Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing Concurrent Validity for a Brief PTSD Screen Among Women in a Domestic Violence Shelter

There is a particular need for valid scales to screen for posttraumatic stress disorder (PTSD) among women who seek safe shelter from intimate partner violence. Screening to identify women who are at risk for PTSD can lead to early intervention that reduces the risk for PTSD-related outcomes such as poor decision making, inconsistent parenting, and behavior dysfunction among their children. The gold standard for diagnosing PTSD is the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) (CAPS-5). A seven-item PTSD screen has been used for in this population and has a well-established cutoff score but has not been validated against the DSM-5 diagnostic criteria for PTSD. The study purpose was to establish concurrent validity for a seven-item screen for PTSD with the CAPS-5. Participants were 75 women, 18 years or older, who were residents of a 120-bed shelter in the southern United States. They spoke English or Spanish. They reported intimate partner physical or sexual violence within 4 months of their entry into the study. Following informed consent, data were collected in individual interviews, conducted in either English or Spanish. In addition to demographic data, the seven-item PTSD screen and the CAPS-5 were administered. A receiver operating characteristic (ROC) curve analysis was conducted to assess the concurrent validity of the seven-item PTSD screen with the CAPS-5. The seven-item PTSD screen results were significantly correlated with the CAPS-5 results in this sample (area under the curve [AUC] = .640, z = 2.670, p = .008). Sensitivity was 96.2, and observed specificity was 31.8. The seven-item PTSD screen demonstrates excellent sensitivity (e.g., 96% of true PTSD cases) and acceptable specificity (32% of non-PTSD cases) and can be used to quickly and accurately identify individuals for diagnostic assessment and intervention.
Approaching Questions of Text Reuse in Ancient Greek Using Computational Syntactic Stylometry

Abstract We are investigating methods by which data from dependency syntax treebanks of ancient Greek can be applied to questions of authorship in ancient Greek historiography. From the Ancient Greek Dependency Treebank were constructed syntax words (sWords) by tracing the shortest path from each leaf node to the root for each sentence tree. This paper presents the results of a preliminary test of the usefulness of the sWord as a stylometric discriminator. The sWord data was subjected to clustering analysis. The resultant groupings were in accord with traditional classifications. The use of sWords also allows a more fine-grained heuristic exploration of difficult questions of text reuse. A comparison of relative frequencies of sWords in the directly transmitted Polybius book 1 and the excerpted books 9–10 indicate that the measurements of the two texts are generally very close, but when frequencies do vary, the differences are surprisingly large. These differences reveal that a certain syntactic simplification is a salient characteristic of Polybius’ excerptor, who leaves conspicuous syntactic indicators of his modifications.
MODEL THEORY OF VALUED FIELDS

Here is a short overview of the notes. We start with a section on the model theory of ordered abelian groups (Section 2). After a short preliminary section on the model theory of valued fields (Section 3), we then present some fundamental results about the theory ACVF of algebraically closed non-trivially valued fields in Section 4. At the end of the section, we point to some more recent and advanced topics in the study of ACVF. Section 5 is devoted to Kaplansky theory. The results of this section play a key role in our treatment of the Ax-Kochen-Ershov principle on henselian valued fields of equal-characteristic 0 in Section 6. Our approach to the AKE principle is via Pas’ Theorem on the elimination of field quantifiers for henselian valued fields of equalcharacteristic 0 in the three-sorted Denef-Pas language with angular components. We finish the notes with a glimpse on p-adic model theory. The text is organised so that similarities between the various contexts are highlighted. The algebraic results on valued fields presented in Franziska Jahnke’s notes will be used throughout the text. Some of the material we present here (e.g., Pas’ Theorem) will play a crucial role in Immanuel Halupczok’s introductory notes on motivic integration.
The Anchoring Effect in Decision-Making with Visual Analytics

Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users’ interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias.Index Terms: K.6. 1 [Management of Computing and Information Systems]: Project and People Management-Life Cycle, K.7. m [The Computing Profession]: Miscellaneous-Ethics
Fault detection & classification using innovative MFCDFT based slope tracking method for digital distance protection

