Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Relationship between Community and Design Smells in Open-Source Software Projects: An Empirical Study

Background: Software smells reflect the sub-optimal patterns in the software. In a similar way, community smells consider the sub-optimal patterns in the organizational and social structures of software teams. Related work performed empirical studies to identify the relationship between community smells and software smells at the architecture and code levels. However, how community smells relate with design smells is still unknown. Aims: In this paper, we empirically investigate the relationship between community smells and design smells during the evolution of software projects. Method: We apply three statistical methods: correlation, trend, and information gain analysis to empirically examine the relationship between community and design smells in 100 releases of 10 large-scale Apache open-source software projects. Results: Our results reveal that the relationship between community and design smells varies across the analyzed projects. We find significant correlations and trend similarities for one type of community smell (when developers work in isolation without peer communication—Missing Links) with design smells in most of the analyzed projects. Furthermore, the results of our statistical model disclose that community smells are more relevant for design smells compared to other community-related factors. Conclusion: Our results find that the relationship of community smells (in particular, the Missing Links smell) exists with design smells. Based on our findings, we discuss specific community smell refactoring techniques that should be done together when refactoring design smells so that the problems associated with the social and technical (design) aspects of the projects can be managed concurrently.
iCast: dynamic information-centric cross-layer multicast for wireless edge network

The native multicast support in Named Data Networking (NDN) is an attractive feature, as multicast content delivery can reduce the redundant traffic and improve the network performance, especially in the wireless edge network. With the awareness of content, NDN routers automatically aggregate the same requests from different end hosts and establish network-layer multicast. However, the current link-layer multicast based on host-centric MAC address management is inflexible. Consequently, supporting NDN dynamic multicast with the current link-layer architecture remains a challenge. In this paper, we propose iCast, short for dynamic information-centric multicast, to enable dynamic multicast in the link layer. Our work has three major contributions. First, iCast collaborates NDN native multicast with the host-centric link layer and meanwhile keeps the host-centric property of the current link layer. Second, iCast achieves per-packet dynamic multicast in the link layer and we further propose a hash-based iCast variant for dynamic connection. Third, iCast is implemented in real testbed and the evaluation results show that iCast reduces up to 59.53% traffic compared with vanilla NDN. iCast bridges the gap between NDN multicast and the host-centric link-layer multicast.
Scan Chain Clustering and Optimization with Constrained Clustering and Reinforcement Learning

Scan chains are used in design for test by providing controllability and observability at each register. Scan optimization is run during physical design after placement where scannable elements are re-ordered along the chain to reduce total wirelength (and power). In this paper, we present a machine learning based technique that leverages constrained clustering and reinforcement learning to obtain a wirelength efficient scan chain solution. Novel techniques like next-min sorted assignment, clustered assignment, node collapsing, partitioned Q-Learning and in-context start-end node determination are introduced to enable improved wire length while honoring design-for-test constraints. The proposed method is shown to provide up to 24% scan wirelength reduction over a traditional algorithmic optimization technique across 188 moderately sized blocks from an industrial 7nm design.
Graph Representation Learning for Gate Arrival Time Prediction

An accurate estimate of the timing profile at different stages of the physical design flow allows for pre-emptive changes to the circuit, significantly reducing the design time and effort. In this work, a graph based deep regression model is utilized to predict the gate level arrival time of the timing paths of a circuit. Three scenarios for post routing prediction are considered: prediction after completing floorplanning, prediction after completing placement, and prediction after completing clock tree synthesis (CTS). A commercial static timing analysis (STA) tool is utilized to determine the mean absolute percentage error (MAPE) and the mean absolute error (MAE) for each scenario. Results obtained across all models trained on the complete dataset indicate that the proposed methodology outperforms the baseline errors produced by the commercial physical design tools with an average improvement of 61.58% in the MAPE score when predicting the post-routing arrival time after completing floorplanning and 13.53% improvement when predicting the post-routing arrival time after completing placement. Additional prediction scenarios are analyzed, where the complete dataset is further sub-divided based on the size of the circuits, which leads to an average improvement of 34.83% in the MAPE score as compared to the commercial tool for post-floorplanning prediction of the post-routing arrival time and 22.71% improvement for post-placement prediction of the post-routing arrival time.
A Tale of Two Tasks: Automated Issue Priority Prediction with Deep Multi-task Learning

