Miracle-dz/UniFAR-FTUs huggingface dataset:
Autovesk: Automatic vectorized code generation from unstructured static kernels using graph transformations

Leveraging the SIMD capability of modern CPU architectures is mandatory to take full advantage of their increased performance. To exploit this capability, binary executables must be vectorized, either manually by developers or automatically by a tool. For this reason, the compilation research community has developed several strategies for transforming scalar code into a vectorized implementation. However, most existing automatic vectorization techniques in modern compilers are designed for regular codes, leaving irregular applications with non-contiguous data access patterns at a disadvantage. In this paper, we present a new tool, Autovesk, that automatically generates vectorized code from scalar code, specifically targeting irregular data access patterns. We describe how our method transforms a graph of scalar instructions into a vectorized one, using different heuristics to reduce the number or cost of instructions. Finally, we demonstrate the effectiveness of our approach on various computational kernels using Intel AVX-512 and ARM SVE. We compare the speedups of Autovesk vectorized code over GCC, Clang LLVM and Intel automatic vectorization optimizations. We achieve competitive results on linear kernels and up to 11x speedups on irregular kernels.
Improving Expert Predictions with Conformal Prediction

Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Otherwise, the experts may be better off solving the classification tasks on their own. In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance. Rather than providing (single) label predictions and letting experts decide when to trust these predictions, our system provides sets of label predictions constructed using conformal prediction$\unicode{x2014}$prediction sets$\unicode{x2014}$and forcefully asks experts to predict labels from these sets. By using conformal prediction, our system can precisely trade-off the probability that the true label is not in the prediction set, which determines how frequently our system will mislead the experts, and the size of the prediction set, which determines the difficulty of the classification task the experts need to solve using our system. In addition, we develop an efficient and near-optimal search method to find the conformal predictor under which the experts benefit the most from using our system. Simulation experiments using synthetic and real expert predictions demonstrate that our system may help experts make more accurate predictions and is robust to the accuracy of the classifier the conformal predictor relies on.
Summarizing Web Archive Corpora Via Social Media Storytelling By Automatically Selecting and Visualizing Exemplars

People often create themed collections to make sense of an ever-increasing number of archived web pages. Some of these collections contain hundreds of thousands of documents. Thousands of collections exist, many covering the same topic. Few collections include standardized metadata. This scale makes understanding a collection an expensive proposition. Our Dark and Stormy Archives (DSA) five-process model implements a novel summarization method to help users understand a collection by combining web archives and social media storytelling. The five processes of the DSA model are: select exemplars, generate story metadata, generate document metadata, visualize the story, and distribute the story. Selecting exemplars produces a set of k documents from the N documents in the collection, where k < <N, thus reducing the number of documents visitors need to review to understand a collection. Generating story and document metadata selects images, titles, descriptions, and other content from these exemplars. Visualizing the story ties this metadata together in a format the visitor can consume. Without distributing the story, it is not shared for others to consume. We present a research study demonstrating that our algorithmic primitives can be combined to select relevant exemplars that are otherwise undiscoverable using a conventional search engine and query generation methods. Having demonstrated improved methods for selecting exemplars, we visualize the story. Previous work established that the social card is the best format for visitors to consume surrogates. The social card combines metadata fields, including the document’s title, a brief description, and a striking image. Social cards are commonly found on social media platforms. We discovered that these platforms perform poorly for mementos and rely on web page authors to supply the necessary values for these metadata fields. With web archives, we often encounter archived web pages that predate the existence of this metadata. To generate this missing metadata and ensure that storytelling is available for these documents, we apply machine learning to generate the images needed for social cards with a Precision@1 of 0.8314. We also provide the length values needed for executing automatic summarization algorithms to generate document descriptions. Applying these concepts helps us create the visualizations needed to fulfill the final processes of story generation. We close this work with examples and applications of this technology.
Disentangling the effects of non-adiabatic interactions upon ion self-diffusion within warm dense hydrogen

