Miracle-dz/UniFAR-FTUs huggingface dataset:
MEDUSA: A Multi-Resolution Machine Learning Congestion Estimation Method for 2D and 3D Global Routing

Routing congestion is one of the many factors that need to be minimized during the physical design phase of large integrated circuits. In this article, we propose a novel congestion estimation method, called MEDUSA, that consists of three parts: (1) a feature extraction and “hyper-image” encoding; (2) a congestion estimation method using a fixed-resolution convolutional neural network model that takes a tile of this hyper-image as input and makes accurate congestion predictions for a small region of the circuit; and (3) a sliding-window method for repeatedly applying this convolutional neural network on a layout, thereby producing higher-resolution congestion maps for arbitrarily large circuits. The proposed congestion estimation approach works with both 2D (collapsed) and 3D global routing. Using both quantitative metrics and qualitative visual inspection, congestion maps produced with MEDUSA show better accuracy than prior estimation techniques. Global routers typically use estimation techniques during their first router iteration and then switch to using actual congestion information extracted from the intermediate router solutions. Experimental results within the same global router infrastructure show a significant impact on quality after the first routing iteration; other estimation techniques result in an average of 22% to 54% higher initial overflow counts. This initial quality improvement carries through to the final global routing solution, with other estimation techniques needing up to 5% more routing iterations and up to 3× more runtime, on average. Compared with other global routers, MEDUSA achieves comparable wire length results and lower total overflow counts (more legal global routing solutions) and is typically faster.
Energy-Efficient Approximate Edge Inference Systems

The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial intelligence based application workloads have led to immense interest in performing inference on energy-constrained edge devices. Approximate computing (a design paradigm that trades off a small degradation in application quality for disproportionate energy savings) is a promising technique to enable energy-efficient inference at the edge. This article introduces the concept of an approximate edge inference system (AxIS) and proposes a systematic methodology to perform joint approximations between different subsystems in a deep neural network (DNN)-based edge inference system, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use a smart camera system that executes various DNN-based image classification and object detection applications to illustrate how the sensor, memory, compute, and communication subsystems can all be approximated synergistically. We demonstrate our proposed methodology using two variants of a smart camera system: (a) CamEdge, where the DNN is executed locally on the edge device, and (b) CamCloud, where the edge device sends the captured image to a remote cloud server that executes the DNN. We have prototyped such an approximate inference system using an Intel Stratix IV GX-based Terasic TR4-230 FPGA development board. Experimental results obtained using six large DNNs and four compact DNNs running image classification applications demonstrate significant energy savings (≈ 1.6× -4.7× for large DNNs and ≈ 1.5× -3.6× for small DNNs), for minimal (<1%) loss in application-level quality. Furthermore, results using four object detection DNNs exhibit energy savings of ≈ 1.5× -5.2× for similar quality loss. Compared to approximating a single subsystem in isolation, AxIS achieves 1.05× -3.25× gains in energy savings for image classification and 1.35× -4.2× gains for object detection on average, for minimal (<1%) application-level quality loss.
COVID-19: Secure Healthcare Internet of Things Networks, Current Trends and Challenges with Future Research Directions

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.
Strategies of Multi-Step-ahead Forecasting for Chaotic Time Series using Autoencoder and LSTM Neural Networks: A Comparative Study

There has been a lot of research on the use of deep neural networks in forecasting time series and chaotic time series data. However, there exist very few works on multi-step ahead forecasting in chaotic time series using deep neural networks. Several strategies that deal with multi-step-ahead forecasting problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, a combination of both the recursive and direct strategies, called DirRec, the Multiple-Input Multiple-Output (MIMO) strategy, and the fifth strategy, called DirMO which combines Direct and MIMO strategies. This paper aims to propose a new deep learning model for chaotic time series forecasting: LSTM-based stacked autoencoder and answer the research question: which strategy for multi-step ahead forecasting using LSTM-based stacked autoencoder yields the best performance for chaotic time series. We evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean-Absolute-Percentage Error (MAPE). The experimental results on synthetic and real-world chaotic time series datasets reveal that MIMO strategy provides the best predictive accuracy for chaotic time series forecasting using LSTM-based stacked autoencoder.
Ecological and Evolutionary Origin of Costus flammulus (Costaceae): A New Species from the Montane Cloud Forests of the Volcanic Cordilleras in Northern Costa Rica