Background. Issues are prevalent, and identifying the correct priority of the reported issues is crucial to reduce the maintenance effort and ensure higher software quality. There are several approaches for the automatic priority prediction, yet they do not fully utilize the related information that might influence the priority assignment. Our observation reveals that there are noticeable correlations between an issue’s priority and its category, e.g., an issue of bug category tends to be assigned with higher priority than an issue of document category. This correlation motivates us to employ multi-task learning to share the knowledge about issue’s category prediction and facilitating priority prediction. Aims. This paper aims at providing an automatic approach for effective issue’s priority prediction, to reduce the burden of the project members and better manage the issues. Method. We propose issue priority prediction approach PRIMA with deep multi-task learning, which takes the issue category prediction as another task to facilitate the information sharing and learning. It consists of three main phases: 1) data preparation and augmentation phase, which allows data sharing beyond single task learning; 2) model construction phase, which designs shared layers to encode the semantics of textual descriptions, and task-specific layers to model two tasks in parallel; it also includes the indicative attributes to better capture an issue’s inherent meaning; 3) model training phase, which enables eavesdropping by shared loss function between two tasks. Results. Evaluations with four large-scale open-source projects show that PRIMA outperforms commonly-used and state-of-the-art baselines, with 32% -55% higher precision, and 28% - 56% higher recall. Compared with single task learning, the performance improvement reaches 18% in precision and 19% in recall. Results from our user study further prove its potential practical value. Conclusions. The proposed approach provides a novel and effective way for issue priority prediction, and sheds light on jointly exploring other issue-management tasks.
Analysis of Evolutionary Diversity Optimization for Permutation Problems

Generating diverse populations of high-quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems: the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and the Quadratic Assignment Problem (QAP). It includes an analysis of the worst-case performance of a simple mutation-only evolutionary algorithm with different mutation operators, using an established diversity measure. Theoretical results show that many mutation operators for these problems guarantee convergence to maximally diverse populations of sufficiently small size within cubic to quartic expected runtime. On the other hand, the results regarding QAP suggest that strong mutations give poor worst-case performance, as mutation strength contributes exponentially to the expected runtime. Additionally, experiments are carried out on QAPLIB and synthetic instances in unconstrained and constrained settings, and reveal much more optimistic practical performances while corroborating the theoretical findings regarding mutation strength. These results should serve as a baseline for future studies.
Autoencoder-Based Data Sampling for Machine Learning-Based Lithography Hotspot Detection

Technology scaling has increased the complexity of integrated circuit design. It has also led to more challenges in the field of Design for Manufacturing (DFM). One of these challenges is lithography hotspot detection. Hotspots (HS) are design patterns that negatively affect the output yield. Identifying these patterns early in the design phase is crucial for high yield fabrication. Machine Learning-based (ML) hotspot detection techniques are promising since they have shown superior results to other methods such as pattern matching. Training ML models is a challenging task due three main reasons. First, industrial training designs contain millions of unique patterns. It is impractical to train models using this large number of patterns due to limited computational and memory resources. Second, the HS detection problem has an imbalanced nature; datasets typically have a limited number of HS and a large number of non-hotspots. Lastly, hotspot and non-hotspot patterns can have very similar geometries causing models to be susceptible to high false positive rates. Due to these reasons, the use of data sampling techniques is needed to choose the best representative dataset for training. In this paper, a dataset sampling technique based on autoencoders is introduced. The autoencoders are used to identify latent data features that can reconstruct the input patterns. These features are used to group the patterns using Density-based spatial clustering of applications with noise (DBSCAN). Then, the clustered patterns are sampled to reduce the training set size. Experiments on the ICCAD2019 dataset show that the proposed data sampling approach can reduce the dataset size while maintaining the levels of recall and precision that were obtained using the full dataset.
Tracking measles infection through non‐linear state space models

Summary. Estimating the burden of infectious disease is complicated by the general tendency for underreporting of cases. When the reporting rate is unknown, conventional methods have relied on accounting methods that do not make explicit use of surveillance data or the temporal dynamics of transmission and infection. State space models are a framework for various methods that allow dynamic models to be fitted with partially or imperfectly observed surveillance data. State space models are an appealing approach to burden estimation as they combine expert knowledge in the form of an underlying dynamic model but make explicit use of surveillance data to estimate parameter values, to predict unobserved elements of the model and to provide standard errors for estimates.
Expanding the role of ultrasound in the diagnosis of thyroid carcinoma and the wish for adjunctive diagnostic tools