Warm dense matter is a material state in the region of parameter space connecting condensed matter to classical plasma physics. In this intermediate regime, we investigate the significance of non-adiabatic electron-ion interactions upon ion dynamics. To disentangle non-adiabatic from adiabatic electron-ion interactions, we compare the ion self-diffusion coefficient from the non-adiabatic electron force field computational model with an adiabatic, classical molecular dynamics simulation. A classical pair potential developed through a force-matching algorithm ensures the only difference between the models is due to the electronic inertia. We implement this new method to characterize non-adiabatic effects on the self-diffusion of warm dense hydrogen over a wide range of temperatures and densities. Ultimately we show that the impact of non-adiabatic effects is negligible for equilibrium ion dynamics in warm dense hydrogen. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.
Urban Knowledge Graph Aided Mobile User Profiling

Nowadays, the explosive growth of personalized web applications and the rapid development of artificial intelligence technology have flourished the recent research on mobile user profiling, i.e., inferring the user profile from mobile behavioral data. Particularly, existing studies mainly follow the data-driven paradigm to develop feature engineering and representation learning on such data, which however suffer from the robustness issue, i.e., generalizing poorly across datasets and profiles without considering semantic knowledge therein. In comparison, the rising knowledge-driven paradigm built upon the knowledge graph (KG) offers a potential solution to mitigate such weakness. Therefore, in this article, we propose a Knowledge Graph aided framework for Mobile User Profiling (KG-MUP). Specifically, to distil semantic knowledge among data, we firstly construct an urban knowledge graph (UrbanKG) with domain entities like users, regions, point of interests (POIs), and so on. identified, as well as semantic relations for home, workplace, spatiality, and so on. extracted. Moreover, we leverage tensor decomposition and graph neural network to obtain knowledgeable user representations from UrbanKG. In addition, we introduce several customized features to quantify individual mobility characteristics for mobile user profiling. Extensive experiments on three real-world mobility datasets demonstrate that KG-MUP achieves state-of-the-art performance on user profile inference tasks. Moreover, further results also reveal the importance of various semantic knowledge to user profile inference, which provides meaningful insights on user modeling with mobile behavioral data.
Procedural Modeling Based Shape Grammar as A Key to Generating Digital Architectural Heritage

Digital architectural heritage content creation for virtual worlds nowadays is one of the most important challenges with the ever-increasing expenses of manual content development. Procedural modeling approaches have become a key tool for automating the design and reconstruction of structures and urban environments to disseminate them through appropriate multimedia approaches. This study presents a general architectural modeling system that combines the full generative power of shape grammars with the ease of use and flexibility of procedural modeling parameters which allows for the creation of heritage buildings that adhere to shape grammars. To evaluate the system, we conducted an initial user study with 20 participants from the Al-Zaytoonah university of Jordan to assess the user experience, user impression, and effectiveness of the system. Participants’ feedbacks were encouraging, indicating that the proposed approach could be effective and beneficial in assisting the creation of digital architectural heritage urban virtual content.
“The Generalizability of Online Experiments Conducted During The COVID-19 Pandemic” – CORRIGENDUM

In the course of running computational reproducibility checks for Peyton, Huber and Coppock (2021), the Yale ISPS Data Archive identified mistakes concerning the number of statistically significant differences reported for comparisons made between pre-COVID experiments and our replications. These mistakes arose because of errors in the code used to adjust p-values for multiple comparisons, leading to inconsistent reporting of the number of significant differences before and after adjustment. Correcting these mistakes affects Figures 2-3 and the first paragraph on p. 6 in the published article. All point estimates, standard errors, and substantive conclusions are unchanged. In Figure 1, we present the original version of Figure 2 from Peyton, Huber and Coppock (2021) in the top panel (1a) and the correction in the bottom panel (1b). Figure 2 likewise shows the original version of Figure 3 in the top panel (2a) and the correction in the bottom panel (2b). The text on p. 6 of Peyton, Huber and Coppock (2021) reads:
Pollution tracker: Finding industrial sources of aerosol emission in satellite imagery

Abstract The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, large sources of greenhouse gases, also emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a supervised deep learning algorithm to detect unreported industry sites in high-resolution satellite data, using the existing data sets for training. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held-out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives, improving the precision to 42% overall, so that human review becomes feasible. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement existing data sets of heavy industry sites.
Telecommuting and job performance during the COVID-19 pandemic: The mediating role of psychological detachment from work