Abstract Costus flammulus is a new herbaceous species endemic to montane cloud forests of the volcanic cordilleras in northern Costa Rica. Costus flammulus has been mistaken for C. wilsonii, but phylogenetic evidence demonstrates that it is closely related to the widespread lowland species C. pulverulentus. Here, we use an integrated framework of species concepts to evaluate whether C. flammulus and C. pulverulentus are distinct species. First, we re-evaluate prior phylogenetic analyses to assess whether C. flammulus bifurcated from or budded off from within C. pulverulentus and whether C. flammulus is monophyletic. We then compare phenotypic traits to determine which diagnostic vegetative and inflorescence traits can be used to identify species in herbarium specimens and examine whether floral traits may confer floral isolation. We compare pollinator assemblages to examine whether pollinator specificity may contribute to reproductive isolation. Finally, we model species distributions and climatic niche overlap to assess ecogeographic isolation. We found that C. flammulus is a monophyletic species phenotypically, ecologically, and geographically distinct from C. pulverulentus and may have speciated as a peripheral isolate at the high elevation range edge of C. pulverulentus. Several lines of evidence, such as C. pulverulentus paraphyly, range size asymmetry, and C. flammulus' nested distribution and vegetative traits, suggest that C. flammulus budded off from a C. pulverulentus-like progenitor species, evolving to tolerate a colder and more seasonal montane environment.
Genomic Relationships of Glycine Remota, a Recently Discovered Perennial Relative of Soybean, within Glycine

Abstract The legume genus, Glycine, which includes the Asian annual cultivated soybean, also includes a group of Australian perennial species comprising the subgenus Glycine. Because the subgenus Glycine represents the tertiary gene pool for one of the world's most important crops, the group has been the target of collection and study for decades, resulting in a steady growth in the number of formally recognized species, from six in the 1970s to over 20 at present, as well as a number of additional informal taxa. These studies have also produced a system of nuclear diploid “genome groups” corresponding to clades in molecular phylogenies. The aptly named G. remota is known only from a single isolated population in the Kimberley region of northwestern Australia and was named only in 2015. The species is unique within Glycine in having unifoliolate leaves; its discoverers hypothesized that G. remota, if diploid, is related to species of the I-genome that are also native to the Kimberley region. We produced low coverage short-read genome sequencing data from an herbarium specimen of G. remota. Genome size estimates from the sequencing data suggests that G. remota is a diploid, while ploidy estimation is inconclusive likely due to the history of whole genome duplication in Glycine. Phylogenomic analyses of genome-wide SNPs, as well as phylogenetic analyses of the low copy nuclear gene (histone H3D), the entire ribosomal RNA cistron, and the internal transcribed spacer all placed the species unequivocally in the diploid I-genome clade. A complete plastome sequence was also generated and its placement with a plastome phylogeny is also consistent with membership in the I-genome.
Promote identification or prevent expansion? The effect of uploader-viewer similarity on viewer inspiration in the context of short video community

Introduction Short video communities have proliferated recently, opening up a new channel for the promotion of online businesses. The main challenge in marketing with short videos is how to inspire consumers. Methods This study employs viewer social identification and self-expansion as its entrance point to to explain how uploader-viewer similarity affects viewers’ short video inspiration. We use empirical investigation method based on the data of 310 short video viewers to test our conceptual model. Results We found that there is a double-edged relationship between uploader-viewer similarity and short video inspiration. Particularly, uploader-viewer similarity enhances perceived social identification while reducing perceived self-expansion. The association between Uploader-viewer similarity and short video inspiration is moderated by viewers’ cognitive demands. Discussion These results provide a new perspective for enterprises to manage the relationship between viewers and media.
Contemporaneous symptom networks and correlates during endocrine therapy among breast cancer patients: A network analysis