Ultrasound is the principal first line diagnostic step in the evaluation of thyroid nodules. Thyroid lesions are thereby characterized by suspi-cious ultrasound features as solidity, calcifications, margin, echogenity, and shape. 1,2 These scores are summed to give an indication of the chance for malignancy (www.tiradscalculator.com). This TIRADS reporting system is helpful to discriminate lesions that require further fine-needle aspiration cytology (FNAC). FNAC with the Bethesda classification system, is namely still the gold standard to determine whether a thyroid lesion is expected to be malignant and requires surgery to reach a definite diagnosis. Today, the TIRADS guided workup of thyroid nodules has gained wide acceptance as a sensitive predictor system for potential thyroid malignancy. 3,4 The incidence of thyroid nodules is rapidly rising, primarily due to the increased application of thyroid ultrasound, and therefore careful risk stratification is important to prevent overdiagnosis and unnecessary treatment. The TIRADS-based ultrasound findings are currently explicitly used to avoid FNAC in benign appearing lesions. Thus, making TIRADS an exclusion system to rule out malignancy. Probably, in the future, the TIRADS-based ultrasound findings may develop to such an accurate esti-mate of malignancy, which it may either by its ultrasound characteristics or in combination with other diagnostic features, aid the decision-making on surgery thereby skipping invasive FNAC. However, this is just the future perspective because until today the diagnostic yield of thyroid ultrasound is restricted to determine the need for an additional FNAC. It is a well-known clinical observation that thyroid malignancies are often hard and firm on palpation with physical examination. However,
Quantifying the benefits of dynamic line ratings in congestion management for improved wind integration in smart grids

This thesis develops a congestion management scheme using a systematic application of Dynamic Line Ratings (DLR) for increased wind integration in smart grids. It proposes a mathematical formulation to incorporate the uncertainty associated with DLR into scheduling and planning decisions. Using this novel framework, the thesis quantifies the potential benefit of dynamic line ratings in terms of congestion management and wind integration under a variety of system operating conditions. Lastly, the thesis proposes a practical approach to determine the thermal overload risk and associated uncertainty based on IEEE Standard 738-2012. The effect of intermittency impedes large scale wind integration due to the associated uncertainty in power output. If actual wind power output exceeds expected output, it may lead to network congestion which is detrimental to the network and the competitiveness of participants in the electricity market. However, most existing congestion management schemes result in wind curtailment as a result of generation management and operate the network in a sub optimal configuration which does not fully utilise renewable generation sources. Research has shown that real time line thermal limits are considerably different to design limits of lines. While some independent system operators (ISO) attempt to exploit this by the use of multiple seasonal ratings and overload lines during emergencies, these methods still underestimate the true thermal capacity of the line. With the advent of sophisticated monitoring and control in the smart grid, dynamic line capacity can be determined more accurately based on real time ambient conditions. This would be a significant improvement over existing ”rule of thumb” type used by utilities. While DLR shows promise in alleviating short term congestion, there is a necessity to systematically determine the effect of DLR on the overall system scheduling and planning and to quantify the benefit in terms of congestion reduction and improved wind integration. The major challenges of modelling dynamic line rating in the optimal dispatch problem involves including uncertain
The Genomic Landscapes of Desert Birds Form over Multiple Time Scales

Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, which could indicate that multiple spatial models better explain divergence in different portions of the genome. We test whether alternative geographic predictors of intraspecific differentiation vary across the genome in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we characterized the genomic landscapes across species and modeled five predictors that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across the ten species, influenced by varying levels of population structuring and admixture between deserts. General dissimilarity matrix modeling indicated that the best-fit models differed from the whole genome and partitions along the genome. Almost all of the historical and contemporary mechanisms were important in explaining genetic distance, but particularly historical and contemporary environment, while contemporary abundance, position of the barrier to gene flow, and distance explained relatively less. Individual species have significantly different patterns of genomic variation. These results illustrate that the genomic landscape of differentiation was influenced by alternative geographic factors operating on different portions of the genome.
Altered population activity and local tuning heterogeneity in auditory cortex of Cacna2d3-deficient mice