The critical requirements for numerical distance protection used for transmission line protection are reliable and fast phasor estimation of input signals to determine correct impedance reach. Factors influencing accurate phasor estimation are Fault Resistance (RF), Fault Type (FT), Fault Inception Angle (FIA), Fault Location (FL) and Power Flow Angle (PFA). In this paper, an innovative slope tracking method is presented which is based on Modified Full Cycle Discrete Fourier Transform (MFCDFT) and its application has been validated for distance relaying. Fault signals are simulated in PSCAD for all possible types of faults and validation is performed in MATLAB®. For transmission line protection, impedance trajectory is adaptively calculated from Ground Distance Function (GDF) using estimated values of voltages and currents. Moreover, the proposed technique calculates operating time, fault location and fault type, which are the key attributes of multi functional numerical relay. The obtained results are compared with existing methods and found precise and faster. Error in fault location estimation is achieved within 1.23% with fault classification accuracy of 100%.
Seasonal dynamics alter taxonomical and functional microbial profiles in Pampa biome soils under natural grasslands

Soil microbial communities’ assembly is strongly tied to changes in temperature and moisture. Although microbial functional redundancy seems to overcome taxonomical composition changes, the sensitivity and resilience of soil microbial communities from subtropical regions in response to seasonal variations are still poorly understood. Thus, the development of new strategies for biodiversity conservation and sustainable management require a complete understanding of the soil abiotic process involved in the selection of microbial taxa and functions. In this work, we used state of the art molecular methodologies (Next Generation Sequencing) to compare the taxonomic (metataxonomics) and functional (metatranscriptomics) profiles among soil samples from two subtropical natural grasslands located in the Pampa biome, Brazil, in response to short-term seasonal variations. Our data suggest that grasslands maintained a stable microbial community membership along the year with oscillation in abundance. Apparently soil microbial taxa are more susceptible to natural climatic disturbances while functions are more stable and change with less intensity along the year. Finally, our data allow us to conclude that the most abundant microbial groups and functions were shared between seasons and locations reflecting the existence of a stable taxonomical and functional core microbiota.
Chinese Adolescents' Sibling Conflicts: Links With Maternal Involvement in Sibling Relationships and Coparenting.

This study explored how maternal involvement in sibling relationships and coparenting behaviors were associated with adolescents' sibling conflicts. Adolescents (Mage = 12.25 years; 47.8% boys) and their mothers from 542 families in China participated in this research. Mothers completed questionnaires that assessed their strategies of involvement in sibling relationships, as well as their perceptions of the quality of their coparenting behaviors. Furthermore, adolescents completed questionnaires that assessed sibling conflicts. Results revealed that the mother's positive guidance was negatively related and their authoritarian control was positively related to sibling conflict. A significant interaction was also found between positive maternal guidance in sibling relationships and undermining coparenting behaviors. These findings underscore the unique and interactive effects of mothers' direct involvement in sibling relationships and coparenting behaviors in adolescents' sibling conflicts.
Learning Latent Factors for Community Identification and Summarization

Network communities, which are also known as network clusters, are typical latent structures in network data. Vertices in each of these communities tend to interact more and share similar features with each other. Community identification and feature summarization are significant tasks of network analytics. To perform either of the two tasks, there have been several approaches proposed, taking into the consideration of different categories of information carried by the network, e.g., edge structure, node attributes, or both aforementioned. But few of them are able to discover communities and summarize their features simultaneously. To address this challenge, we propose a novel latent factor model for community identification and summarization (LFCIS). To perform the task, the LFCIS first formulates an objective function that evaluating the overall clustering quality taking into the consideration of both edge topology and node features in the network. In the objective function, the LFCIS also adopts an effective component that ensures those vertices sharing with both similar local structures and features to be located into the same clusters. To identify the optimal cluster membership for each vertex, a convergent algorithm for updating the variables in the objective function is derived and used by LFCIS. The LFCIS has been tested with six sets of network data, including synthetic and real networks, and compared with several state-of-the-art approaches. The experimental results show that the LFCIS outperforms most of the prevalent approaches to community discovery in social networks, and the LFCIS is able to identify the latent features that may characterize those discovered communities.
Prevalence and outcome of post-transfusion hepatitis C acquired at different ages and detected in look-back screening

