Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty relations in terms of the Gini index for finite quantum systems

Lorenz values and the Gini index are popular quantities in Mathematical Economics, and are used here in the context of quantum systems with finite-dimensional Hilbert space. They quantify the uncertainty in the probability distribution related to an orthonormal basis. It is shown that Lorenz values are superadditive functions and the Gini indices are subadditive functions. The supremum over all density matrices of the sum of the two Gini indices with respect to position and momentum states is used to define an uncertainty coefficient which quantifies the uncertainty in the quantum system. It is shown that the uncertainty coefficient is positive, and an upper bound for it is given. Various examples demonstrate these ideas.
Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis

Generating images via a generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called auto-embedding generative adversarial network, which simultaneously encodes the global structure features and captures the fine-grained details. In our network, we use an autoencoder to learn the intrinsic high-level structure of real images and design a novel denoiser network to provide photo-realistic details for the generated images. In the experiments, we are able to produce $512 \times 512$ images of promising quality directly from the input noise. The resultant images exhibit better perceptual photo-realism, that is, with sharper structure and richer details, than other baselines on several datasets, including Oxford-102 Flowers, Caltech-UCSD Birds (CUB), High-Quality Large-scale CelebFaces Attributes (CelebA-HQ), Large-scale Scene Understanding (LSUN), and ImageNet.
Efficacy and Safety of Bimatoprost 0.01% for the Treatment of Eyebrow Hypotrichosis: A Randomized, Double-Blind, Vehicle-Controlled Study.

BACKGROUND Eyebrow hypotrichosis is an important dermatological problem. However, there is no standard treatment. OBJECTIVE To study the efficacy and safety of bimatoprost 0.01% for the treatment of eyebrow hypotrichosis. MATERIALS AND METHODS Although bimatoprost 0.03% has been studied previously, this is the first study to evaluate the efficacy and safety of bimatoprost 0.01% for the treatment of eyebrow hypotrichosis. A randomized, double-blinded, vehicle-controlled trial was conducted in 40 patients. All patients were randomized to receive bimatoprost 0.01% or placebo vehicle, once daily, for 6 months. The primary outcome was improvement in eyebrow density and diameter. Additional outcomes were the improvement in clinical assessments and safety evaluation. RESULTS Compared to the vehicle group, bimatoprost 0.01% significantly increased mean eyebrow hair density, eyebrow hair diameter, and clinical assessments (p < .001) in the drug group. Patients' satisfaction score was higher for the drug group than the vehicle group (p < .05). Adverse effects of the treatment were minimal and similar between the 2 groups. CONCLUSION Bimatoprost 0.01% was found to be superior to a placebo for eyebrow enhancement. Bimatoprost 0.01% can be considered effective, safe, and well-tolerated for the treatment of eyebrow hypotrichosis.
A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance

Statistical process control (SPC) has natural applications in data network surveillance. However, network data are commonly autocorrelated, which presents challenges to the basic SPC methods. Most existing SPC methods for correlated data assume parametric models to account for the correlation structure within the data. Those model assumptions can be difficult to justify in practice. In this paper, we propose a nonparametric cumulative sum (CUSUM) control chart for autocorrelated processes. In our proposed approach, we incorporate a wavelet decomposition and a nonparametric multivariate CUSUM control chart to obtain a robust procedure for autocorrelated processes without distribution assumptions. Extensive simulations show that the procedure appropriately controls the in‐control average run length and also has good sensitivity for detecting location shifts.
Increased chronic stress predicts greater emotional negativity bias and poorer social skills but not cognitive functioning in healthy adults

ABSTRACT Background and Objectives: Chronically stressed individuals report deficits spanning cognitive and emotional functioning. However, limitations to clinical populations and measures of stress have impeded the generalisability and scope of results. This study investigated whether chronic stress predicted cognitive and emotional functioning, and whether these relationships differed between males and females, in a large representative sample of healthy participants. Design: Cross-sectional study. Method: 1883 healthy adults sampled from the Brain Resource International Database reported stress using the 21-item Depression Anxiety Stress Scales. Participants then completed a cognitive and emotional assessment battery (IntegNeuro), as well as questionnaires related to sleep, emotional functioning, and self-regulation. Results: In contrast to previously reported results, chronic stress did not predict cognitive functioning. However, higher stress predicted a greater negativity bias and poorer social skills, confirming previous research identifying these links. Conclusions: Cognitive deficits related to stress are absent in healthy participants when stress is measured using the 21-items Depression Anxiety Stress Scales. Identifying how chronic stress is associated with aspects of emotional functioning can lead to personalized interventions for individuals to better manage the negative outcomes resulting from stress.
Shouji: a fast and efficient pre-alignment filter for sequence alignment

MOTIVATION The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly-parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern FPGA (Field-Programmable Gate Array) architectures to further boost the performance of our algorithm. RESULTS Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8x. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step. AVAILABILITY https://github.com/CMU-SAFARI/Shouji. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Network analyses of student engagement with on-line textbook problems.