This study examined the mediating role of psychological detachment from work and the moderating influence of perceived organisational telework task support in the relationship between the extent of telecommuting and the job performance of employees. Using a time-lag study design, we collected data from a sample of 435 employees from several companies in China during the COVID-19 pandemic (female = 67.8%; 31–40 years old = 51.7%; mean telecommuting hours = 8.29 hours per week; SD = 11.86). Following the mediating analyses, our results showed psychological detachment from work significantly mediated this relationship between the extent of telecommuting and the job performance of employees, so that employee job performance was lower. Furthermore, perceived organisational telework task support did not moderate the relationship between the extent of telecommuting and job performance. These findings suggest a need for work managers to provide telecommute support as part of work organisation COVID-19 mitigation.
Attachment and general well-being in breast cancer patients: Mediation of body esteem and social support

This study aimed to explore the mediation of body esteem and social support in the relationship between attachment and general well-being in breast cancer patients. We collected data from 150 women with breast cancer and 150 healthy women. Employing bootstrap analysis with 5 000 samples using the ordinary least squares regression, we found that higher attachment avoidance was significantly associated with lower general well-being in both the breast cancer patients and healthy comparison peers. Also, body esteem and social support were significantly associated with attachment anxiety and attachment avoidance and general well-being. Furthermore, body esteem significantly mediated the relationship between attachment avoidance and well-being in breast cancer patients directly through social support; while social support significantly mediated the relationship between attachment anxiety and attachment avoidance and well-being in breast cancer women directly by body esteem. Therefore, body esteem and social support played a chain intermediary role between attachment avoidance and well-being. These findings suggest a need for breast cancer support programmes for women to enhance their body esteem and sense of social support in the context of their recovery efforts.
Mindfulness and meaning in life: The mediating effect of self-concept clarity and coping self-efficacy during the COVID-19 pandemic

The current study aimed to investigate self-concept clarity and coping self-efficacy as mechanisms for mindfulness and meaning in life in a community pandemic experience. Participants were 538 Chinese college students (female = 70.40%, mean age = 20.01 years, SD = 1.31 years). The students completed the following measures: Mindful Attention Awareness Scale, Self-concept Clarity Scale, Coping Self-efficacy Scale, and Meaning in Life Questionnaire. Following path analyses, results showed that mindfulness was significantly and positively related to meaning in life. Mediation analysis revealed that self-concept clarity, as well as coping self-efficacy, played a mediating role between mindfulness and meaning in life, increasing meaning in life. In terms of self-concept and coping self-efficacy, coping self-efficacy had a stronger effect on the relationship between mindlessness and meaning in life, accounting for 33.0% and 64.3% of the total effect, respectively. The findings suggest that gains in mindfulness predicted greater meaning in life through both self-concept clarity and coping self-efficacy.
Development and Application of Bayesian Semiparametric Models For Dependent Data

Dependent data are very common in many research fields, such as medicine (repeated measures), finance (time series), traffic (clustered), etc. Effective control/modeling of the dependency among data can enhance the performance of the models and result in better prediction. In many cases, the correlation itself may be of great interest. In this dissertation, we develop novel Bayesian semi-/nonparametric regression models to analyze data with various dependence structures. In Chapter 2, a Bayesian nonparametric multivariate ordinal regression model is proposed to fit drinking behavior survey data from DWI offenders. The responses are two-dimensional ordinal data, drinking frequency and drinking quantity at each occasion. In Chapter 3, we develop a hierarchical Gaussian process model to analyze repeated hearing test data of pediatric cancer patients. A penalized B-spline is used to capture the overall trend of the curve. Individual intercepts and slopes as random effects are allowed to model individual deviation from the population mean. Since the curves are theoretically smooth, a hierarchical Gaussian process is assumed on top of the individual-specific mean function. In Chapter 4, we propose a constructive approach to imposing a mean constraint in a finite mixture of multivariate normal densities. Implemented in a linear mixed model, the effectiveness of the constraint is verified by both simulation and data analysis using longitudinal cholesterol data.
Robust linear quadratic regulator applied to an inverted pendulum