Abstract Objectives: The prevalence of hepatitis C virus (HCV) infection in Sweden is estimated to 0.5%. Before 1992, blood transfusion posed a risk of HCV transmission. The primary aim of this study was to estimate anti-HCV prevalence in Stockholm County among individuals receiving blood transfusions 1965–1991. The secondary aim was to study the effect of age at transfusion on the development of liver disease and treatment outcome. Materials and methods: This is a retrospective analysis of individuals found to be anti-HCV tested positive in Stockholm County during a national screening campaign in Sweden 2008–2010. All anti-HCV-positive individuals were also HCV RNA tested. Data on age at transfusion, age at diagnosis, HCV genotype, viral load, fibrosis score, liver histology and antiviral treatment were recorded. Results: Out of 7473, 134 (1.8%) tested individuals were anti-HCV positive and 102 were HCV RNA positive resulting in a prevalence of chronic hepatitis C (CHC) of 1.4%. The rate of advanced liver damage was 18% (10/56). Patients younger than 19 years of age at transfusion were significantly more often started on antiviral treatment compared to adult patients, 65% vs 29% p < . 001. No significant correlation was found between treatment outcome and gender or age at transfusion. Conclusions: In this study, we found an anti-HCV prevalence of 1.8% which is considerably higher than the estimated prevalence in the Swedish general population (0.5%), and patients infected during childhood were more likely to receive antiviral treatment. Additional data on the HCV epidemic in Sweden are needed regarding prevalence and age distribution.
Between tradition and modernity: girls’ talk about sexual relationships and violence in Kenya, Ghana and Mozambique

ABSTRACT This paper interrogates the influence of a tradition-modernity dichotomy on perspectives and practices on sexual violence and sexual relationships involving girls in three districts of Kenya, Ghana and Mozambique. Through deploying an analytical framework of positioning within multiple discursive sites, we argue that although the dichotomy misrepresents the complexity of contemporary communities, it is nonetheless deployed by girls, educational initiatives and researchers in their reflections on girls’ sexual practices and sexual violence. The analysis examines variations between communities in patterns of and perspectives about sexual relationships, transactional sex and sexual violence. It illuminates ways in which features of ‘modernisation’ and ‘tradition’ both exacerbate and protect girls from violence. Across contexts, girls actively positioned themselves between tradition and modernity, while positioning others at the extreme poles. Education initiatives also invoked bipolar positions in their attempts to protect girls’ rights to education and freedom from violence. The paper concludes by considering the implications for educational intervention and the potential for the analytical framing to generate richer, more contextualised understandings about girls’ perspectives, experiences and ways of resisting sexual violence.
Contagion of Cheating Behaviors in Online Social Networks

Human behaviors are known to spread through social contact. The diffusion process on social networks has also been leveraged to understand the spread of undesirable contagion. The contagion of malicious or even criminal behaviors in online social networks is just beginning to attract attention. Here, we study the social contagion problem of cheating behavior found in the massively multiplayer online role-playing game (MMORPG) that provides a lifelike environment with rich and realistic user interactions. Because cheating users boast an abnormal thus conspicuous degree of success, it has a strong chance of being noticed by their friends and leading them to cheat themselves. To detect and prevent cheating, it is beneficial to understand this dynamic as a contagion problem. In this paper, we show the existence of the contagion of cheating. We then explore various possible social reinforcement mechanisms after introducing several factors to quantify the effect of social reinforcement on the contagion and analyze the dynamics of bot diffusion in an extensive user interaction log from a major MMORPG.
Large System Analysis of Two-Stage Beamforming With Limited Feedback in FDD Massive MIMO Systems

Two-stage beamforming is a transmit strategy that uses two types of beamformers to reduce the feedback overhead of frequency-division-duplexing massive multiple-input multiple-output systems that are spatially correlated. In this paper, we present large system analysis of two-stage beamforming when a transmitter has limited channel information from feedback and when regularized-zero-forcing (RZF) is used as a second-stage beamformer. We consider two random-vector-quantization-based feedback schemes that can be respectively applied when users have perfect channel information or perfect effective channel information. For each feedback method, we analyze both expected signal-to-interference-plus-noise ratio (SINR) and expected rate loss and then characterize their bounds as a function of the number of feedback bits. From the characterization, we reveal that the sum rate of two-stage beamforming is very sensitive to the regularization parameter of RZF, especially when the number of feedback bits is limited. Motivated by this, we derive the optimal regularization parameter that maximizes the SINR of two-stage beamforming with limited feedback. Using simulations, we verify the tightness of the characterized bounds and quantify how the use of the optimal regularization parameter improves the sum rate of two-stage beamforming.
Sentiment analysis of Twitter corpus related to artificial intelligence assistants

