Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Enabled Communication Using NOMA

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.
Deep Code Search

To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat source code as textual documents and utilize information retrieval models to retrieve relevant code snippets that match a given query. These approaches mainly rely on the textual similarity between source code and natural language query. They lack a deep understanding of the semantics of queries and source code. In this paper, we propose a novel deep neural network named CODEnn (Code-Description Embedding Neural Network). Instead of matching text similarity, CODEnn jointly embeds code snippets and natural language descriptions into a high-dimensional vector space, in such a way that code snippet and its corresponding description have similar vectors. Using the unified vector representation, code snippets related to a natural language query can be retrieved according to their vectors. Semantically related words can also be recognized and irrelevant/noisy keywords in queries can be handled. As a proof-of-concept application, we implement a code search tool named DeepCS using the proposed CODEnn model. We empirically evaluate DeepCS on a large scale codebase collected from GitHub. The experimental results show that our approach can effectively retrieve relevant code snippets and outperforms previous techniques.
Redefining Prioritization: Continuous Prioritization for Continuous Integration

Continuous integration (CI) development environments allow soft-ware engineers to frequently integrate and test their code. While CI environments provide advantages, they also utilize non-trivial amounts of time and resources. To address this issue, researchers have adapted techniques for test case prioritization (TCP) to CI environments. To date, however, the techniques considered have operated on test suites, and have not achieved substantial improvements. Moreover, they can be inappropriate to apply when system build costs are high. In this work we explore an alternative: prioritization of commits. We use a lightweight approach based on test suite failure and execution history that is highly effcient; our approach "continuously" prioritizes commits that are waiting for execution in response to the arrival of each new commit and the completion of each previously scheduled commit. We have evaluated our approach on three non-trivial CI data sets. Our results show that our approach can be more effective than prior techniques.
Fast Blocking of Householder Reflectors on Graphics Processors

We revisit an alternative representation to the compact WY transform for the accumulation (blocking) of Householder reflectors that exhibits the same numerical stability and is composed of efficient computational kernels from Level-3 Basic Linear Algebra Subprograms (BLAS) in contrast with the Level-2 BLAS that are utilized for the construction of the conventional compact WY representation. For the orthogonal reduction to condensed forms on multicore platforms equipped with a fast graphics processing unit (GPU), (or when there is a notable gap in performance between the multicore processors and the graphics accelerator,) our approach removes the assembly of the accumulation from the critical path of the algorithm. This comes as a consequence of accelerating this operation via the use of Level-3 BLAS, moving this computation to the GPU, and allowing the use of larger algorithmic block sizes. Our experiments with the alternative orthogonal representation show considerable speed-ups, which can be in the range 20-40% on recent GPUs when compared with the codes in MAGMA.
Randomized Prior Functions for Deep Reinforcement Learning

Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable `prior' network to each ensemble member. We prove that this approach is efficient with linear representations, provide simple illustrations of its efficacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.
An Optimal Algorithm for Online Unconstrained Submodular Maximization

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,. ,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive over time. The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret algorithm for this problem, meaning that for every sequence of nonnegative submodular functions, the algorithm's expected total value is at least $1/2$ times that of the best subset in hindsight, up to an error term sublinear in $T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online algorithm when the submodular functions are presented as value oracles. Previous work on the offline problem implies that picking a subset uniformly at random in each time step achieves zero $1/4$-regret. A byproduct of our techniques is an explicit subroutine for the two-experts problem that has an unusually strong regret guarantee: the total value of its choices is comparable to twice the total value of either expert on rounds it did not pick that expert. This subroutine may be of independent interest.
The Neuropeptides Vasoactive Intestinal Peptide and Pituitary Adenylate Cyclase-Activating Polypeptide Control HIV-1 Infection in Macrophages Through Activation of Protein Kinases A and C

Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) are highly similar neuropeptides present in several tissues, endowed with immunoregulatory functions and other systemic effects. We previously reported that both neuropeptides reduce viral production in HIV-1-infected primary macrophages, with the participation of β-chemokines and IL-10, and now we describe molecular mechanisms engaged in this activity. Macrophages exposed to VIP or PACAP before HIV-1 infection showed resistance to viral replication, comparable to that observed when the cells were treated after infection. Also, multiple treatments with a suboptimal dose of VIP or PACAP after macrophage infection resulted in a decline of virus production similar to the inhibition promoted by a single exposure to the optimal inhibitory concentration. Cellular signaling pathways involving cAMP production and activation of protein kinases A and C were critical components of the VIP and PACAP anti-HIV-1 effects. Analysis of the transcription factors and the transcriptional/cell cycle regulators showed that VIP and PACAP induced cAMP response element-binding protein activation, inhibited NF-kB, and reduced Cyclin D1 levels in HIV-1-infected cells. Remarkably, VIP and PACAP promoted G-to-A mutations in the HIV-1 provirus, matching those derived from the activity of the APOBEC family of viral restriction factors, and reduced viral infectivity. In conclusion, our findings strengthen the antiretroviral potential of VIP and PACAP and point to new therapeutic approaches to control the progression of HIV-1 infection.
Benchmarking Wilms’ tumor in multisequence MRI data: why does current clinical practice fail? Which popular segmentation algorithms perform well?

Abstract. Wilms’ tumor is one of the most frequent malignant solid tumors in childhood. Accurate segmentation of tumor tissue is a key step during therapy and treatment planning. Since it is difficult to obtain a comprehensive set of tumor data of children, there is no benchmark so far allowing evaluation of the quality of human or computer-based segmentations. The contributions in our paper are threefold: (i) we present the first heterogeneous Wilms’ tumor benchmark data set. It contains multisequence MRI data sets before and after chemotherapy, along with ground truth annotation, approximated based on the consensus of five human experts. (ii) We analyze human expert annotations and interrater variability, finding that the current clinical practice of determining tumor volume is inaccurate and that manual annotations after chemotherapy may differ substantially. (iii) We evaluate six computer-based segmentation methods, ranging from classical approaches to recent deep-learning techniques. We show that the best ones offer a quality comparable to human expert annotations.
Tooth wear and microwear of theropods from the Late Maastrichtian Marília Formation (Bauru Group), Minas Gerais State, Brazil

ABSTRACT Little is known about the tooth wear of South American theropod dinosaurs. This paper describes wear facets in Abelisauridae, Carcharodontosauridae and some indeterminate theropods teeth, from the Marília Formation. Four types of wear facets are proposed: vertically-oriented attritional striations; perpendicular attritional surfaces; oval wear facets; and apical grooves. All these worn surfaces were produced by dental occlusion, except the apical grooves, which are produced by the contact between predator teeth and the prey bone during predator–prey interaction. More detailed biomechanical and hardness testing of teeth and bone may further elucidate the pattern of tooth wear in theropods.
Attitudes Toward Computers, the Introductory Course and Recruiting New Majors: Preliminary Results

In order to investigate reasons contributing to the decline in enrollment in computing programs in colleges and universities, this study examined student attitudes towards computing throughout an introductory computing course for non-majors at a northeastern metropolitan university. In addition to providing computer literacy, the course is used for recruiting students into the CS and IS majors. Six sections of the course, with participation ranging from 106 to 133 subjects at four measurement points, were administered the Loyd Gressard Computer Attitude Scale (Loyd and Gressard, 1984, 1985). This paper is a preliminary analysis of the computer attitude data of all students in all sections across two campuses. The results indicated that after statistically significant decreases in positive attitudes toward computers through the first 3 measures (2/3 of the course), there was a statistically non-significant rise at the end of the course once the programming part of the course was completed. The conclusion is that initial enthusiasm for computers, which may come from familiarity with using computers for recreational activities, drops rapidly as the work of learning computing concepts and skills begins.
Personal Informatics for Sport

