2015 and Analysis of Climate Contribution

Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems. High‐temperature extremes and associated droughts significantly reduce terrestrial ecosystem carbon uptake, but there is uncertainty as to whether climatic extremes can be beneficial to ecosystem carbon uptake. In this study, we used three ecological models: the Boreal Ecosystem Productivity Simulator, the Terrestrial Ecosystem Carbon flux model, and the Global Production Efficiency Model coupled with the Carbon Exchange between Vegetation, Soil, and the Atmosphere model, to simulate China's terrestrial ecosystem GPP during the study period of 1982–2015. Positive GPP extremes were identified on yearly scales and analyzed for their temperature, precipitation, and solar radiation attributions. We found that the 3 years of positive GPP extremes occurred in 1990, 1998, and 2013, with the detrended GPP anomalies of 0.4 PgC/year, 0.2 PgC/year, and 0.3 PgC/year, respectively. Maximum GPP years were associated with an increased carbon uptake in response to increasing temperature and solar radiation under adequate precipitation for plant growth. China's subtropical‐tropical monsoonal region, a region dominated by managed forests and agricultural lands, contributed the largest in GPP extremes and accounted for 46%, 50%, and 46% of the total detrended GPP anomalies in 1990, 1998, and 2013, respectively. Positive GPP extremes were associated with increasing temperature and solar radiation, indicating favorable positive climate extremes would be beneficial to carbon uptake in China's terrestrial ecosystem. This study provides a method to assess positive GPP extremes in response to global climate change.
Contribution of olive seed to the phenolic profile and related quality parameters of virgin olive oil.

BACKGROUND Conflicting results have been reported about the effect of fruit de-stoning on the virgin olive oil (VOO) phenolic profile. The aim of the present study was to determine whether olive seed plays any role in the synthesis of this oil phenolic fraction. RESULTS Increases of around 25% of total phenolic compounds were observed in oils obtained from de-stoned olive fruits in three main Spanish cultivars. To investigate the involvement of olive seed in determining the phenolic profile of VOO, whole intact olive fruits were added with up to 400% olive stones. Excellent regression coefficients were found in general for the decrease of total phenolic compounds and, particularly, of o-diphenolics in the resulting oils. On the other hand, it was found that olive seed contains a high level of peroxidase (POX) activity (72.4 U g(-1) FW), accounting for more than 98% of total POX activity in the whole fruit. This activity is able to modify VOO phenolics in vitro, similar to the effect of adding stones during VOO extraction. CONCLUSION Olive seed plays an important role in determining VOO phenolic profile during the process to obtain an oil that seems to be associated with a high level of POX activity. Copyright © 2007 Society of Chemical Industry.
Data Hiding by LSB Substitution using Gene Expression Programming

Steganography is defined as the art and science of writing hidden in such a way that no one apart from the sender and the intended recipient even knows that there is a hidden message. One of the most important techniques of Steganography is the Least Significant Bit (LSB) which embeds the secret message in the host image. It is based on replacing the LSBs of the host-image with the secret message bits giving a stego-image. The proposed scheme consists mainly of two phases: In the first phase, we propose a hybrid data hiding scheme incorporates LSB technique with a key permutation method. While in the second phase, we proposed a new scheme for finding the optimal key-permutation by using gene expression programming (GEP). Where, GEP is a powerful evolutionary algorithm for data analysis and combines the advantages of both genetic algorithms (GA) and genetic programming (GP).