Providing an enhancing experience is one of the most significant current issues in the user's research. A process that improves user's experience should be required to evaluate the usability and emotion. Above all, sentiment analysis based on user's opinions can be used to understand user's tendency. This paper aims to make a criterion what artificial intelligence assistant is statistically better. User's opinions about three artificial intelligence assistants from Twitter were collected and classified into positive, negative, neutral opinions by a lexicon named Valence Aware Dictionary and sEntiment Reasoner (VADER). Also, we analyzed tweets through independent samples T-test, Kruskal-Wallis test, and Mann-Whitney test to show the statistical significance among groups. The results suggested the highest rank of three artificial intelligence assistants by using statistical analysis.
Typologic versus population concepts of chronospecies : implications for ammonite biostratigraphy

The effects of the application of horizontal (population) as opposed to vertical (typologic) taxonomy on precision of ammonite chronozonation are discussed and an example of stratigraphic inference that can be derived from the population method is presented. An almost complete macroconch from Świetoszewo, Pomerania, is compared with a standard, biometrically studied series of samples from the Early Volgian of Brzostowka, Central Poland. Specimens identical morphologically with that from Świetoszewo can be found among extreme end-members of the population variability much above and below the horizon of the best fit with the mean for the sample. Time correlation is thus a probabilistic kind of inference as the probable identity of time decreases with increasing distance from the horizon of the best fit. For this reason vertical (typologie) diagnoses of chronospecies cannot result in better and more rellable correlations than a horizontal (population) one. Because the typologie species concept leads to false representation in phylogenetics and biogeography it is proposed to abandon its use in biostratigraphy.
Deep Learning Approximation: Zero-Shot Neural Network Speedup

Neural networks offer high-accuracy solutions to a range of problems, but are costly to run in production systems because of computational and memory requirements during a forward pass. Given a trained network, we propose a techique called Deep Learning Approximation to build a faster network in a tiny fraction of the time required for training by only manipulating the network structure and coefficients without requiring re-training or access to the training data. Speedup is achieved by by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass. First, lossless optimizations are applied, followed by lossy approximations using singular value decomposition (SVD) and low-rank matrix decomposition. The optimal approximation is chosen by weighing the relative accuracy loss and FLOP reduction according to a single parameter specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can be re-gained by finetuning.
Hardware Trojan Attacks on Neural Networks

With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural networks and other machine learning models. As the technology evolves, it is also plausible that machine learning or artificial intelligence will soon become consumer electronic products and military equipment, in the form of well-trained models. Unfortunately, the modern fabless business model of manufacturing hardware, while economic, leads to deficiencies in security through the supply chain. In this paper, we illuminate these security issues by introducing hardware Trojan attacks on neural networks, expanding the current taxonomy of neural network security to incorporate attacks of this nature. To aid in this, we develop a novel framework for inserting malicious hardware Trojans in the implementation of a neural network classifier. We evaluate the capabilities of the adversary in this setting by implementing the attack algorithm on convolutional neural networks while controlling a variety of parameters available to the adversary. Our experimental results show that the proposed algorithm could effectively classify a selected input trigger as a specified class on the MNIST dataset by injecting hardware Trojans into $0.03\%$, on average, of neurons in the 5th hidden layer of arbitrary 7-layer convolutional neural networks, while undetectable under the test data. Finally, we discuss the potential defenses to protect neural networks against hardware Trojan attacks.
CGraph: A Correlations-aware Approach for Efficient Concurrent Iterative Graph Processing

With the fast growing of iterative graph analysis applications, the graph processing platform has to efficiently handle massive Concurrent iterative Graph Processing (CGP) jobs. Although it has been extensively studied to optimize the execution of a single job, existing solutions face high ratio of data access cost to computation for the CGP jobs due to significant cache interference and memory wall, which incurs low throughput. In this work, we observed that there are strong spatial and temporal correlations among the data accesses issued by different CGP jobs because these concurrently running jobs usually need to repeatedly traverse the shared graph structure for the iterative processing of each vertex. Based on this observation, this paper proposes a correlations-aware execution model, together with a core-subgraph based scheduling algorithm, to enable these CGP jobs to efficiently share the graph structure data in cache/memory and their accesses by fully exploiting such correlations. It is able to achieve the efficient execution of the CGP jobs by effectively reducing their average ratio of data access cost to computation and therefore delivers a much higher throughput. In order to demonstrate the efficiency of the proposed approaches, a system called CGraph is developed and extensive experiments have been conducted. The experimental results show that CGraph improves the throughput of the CGP jobs by up to 2.31 times in comparison with the existing solutions.