The natural instability of an inverted pendulum and its dynamics richness, in terms of nonlinearity, provide a nice apparatus to reproduce behaviors of analogous systems. In this way, it is useful to perform benchmark tests for new control approaches developed. In this paper, we address the main inverted pendulum problems: pendulum stabilization, tracking, and catching swing‐up control. We show how robust recursive, control and filtering, techniques improve the system performance. They are developed to solve stochastic problems based on deterministic approaches, in order to decrease the worst influence of uncertainties. Experimental results of the proposed robust approach provide robust stability and performance despite parametric uncertainties, disturbances, and noise effects.
On The Structured Manifold Optimization: Reduced-rank and Positive Definite Matrix Estimation

This thesis mainly proposes optimization schemes regarding both types of structured matrices, as well as their applications in statistics. The first structured matrix optimization is proposed under the reduced-rank constraint, and particularly it can be applied for conducting multi-label classification and variable selection simultaneously. The proposed algorithm for optimization is computationally efficient and delivers superior numerical performance in terms of both classification and variable selection accuracy. The asymptotic consistencies are also established to support the advantages of the proposed method. The second structured matrix optimization proposes the estimation of sparse positive definite matrix generated by a generic coordinate descent (CD) algorithm, and particularly it can be applied to the estimation of the high-dimensional covariance matrix and inverse covariance or precision matrix with variable selection.
The human Dicer helicase domain mediates ATP hydrolysis and RNA rearrangement

Vertebrates have one Dicer ortholog that generates both microRNAs (miRNAs) and small interfering RNAs (siRNAs), in contrast to the multiple Dicer-like proteins found in flies and plants. Here, we focus on the functions of the human Dicer (hDicer) helicase domain. The helicase domain of hDicer is known to recognize pre-miRNA substrates through interactions with their apical loop regions. Besides interacting with canonical substrates, the hDicer helicase domain has also been suggested to bind many different cellular RNAs; however, a comprehensive study of the biochemical activities and substrate specificity of the hDicer helicase domain towards different nucleic acids has yet to be undertaken. Here, we conduct such an analysis and reveal that full-length hDicer, through its helicase domain, hydrolyzes ATP. We also show that the hDicer helicase domain binds single-but not double-stranded RNAs and DNAs and that a structural rearrangement of the substrate accompanies the binding of single-stranded RNAs. This RNA rearrangement activity is ATP-independent. Our findings open new avenues for future studies aimed at defining the cellular activities of hDicer that may be associated with these newly described biochemical properties.
Agentic and communal narcissism in predicting different types of lies in romantic relationships

Several studies have investigated a potential positive association between agentic narcissism and general dishonesty, revealing both supportive and contradicting evidence. Few have focused on dishonesty within romantic relationships, a phenomenon that occurs in almost all partnerships. With the present research, we first aimed to extend existing literature on narcissism by including its two complementary facets (i.e., agentic and communal narcissism). Second, we aimed to improve the understanding of narcissists’ lying behavior in the context of partnerships by distinguishing between two different types of lies (i.e., self-centered and other-oriented lies). We hypothesized that both, people higher in agentic and communal narcissism, will report increased dishonesty toward their romantic partners (Hypothesis 1). Given the self-benefit function of self-centered lies and given that agentic narcissists aim to fulfill their relationship-based motives by agentic means, we predict agentic narcissism (compared with communal narcissism) will be a stronger predictor for self-centered lies (Hypothesis 1a). Given the other-benefiting function of other-oriented lies and given that communal narcissists aim to fulfill their motive of self-enhancement by communal means, we predict that communal narcissism (compared with agentic narcissism) will be a stronger predictor for other-oriented lies (Hypothesis 1b). In two preregistered online studies (N = 298: N = 256) we showed that people higher in agentic narcissism believed to be good liars, but this does not lead to higher self-reported frequencies of other-oriented and self-centered lies within romantic relationships historically; communal narcissism was also not related to self-reported deception. Limitations and directions for future research are discussed.