Abstract The α2δ3 auxiliary subunit of voltage-activated calcium channels is required for normal synaptic transmission and precise temporal processing of sounds in the auditory brainstem. In mice its loss additionally leads to an inability to distinguish amplitude-modulated tones. Furthermore, loss of function of α2δ3 has been associated with autism spectrum disorder in humans. To investigate possible alterations of network activity in the higher-order auditory system in α2δ3 knockout mice, we analyzed neuronal activity patterns and topography of frequency tuning within networks of the auditory cortex (AC) using two-photon Ca2+ imaging. Compared to wild-type mice we found distinct subfield-specific alterations in the primary auditory cortex, expressed in overall lower correlations between the network activity patterns in response to different sounds as well as lower reliability of these patterns upon repetitions of the same sound. Higher AC subfields did not display these alterations but showed a higher amount of well-tuned neurons along with lower local heterogeneity of the neurons’ frequency tuning. Our results provide new insight into AC network activity alterations in an autism spectrum disorder-associated mouse model.
Reactivation of Epstein-Barr Virus from Latency Involves Increased RNA Polymerase Activity at CTCF Binding Sites on the Viral Genome

The ability of EBV to switch between latent and lytic infection is key to its long-term persistence in memory B cells, and its ability to persist in proliferating cells is strongly linked to oncogenesis. During latency, most viral genes are epigenetically silenced, and the virus must overcome this repression to reactivate lytic replication. ABSTRACT The ability of Epstein-Barr virus (EBV) to switch between latent and lytic infection is key to its long-term persistence, yet the molecular mechanisms behind this switch remain unclear. To investigate transcriptional events during the latent-to-lytic switch, we utilized Precision nuclear Run On followed by deep Sequencing (PRO-Seq) to map cellular RNA polymerase (Pol) activity to single-nucleotide resolution on the host and EBV genome in three different models of EBV latency and reactivation. In latently infected Mutu-I Burkitt lymphoma (BL) cells, Pol activity was enriched at the Qp promoter, the EBER region, and the BHLF1/LF3 transcripts. Upon reactivation with phorbol ester and sodium butyrate, early-phase Pol activity occurred bidirectionally at CTCF sites within the LMP-2A, EBER-1, and RPMS1 loci. PRO-Seq analysis of Akata cells reactivated from latency with anti-IgG and a lymphoblastoid cell line (LCL) reactivated with small molecule C60 showed a similar pattern of early bidirectional transcription initiating around CTCF binding sites, although the specific CTCF sites and viral genes were different for each latency model. The functional importance of CTCF binding, transcription, and reactivation was confirmed using an EBV mutant lacking the LMP-2A CTCF binding site. This virus was unable to reactivate and had disrupted Pol activity at multiple CTCF binding sites relative to the wild-type (WT) virus. Overall, these data suggest that CTCF regulates the viral early transcripts during reactivation from latency. These activities likely help maintain the accessibility of the viral genome to initiate productive replication. IMPORTANCE The ability of EBV to switch between latent and lytic infection is key to its long-term persistence in memory B cells, and its ability to persist in proliferating cells is strongly linked to oncogenesis. During latency, most viral genes are epigenetically silenced, and the virus must overcome this repression to reactivate lytic replication. Reactivation occurs once the immediate early (IE) EBV lytic genes are expressed. However, the molecular mechanisms behind the switch from the latent transcriptional program to begin transcription of the IE genes remain unknown. In this study, we mapped RNA Pol positioning and activity during latency and reactivation. Unexpectedly, Pol activity accumulated at distinct regions characteristic of transcription initiation on the EBV genome previously shown to be associated with CTCF. We propose that CTCF binding at these regions retains Pol to maintain a stable latent chromosome conformation and a rapid response to various reactivation signals.
Learning from adaptive neural network output feedback control of uncertain ocean surface ship dynamics

This paper studies the problem of learning from adaptive neural network (NN) output feedback control of ocean surface ship without velocity measurements in uncertain dynamical environments. When only ship position and heading measurements are available for identification and control, using a high‐gain observer to estimate the unmeasurable velocities, we propose stable adaptive output feedback NN tracking control. Partial persistent excitation condition of some internal signals in the closed‐loop system is satisfied during tracking control to a recurrent reference trajectory. Under the persistent excitation condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the uncertain ship dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, a novel NN learning control method exploiting the learned knowledge without readapting to the unknown ship dynamics is proposed to achieve closed‐loop stability and the improvement of the control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.