Technological advances in wearable computing are changing the sports domain. A variety of Personal Informatics (PI) tools are starting to provide support and improve athletes’ performance in many sports. In this article, we interviewed 20 amateur and elite athletes of different disciplines, using an array of PI devices, to explore how sports, as well as athletes’ experience, are affected by such instruments. We discovered that amateur athletes present different patterns of usage compared to elite ones. Moreover, we found that elite athletes make sense of their data by exploiting the knowledge they have about their own body and sports practice. We then proposed four considerations for design that we believe should be explored in the future, to reflect on how self-tracking is changing our perspective on sports, and, by and large, on our everyday life.
Chaining Mutual Information and Tightening Generalization Bounds

Bounding the generalization error of learning algorithms has a long history, which yet falls short in explaining various generalization successes including those of deep learning. Two important difficulties are (i) exploiting the dependencies between the hypotheses, (ii) exploiting the dependence between the algorithm's input and output. Progress on the first point was made with the chaining method, originating from the work of Kolmogorov, and used in the VC-dimension bound. More recently, progress on the second point was made with the mutual information method by Russo and Zou '15. Yet, these two methods are currently disjoint. In this paper, we introduce a technique to combine the chaining and mutual information methods, to obtain a generalization bound that is both algorithm-dependent and that exploits the dependencies between the hypotheses. We provide an example in which our bound significantly outperforms both the chaining and the mutual information bounds. As a corollary, we tighten Dudley's inequality when the learning algorithm chooses its output from a small subset of hypotheses with high probability.
Partial control-flow linearization

If-conversion is a fundamental technique for vectorization. It accounts for the fact that in a SIMD program, several targets of a branch might be executed because of divergence. Especially for irregular data-parallel workloads, it is crucial to avoid if-converting non-divergent branches to increase SIMD utilization. In this paper, we present partial linearization, a simple and efficient if-conversion algorithm that overcomes several limitations of existing if-conversion techniques. In contrast to prior work, it has provable guarantees on which non-divergent branches are retained and will never duplicate code or insert additional branches. We show how our algorithm can be used in a classic loop vectorizer as well as to implement data-parallel languages such as ISPC or OpenCL. Furthermore, we implement prior vectorizer optimizations on top of partial linearization in a more general way. We evaluate the implementation of our algorithm in LLVM on a range of irregular data analytics kernels, a neutronics simulation benchmark and NAB, a molecular dynamics benchmark from SPEC2017 on AVX2, AVX512, and ARM Advanced SIMD machines and report speedups of up to 146 % over ICC, GCC and Clang O3.
Dynamic Balancing of Exploration and Exploitation: The Contingent Benefits of Ambidexterity

We study the evolution of firms’ exploration–exploitation allocations and their long-term performance outcomes. Extending current ambidexterity theory, we suggest that not only firms pursuing one-sided exploration or exploitation orientations show self-reinforcing tendencies but also ambidextrous firms adopting balanced exploration–exploitation orientations. Integrating formal modeling arguments, we further propose that reinforcing ambidexterity can be good or bad for firms’ long-term performance, depending on the environment they face: In contexts characterized by incremental change, firms benefit more from the learning effects of maintaining ambidexterity, which lead to superior performance. Firms in discontinuous change contexts, however, suffer more from the misalignment that reinforcement creates, which affects their performance negatively. A longitudinal data set of global insurance firms (1999–2014) supports our arguments. Building on these findings, we reconceptualize ambidexterity as the ability to dynamically balance exploration and exploitation, which emerges from combining capability-building processes (to balance exploration and exploitation) with capability-shifting processes (to adapt the exploration–exploitation balance). We contribute to the organizational literature by developing a dynamic perspective on balancing exploration and exploitation, by clarifying the contingent nature of the ambidexterity–firm performance relationship, and by integrating and extending the ambidexterity and formal modeling perspectives on exploration and exploitation.
Bridging Psychological and Educational Research on Rational Number Knowledge