Problem solving in physics and mathematics have been characterized in terms of five phases by Schonfeld and these have previously been used to describe also online and blended behavior. We argue that expanding the use of server logs to make detailed categorizations of student actions can help increase knowledge about how students solve problems. We present a novel approach for analyzing server logs that relies on network analysis and principal component analysis. We use the approach to analyze student interactions with an online textbook that features physics problems. We find five 'components of behavioral structure': Complexity, Linear Length, Navigation, Mutuality, and Erraticism. Further, we find that problem solving sessions can be divided into three over-arching groups that differ in their Complexity and further into ten clusters that also differ on the other components. Analyzing typical sessions in each cluster, we find ten different behavioral structures,which we describe in terms of Schonfeld's phases. We suggest that further research integrates this approach with other methodological approaches to get a fuller picture of how learning strategies are employed by students in settings with online features.
A double machine learning approach to estimate the effects of musical practice on student’s skills

This study investigates the dose–response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning parameter choices in the machine learning part? (ii) How to assess covariate balancing in high‐dimensional settings? The results show that improvements in objectively measured cognitive skills require at least medium intensity, while improvements in school grades are already observed for low intensity of practice.
Development, characterization, and application of a 2‐Compartment system to investigate the impact of pH inhomogeneities in large‐scale CHO‐based processes

Large‐scale bioreactors for the production of monoclonal antibodies reach volumes of up to 25 000 L. With increasing bioreactor size, mixing is however affected negatively, resulting in the formation of gradients throughout the reactor. These gradients can adversely affect process performance at large scale. Since mammalian cells are sensitive to changes in pH, this study investigated the effects of pH gradients on process performance. A 2‐Compartment System was established for this purpose to expose only a fraction of the cell population to pH excursions and thereby mimicking a large‐scale bioreactor. Cells were exposed to repeated pH amplitudes of 0.4 units (pH 7.3), which resulted in decreased viable cell counts, as well as the inhibition of the lactate metabolic shift. These effects were furthermore accompanied by increased absolute lactate levels. Continuous assessment of molecular attributes of the expressed target protein revealed that subunit assembly or N‐glycosylation patterns were only slightly influenced by the pH excursions. The exposure of more cells to the same pH amplitudes further impaired process performance, indicating this is an important factor, which influences the impact of pH inhomogeneity. This knowledge can aid in the design of pH control strategies to minimize the effects of pH inhomogeneity in large‐scale bioreactors.
Characterization of G-CSF receptor expression in medulloblastoma

Abstract Background Identifying mechanisms of medulloblastoma recurrence is a key to improving patient survival, and targeting treatment-resistant subpopulations within tumors could reduce disease recurrence. Expression of the granulocyte colony-stimulating factor receptor (G-CSF-R, CD114) is a potential marker of cancer stem cells, and therefore we hypothesized that a subpopulation of medulloblastoma cells would also express CD114 and would demonstrate chemoresistance and responsiveness to G-CSF. Methods Prevalence of CD114-positive (CD114+) cells in medulloblastoma cell lines, patient-derived xenograft (PDX) tumors, and primary patient tumor samples were assessed by flow cytometry. Growth rates, chemoresistance, and responses to G-CSF of CD114+ and CD114-negative (CD114−) cells were characterized in vitro using continuous live cell imaging and flow cytometry. Gene expression profiles were compared between CD114+ and CD114− medulloblastoma cells using quantitative RT-PCR. Results CD114+ cells were identifiable in medulloblastoma cell lines, PDX tumors, and primary patient tumors and have slower growth rates than CD114− or mixed populations. G-CSF accelerates the growth of CD114+ cells, and CD114+ cells are more chemoresistant. The CD114+ population is enriched when G-CSF treatment follows chemotherapy. The CD114+ population also has higher expression of the CSF3R, NRP-1, TWIST1, and MYCN genes. Conclusions Our data demonstrate that a subpopulation of CD114+ medulloblastoma cells exists in cell lines and tumors, which may evade traditional chemotherapy and respond to exogenous G-CSF. These properties invite further investigation into the role of G-CSF in medulloblastoma therapy and methods to specifically target these cells.
A pr 2 02 0 Causality : a Decision-Theoretic Foundation ∗

We propose a decision-theoretic model akin to that of Savage [17] that is useful for defining causal effects. Within this framework, we define what it means for a decision maker (DM) to act as if the relation between two variables is causal. Next, we provide axioms on preferences that are equivalent to the existence of a (unique) directed acyclic graph (DAG) that represents the DM’s preferences. The notion of representation has two components: the graph factorizes the conditional independence properties of the DM’s subjective beliefs, and arrows point from cause to effect. Finally, we explore the connection between our representation and models used in the statistical causality literature (for example, that of Pearl [14]).
Privacy and the Panopticon: Online mass surveillance’s deterrence and chilling effects

