Miracle-dz/UniFAR-FTUs huggingface dataset:
Only Time Will Tell: Modelling Information Diffusion in Code Review with Time-Varying Hypergraphs

Background: Modern code review is expected to facilitate knowledge sharing: All relevant information, the collective expertise, and meta-information around the code change and its context become evident, transparent, and explicit in the corresponding code review discussion. The discussion participants can leverage this information in the following code reviews; the information diffuses through the communication network that emerges from code review. Traditional time-aggregated graphs fall short in rendering information diffusion as those models ignore the temporal order of the information exchange: Information can only be passed on if it is available in the first place. Aim: This manuscript presents a novel model based on time-varying hypergraphs for rendering information diffusion that overcomes the inherent limitations of traditional, time-aggregated graph-based models. Method: In an in-silico experiment, we simulate an information diffusion within the internal code review at Microsoft and show the empirical impact of time on a key characteristic of information diffusion: the number of reachable participants. Results: Time-aggregation significantly overestimates the paths of information diffusion available in communication networks and, thus, is neither precise nor accurate for modelling and measuring the spread of information within communication networks that emerge from code review. Conclusion: Our model overcomes the inherent limitations of traditional, static or time-aggregated, graph-based communication models and sheds the first light on information diffusion through code review. We believe that our model can serve as a foundation for understanding, measuring, managing, and improving knowledge sharing in code review in particular and information diffusion in software engineering in general.
Machine Learning the Dimension of a Polytope

We use machine learning to predict the dimension of a lattice polytope directly from its Ehrhart series. This is highly effective, achieving almost 100% accuracy. We also use machine learning to recover the volume of a lattice polytope from its Ehrhart series, and to recover the dimension, volume, and quasi-period of a rational polytope from its Ehrhart series. In each case we achieve very high accuracy, and we propose mathematical explanations for why this should be so.
Linearization of the Box-ball System with Box Capacity L

We construct a bijection between the state of the box-ball system with box capacity L and a pair of two sequences. In time evolution, one of the sequences moves at speed 1, and the other follows the rules of the box-ball system with box capacity one, which can be linearized by the Kerov-Kirillov-Reshetikhin(KKR) bijection. Our method can be applied to a state including a negative value or a value greater than the box capacity.
Exploiting Patent Documents for Cross-Domain Knowledge Transfer in Innovative Engineering Design: A Doc2Vec-GAT-Based Approach

The innovative design of complex engineering products is a knowledge-intensive and technology- driven task. Since technologies with the same functions could be deployed in similar situations under different domains, cross-domain knowledge transfer would prosper the success of innovative engineering product design. However, transferring proper specialized domain knowledge faces the defect of returning arbitrary results. One of the causes is the insufficient knowledge mining of the knowledge resources. Given that patents are high-quality knowledge resources in almost all technological fields, this study presents a Doc2Vec-Graph Attention Network (GAT)-based approach to exploit patent documents for cross-domain knowledge transfer. First, a three-dimensional patent knowledge model containing domain, function, and technology was built to define the key features of patent contents. Second, an approach integrating Doc2Vec and GAT was proposed to learn the patent content and patent citation relationships respectively, thereafter constructing a patent knowledge space. Third, to formalize the designer queries, a knowledge requirement space is established by matching the vectorized queries to the most similar patents. Finally, a cross-domain knowledge transfer mechanism is proposed based on a diversified searching method to map the knowledge requirement space to the patent knowledge space. A case study of the knowledge transfer on a sealing structure design of aircraft fuel tank and quantitative comparative experiments verified the feasibility of our approach.
Latency Minimization for mmWave D2D Mobile Edge Computing Systems: Joint Task Allocation and Hybrid Beamforming Design

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.
Sharp convergence for sequences of Schrödinger means and related generalizations