In this paper we focus on the development of rational number knowledge and present three research programs that illustrate the possibility of bridging research between the fields of cognitive developmental psychology and mathematics education. The first is a research program theoretically grounded in the framework theory approach to conceptual change. This program focuses on the interference of prior natural number knowledge in the development of rational number learning. The other two are the research program by Moss and colleagues that uses Case’s theory of cognitive development to develop and test a curriculum for learning fractions, and the research program by Siegler and colleagues, who attempt to formulate an integrated theory of numerical development. We will discuss the similarities and differences between these approaches as a means of identifying potential meeting points between psychological and educational research on numerical cognition and in an effort to bridge research between the two fields for the benefit of rational number instruction.
Alternative Management Practices Improve Soil Health Indices in Intensive Vegetable Cropping Systems: A Review

An increase in intensive cropping would benefit society by providing food to a growing population, and vegetable production is an excellent example of intensive cropping systems that are indeed on the rise. Vegetable cropping systems are high-input and generally require large quantities of fertilization, frequent irrigation, and repeated tillage operations. Consequently, an increase in global vegetable production may have seriously negative impacts on soil health and ecosystem services. Yet, not only maintaining but improving soil health is critical to enhancing the sustainability of food production systems. Previous agricultural research mainly focused on field crop systems and largely ignored vegetable cropping systems; consequently, this represents a conspicuous research gap, one that must be addressed in order to make progress towards sustainable food production. Here, we review the literature to gain a better understanding of how management has influenced various soil health indices (soil biology, chemistry, and physical dynamics) and to evaluate the implications for soil ecosystem services in vegetable cropping systems. We found that alternative modifications to conventional vegetable production systems, which resemble methods used in organic or conservation agriculture, tended to improve aspects of soil health. For example, soil amendments generally improved soil chemical indices of health – soil carbon levels and nitrogen reserves in particular. Incorporation of cover crops to vegetable crop rotations tended to improve nitrogen recycling via reduced nitrate leaching risks, increased soil carbon levels, and weed suppression. Reduced tillage systems were rare, presenting an important challenge and opportunity for further improving soil health dynamics in vegetable production. Notably, adopting alternative practices generally had no effect on crop yields, which implies little risk of yield penalties when agronomic management is carefully planned. Our results indicate that future sustainable vegetable cropping systems may embody a blend between organic and conventional ideologies to better maintain or improve soil ecosystem functioning.
The Future Cybersecurity Workforce: Going Beyond Technical Skills for Successful Cyber Performance

One of the challenges in writing an article reviewing the current state of cyber education and workforce development is that there is a paucity of quantitative assessment regarding the cognitive aptitudes, work roles, or team organization required by cybersecurity professionals to be successful. In this review, we argue that the people who operate within the cyber domain need a combination of technical skills, domain specific knowledge, and social intelligence to be successful. They, like the networks they operate, must also be reliable, trustworthy, and resilient. Defining the knowledge, skills, attributes, and other characteristics is not as simple as defining a group of technical skills that people can be trained on; the complexity of the cyber domain makes this a unique challenge. There has been little research devoted to exactly what attributes individuals in the cyber domain need. What research does exist places an emphasis on technical and engineering skills while discounting the important social and organizational influences that dictate success or failure in everyday settings. This paper reviews the literature on cyber expertise and cyber workforce development to identify gaps and then argues for the important contribution of social fit in the highly complex and heterogenous cyber workforce. We then identify six assumptions for the future of cybersecurity workforce development, including the requirement for systemic thinkers, team players, a love for continued learning, strong communication ability, a sense of civic duty, and a blend of technical and social skill. Finally, we make recommendations for social and cognitive metrics which may be indicative of future performance in cyber work roles to provide a roadmap for future scholars.