The Panopticon is a popular metaphor in discussions about mass surveillance. Drawing on deterrence theory and chilling effects, we provide two empirical tests of this analogy to examine whether perceptions of online government surveillance suppress or entirely eradicate an array of sensitive online activities. Study 1 indicates that surveillance significantly deters individuals’ intentions to engage in illegal offenses, an effect that extends to political, but not privacy-protective behaviors. Study 2 retests the pervasiveness of this effect with a sample of Muslims who reside in the United States. Results indicate that restrictive chilling effects are not specific to any one online population, experimental stimuli, or political context. Implications for US political and social systems are discussed.
Hacia una generación de resúmenes sin sesgo a partir de contenido generado por el usuario: Un enfoque preliminar

espanolEn este trabajo se propone un enfoque novedoso de generacion automatica de resumenes capaz de sintetizar grandes cantidades de informacion generada por diferentes tipos de usuarios en Internet y producir un nuevo texto coherente que presente la informacion de forma objetiva, es decir, evitando proporcionar informacion parcial o sesgada, a la par que aportando multiples perspectivas sobre el tema en cuestion. En concreto, el escenario en el que se enmarca esta investigacion es el ambito turistico, centrandonos en las opiniones sobre distintos aspectos de varios hoteles proporcionadas por 5 tipos de perfil de usuario. La evaluacion realizada con usuarios demuestra que i) la calidad de los resumenes generados es adecuada y ii) que este tipo de resumenes ayudaria a los usuarios a tomar mejores decisiones. EnglishIn this paper a novel approach for automatic summarization is proposed. This approach is able to synthetize huge amounts of information generated by different types of users on the Internet and produce a new coherent text that presents the information in an objective way, i.e., avoiding biased information and giving multiple perspectives for an specific aspect/topic. This study is mainly focused on the tourism sector, especially on the opinions abour different topics existing in multiple hotels and given by 5 user types. The user evaluation conducted proves that i) the generated summaries have an appropriate quality, and ii) they would really help users to make better decisions.
Increasing sero-discordancy among young HIV infected pregnant women from India: a likely pointer of changing transmission dynamics

ABSTRACT The flat-lining of HIV incidence in India has raised concerns about the presence of emerging risk groups. As HIV prevalence among pregnant women is reflective of the situation in general population, its closer scrutiny provides valuable insights about the evolving epidemic. The present study assesses temporal trends of sero-discordance (where woman is HIV infected and husband is uninfected), among pregnant women living with HIV (pWLHIV) from India. Data of program for prevention of parent to child transmission of HIV was analyzed. Statistical analysis was done using Cochrane-Armitage trend test and logistic regression. Of the 1209 currently married pWLHIV, 302 (25%) were sero-discordant. The proportion increased from 16% in 2007 to 36% in 2016–17 (p = 0.000). The likelihood of sero-discordance was higher for women aged 18–20 (OR: 2.68, CI: 1.30–5.83) and 21–23 (OR: 1.98, CI: 1.01–4.15) years compared to 36–40 years; and for primi-parous women (OR: 1.84, CI: 1.31–2.58) compared to women pregnant for second/third time. The findings are indicative of changing HIV transmission dynamics. Steeper rise in sero-discordance in younger women implies an increasing risk of HIV in unmarried women population. A better understanding of HIV specific vulnerabilities of young women, married and unmarried, is warranted.
High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study.

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < . 05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70). Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article. © RSNA, 2020.
Psychiatric Symptoms and Diagnoses Among U.S. College Students: A Comparison by Race and Ethnicity.

OBJECTIVE The mental health challenges of college students are a critical public health concern, and they may be exacerbated among racial and ethnic minority groups. Unfortunately, there is a lack of recent large-scale research on this topic. This study provides an update on the mental health experiences of U.S. college students from minority backgrounds. METHODS This is a retrospective analysis of cross-sectional data from the spring 2015 administration of the American College Health Association-National College Health Assessment (ACHA-NCHA IIB). Survey results from 67,308 undergraduates at 108 colleges were analyzed. RESULTS Past-year rates of self-reported psychiatric symptoms and diagnoses were high, regardless of race or ethnicity. Students from minority groups generally reported lower rates of both symptoms and diagnoses compared with whites, with notable exceptions. Despite reporting generally lower rates of psychiatric diagnoses compared with whites, students who identified as multiracial (N=7,473) or Asian/Pacific Islander (N=7,166) were more likely to endorse having felt hopeless, so depressed that it was difficult to function, or overwhelmed by anger and were more likely to have considered or attempted suicide. Compared with whites, blacks and Hispanics endorsed lower rates of psychiatric diagnoses but had similar rates of past-year suicide attempts. CONCLUSIONS Lower rates of formally diagnosed psychiatric illnesses may obscure significant mental health burden among minority students, especially with regard to suicidal thoughts and attempts among Asian/Pacific Islander and multiracial students. Students from racial and ethnic minority backgrounds may have undetected psychiatric problems and, therefore, represent a particularly at-risk group on campus.
From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In ”real” economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods’ circulation can be tested and confirmed before their introduction to ”real life” and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.