For decreasing sequences $\{t_{n}\}_{n=1}^{\infty }$ converging to zero and initial data $f\in H^s(\mathbb {R}^N)$ , $N\geq 2$ , we consider the almost everywhere convergence problem for sequences of Schrödinger means ${\rm e}^{it_{n}\Delta }f$ , which was proposed by Sjölin, and was open until recently. In this paper, we prove that if $\{t_n\}_{n=1}^{\infty }$ belongs to Lorentz space ${\ell }^{r,\infty }(\mathbb {N})$ , then the a.e. convergence results hold for $s>\min \{\frac {r}{\frac {N+1}{N}r+1},\,\frac {N}{2(N+1)}\}$ . Inspired by the work of Lucà-Rogers, we construct a counterexample to show that our a.e. convergence results are sharp (up to endpoints). Our results imply that when $0< r<\frac {N}{N+1}$ , there is a gain over the a.e. convergence result from Du-Guth-Li and Du-Zhang, but not when $r\geq \frac {N}{N+1}$ , even though we are in the discrete case. Our approach can also be applied to get the a.e. convergence results for the fractional Schrödinger means and nonelliptic Schrödinger means.
Spurious Correlations between Galaxies and Multiepoch Image Stacks in the DESI Legacy Surveys

A nonnegligible source of systematic bias in cosmological analyses of galaxy surveys is the on-sky modulation that is caused by foregrounds and variable image characteristics, such as observing conditions. Standard mitigation techniques perform a regression between the observed galaxy density field and sky maps of the potential contaminants. Such maps are ad hoc lossy summaries of the heterogeneous sets of coadded exposures that contribute to the survey. We present a methodology for addressing this limitation, and we extract spurious correlations between the observed distributions of galaxies and arbitrary stacks of single-epoch exposures. We study four types of galaxies (luminous red galaxies, emission-line galaxies, quasars, and Lyman-break galaxies) in the three regions of the DESI Legacy Surveys (North, South, and Dark Energy Survey), resulting in 12 samples with varying levels and types of contamination. We find that the new technique outperforms the traditional ones in all cases, and is able to remove higher levels of contamination. This paves the way for new methods that extract more information from multiepoch galaxy survey data and mitigate large-scale biases more effectively.
Load Modulation for Backscatter Communication: Channel Capacity and Capacity-Approaching Finite Constellations

In backscatter communication (BC), a passive tag transmits information by just affecting an external electromagnetic ﬁeld through load modulation. Thereby, the feed current of the excited tag antenna is modulated by adapting the passive termination load. This paper studies the achievable information rates with a freely adaptable passive load. As a prerequisite, we unify monostatic, bistatic, and ambient BC with circuit-based system modeling. A crucial insight is that channel capacity is described by existing results on peak-power-limited quadrature Gaussian channels, because the steady-state tag current phasor lies on a disk. Consequently, we derive the channel capacity in the case of an unmodulated external ﬁeld, for a general passive or purely reactive or resistive tag load. We ﬁnd that modulating both resistance and reactance is crucial for high rates. We discuss the capacity-achieving load statistics, the rate asymptotics, and the capacity of ambient BC in important special cases. Furthermore, we propose a capacity-approaching ﬁnite constellation design: a tailored amplitude-and-phase-shift keying on the reﬂection coefﬁcient. We also demonstrate high rates for very simple loads of just a few switched resistors and capacitors. Finally, we investigate the rate loss from a value-range-constrained load, which is found to be small for moderate constraints.
Topology Reconfigurable Series Resonant Converter with Fixed-frequency PWM Control for Ultra Wide Voltage Applications

This paper proposes a topology reconfigurable series resonant converter with fixed-frequency pulse width modulation (PWM) control for chargers of electric vehicles in ultra wide voltage applications. Both sides of the switches adopt PWM control, which can achieve load independent voltage regulation and full range soft switching. In addition, by topology reconstruction, the converter operates in multiple operation modes, thereby achieving an exceptional 8-fold ultra wide voltage regulation range. A simulation model is developed, including an input voltage range of 100-800V and an output voltage of 400V. The simulation results affirm the reliability and validity of the proposed model.
Integrated Sensing and Communications with Joint Beam Squint and Beam Split for Massive MIMO

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communication systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth. However, the growing size of transmission bandwidth and antenna array results in the beam squint effect, which hampers the communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitive for practical systems. In this paper, instead of alleviating the wideband beam squint effect, we take advantage of joint beam squint and beam split effect and propose a novel user directions sensing method integrated with massive MIMO orthogonal frequency division multiplexing (OFDM) systems. Specifically, with the beam squint effect, the BS utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards different directions simultaneously. The users feedback the subcarrier frequency with the maximum array gain to the BS. Then, the BS calculates the direction based on the subcarrier frequency feedback. Futhermore, the beam split effect introduced by enlarging the inter-antenna spacing is exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which reduces the time overhead of the conventional sensing significantly. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning

Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance. In this paper, we propose Thinker, a bootstrapping method to remove adversarial effects of confounding features from the observation in an unsupervised way, and thus, it improves RL agents' generalization. Thinker first clusters experience trajectories into several clusters. These trajectories are then bootstrapped by applying a style transfer generator, which translates the trajectories from one cluster's style to another while maintaining the content of the observations. The bootstrapped trajectories are then used for policy learning. Thinker has wide applicability among many RL settings. Experimental results reveal that Thinker leads to better generalization capability in the Procgen benchmark environments compared to base algorithms and several data augmentation techniques.
On the asymptotics of Harish-Chandra modules.

In his study of irreducible, admissible representations of semisimple Lie groups, Harish-Chandra makes extensive use of the asymptotic expansions of matrix coefficients [12], [13]. In particular, he constructs certain embeddings into principal series representations from the asymptotic expansions [14]; these ideas were then extended and refined by Casselman [5]. Both Harish-Chandra and Casselman raised the question whether the asymptotic expansions determine all embeddings into principal series representations. Our main result is a Statement, conjectured by Casselman, that answers the question affirmatively.
Ambidexterity in the Cellphone Industry: An Empirical Study of Asian Firms

Leveraging a hybrid research approach between quantitative and qualitative methods, the present research project intended to investigate 1)in the Chinese cellphone market, the difference in level of organizational ambidexterity exhibited by East Asian and Non-East Asian manufacturers, and 2)whether there exists a positive correlation between cellphone manufacturers’ market performance and organizational ambidexterity. This study drew from He & Wong’s (2004) framework of a dual-dimensioned organizational ambidexterity which includes the Balance Dimension (BD) that describes a balanced effort in organizational exploitation and exploration, and the Combined Dimension (CD) that describes the totality of effort devoted to exploitation and exploration activities. The present study proposes a positive correlation in the Chinese cellphone market between a cellphone manufacturer’s sales growth rate and 1a)BD, 1b)CD, and the simultaneous pursuit of 1c)BD & CD. The study also proposes that East Asian firms would exhibit a higher level of organizational ambidexterity in both 2a)BD and 2b)CD based on my discussion of cultural and institutional factors. The analytical results indicated that East Asian cellphone firms indeed demonstrated a higher level of organizational ambidexterity across both dimensions compared to their Non-East Asian counterparts. Mixed findings were obtained concerning organizational ambidexterity’s impact on organizational performance. The results partially supported that BD ambidexterity has a positive correlation with organizational performance. Contrary to expectations, the findings revealed a negative effect of CD ambidexterity on organizational performance. In addition, no significant relationship was detected between the simultaneous pursuit of BD & CD ambidexterity and organizational performance. Overall, the results support Raisch & Birkinshaw’s (2008) findings that the relationship between organizational ambidexterity and organizational performance is complex. The present study contributes to the literature by providing empirical evidence to the presence of a complex relationship between organizational ambidexterity and performance using the BD & CD framework. Discussion of the findings also offers insights into business practices in the consumer electronics industry. Keywords: organizational ambidexterity, exploration, exploitation, innovation, cellphone, mobile phone, Chinese market, East Asian
On the Subjectivity of Emotions in Software Projects: How Reliable are Pre-Labeled Data Sets for Sentiment Analysis?

Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based communication. These sentiment analysis tools are trained using pre-labeled data sets from different sources, including GitHub and Stack Overflow. In this paper, we investigate if the labels of the statements in the data sets coincide with the perception of potential members of a software project team. Based on an international survey, we compare the median perception of 94 participants with the pre-labeled data sets as well as every single participant's agreement with the predefined labels. Our results point to three remarkable findings: (1) Although the median values coincide with the predefined labels of the data sets in 62.5% of the cases, we observe a huge difference between the single participant's ratings and the labels; (2) there is not a single participant who totally agrees with the predefined labels; and (3) the data set whose labels are based on guidelines performs better than the ad hoc labeled data set.
Sotto Voce: Federated Speech Recognition with Differential Privacy Guarantees

Speech data is expensive to collect, and incredibly sensitive to its sources. It is often the case that organizations independently collect small datasets for their own use, but often these are not performant for the demands of machine learning. Organizations could pool these datasets together and jointly build a strong ASR system; sharing data in the clear, however, comes with tremendous risk, in terms of intellectual property loss as well as loss of privacy of the individuals who exist in the dataset. In this paper, we offer a potential solution for learning an ML model across multiple organizations where we can provide mathematical guarantees limiting privacy loss. We use a Federated Learning approach built on a strong foundation of Differential Privacy techniques. We apply these to a senone classification prototype and demonstrate that the model improves with the addition of private data while still respecting privacy.
Maximizing Fair Content Spread via Edge Suggestion in Social Networks

Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common feature in social network platforms, as an opportunity to achieve an equitable spread of content. In particular, we propose to suggest a subset of potential edges (currently not existing in the network but likely to be accepted) that maximizes content spread while achieving fairness. Instead of re-engineering the existing systems, our proposal builds a fairness wrapper on top of the existing friendship suggestion components. We prove the problem is NP-hard and inapproximable in polynomial time unless P = NP. Therefore, allowing relaxation of the fairness constraint, we propose an algorithm based on LP-relaxation and randomized rounding with fixed approximation ratios on fairness and content spread. We provide multiple optimizations, further improving the performance of our algorithm in practice. Besides, we propose a scalable algorithm that dynamically adds subsets of nodes, chosen via iterative sampling, and solves smaller problems corresponding to these nodes. Besides theoretical analysis, we conduct comprehensive experiments on real and synthetic data sets. Across different settings, our algorithms found solutions with near-zero unfairness while significantly increasing the content spread. Our scalable algorithm could process a graph with half a million nodes on a single machine, reducing the unfairness to around 0.0004 while lifting content spread by 43%.
Investigating the low pass-rate of CCNA1 students at the Central University of Technology

The field of Information Technology (IT) has experienced rapid change over the past few decades. Since the late 90’s, many businesses in the IT industry begun to require certifications for specific technologies. Corporations begun to collaborate with traditional educational institutions so that they can control the quality and content of students that have the desire to learn their networking concepts. The Department of Information Technology (DIT) at Central University of Technology (CUT) adopted the Cisco program and teach students the Cisco curriculum. Cisco curriculum is the most widely used computer networking curriculum and the international standard by which professional competency in this field can be measured. The DIT has been experiencing a decline in student pass-rate and this has raised a lot of concern. Hence, the main aim of this research project was to investigate the low pass-rate of CCNA-1 students at CUT. Due to time constraints, this research examined the delivery method and the assessment method as they might influence student success. The quantitative research design allowed the researcher to collect both qualitative and quantitative data using an online questionnaire from 2015 and 2016 CCNA-1 students from the DIT at CUT, Bloemfontein campus. The questionnaire was administered to 188 students at the end of the second semester. The data was coded and analysed manually using Microsoft Excel. From the results it is very clear that students do not make time to study and they do not visit the library to expand on their knowledge. Some of the students indicated that the content they received was too much and coupled with little contact time they have with the lecturer, exacerbated the situation. 53% of the participants suggested that the delivery method must be