Background Endocrine therapy-related symptoms are associated with early discontinuation and quality of life among breast cancer survivors. Although previous studies have examined these symptoms and clinical covariates, little is known about the interactions among different symptoms and correlates. This study aimed to explore the complex relationship of endocrine therapy-related symptoms and to identify the core symptoms among breast cancer patients. Methods This is a secondary data analysis conducted based on a multicenter cross-sectional study of 613 breast cancer patients in China. All participants completed the 19-item Chinese version of the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES). Multivariate linear regression analysis was performed to identify significant factors. A contemporaneous network with 15 frequently occurring symptoms was constructed after controlling for age, payment, use of aromatase inhibitors, and history of surgery. Network comparison tests were used to assess differences in network structure across demographic and treatment characteristics. Results All 613 participants were female, with an average age of 49 years (SD = 9.4). The average duration of endocrine therapy was 3.6 years (SD = 2.3) and the average symptom score was 18.99 (SD = 11.43). Irritability (n = 512, 83.52%) and mood swings (n = 498, 81.24%) were the most prevalent symptoms. Lost interest in sex (mean = 1.95, SD = 1.39) and joint pain (mean = 1.57, SD = 1.18) were the most severe symptoms. The edges in the clusters of emotional symptoms (“irritability-mood swings”), vasomotor symptoms (“hot flashes-cold sweats-night sweats”), vaginal symptoms (“vaginal discharge-vaginal itching”), sexual symptoms (“pain or discomfort with intercourse-lost interest in sex-vaginal dryness”), and neurological symptoms (“headaches-dizziness”) were the thickest in the network. There were no significant differences in network structure (P = 0.088), and global strength (P = 0.330) across treatment types (selective estrogen receptor modulators vs. aromatase inhibitors). Based on an evaluation of the centrality indices, irritability and mood swings appeared to be structurally important nodes after adjusting for the clinical covariates and after performing subgroup comparisons. Conclusion Endocrine therapy-related symptoms are frequently reported issues among breast cancer patients. Our findings demonstrated that developing targeted interventions focused on emotional symptoms may relieve the overall symptom burden for breast cancer patients during endocrine therapy.
Can Quasigeostrophic Turbulence Be Modeled Stochastically

Abstract Numerically generated data of quasigeostrophic turbulence in an equilibrated shear flow are analyzed to determine the extent to which they can be modeled by a Markov model. The time lagged covariances are collected into a matrix, Cτ, and are substituted into the fluctuation-dissipation relation for a first-order Markov model with white noise forcing CτC0−1 = exp (Aτ),to determine whether the relation is satisfied for a single dynamic operator A. The dynamic operator obtained by inverting the relation was found to depend on time lag. In particular, for small time lags (τ < 1 day), the eigenvectors and imaginary eigenvalues were independent of time lag, while the damping rates increased linearly with time lag. It is shown analytically that precisely this discrepancy occurs when the relation is applied to data generated by a red noise Markov model using a time lag that is small compared to the decorrelation time of the noise. Although a fourth-order Markov model with white noise can more accurately .
Comprehensive analysis of the role of SFXN family in breast cancer

Abstract The sideroflexin (SFXN) family is a group of mitochondrial membrane proteins. Although the function of the SFXN family in mitochondria has been widely recognized, the expression levels, role, and prognostic value of this family in breast cancer (BC) have not been clearly articulated and systematically analysed. In our research, SFXN1 and SFXN2 were significantly upregulated in BC versus normal samples based on Gene Expression Profiling Interactive Analysis 2 and the Human Protein Atlas databases. We found that high SFXN1 expression was significantly related to poor prognosis in BC patients and that high SFXN2 expression was significantly associated with good prognosis in BC patients. Gene Ontology analysis of the SFXN family was performed based on the STRING database to explore the potential functions of this family, including biological processes, cellular components, and molecular functions. Based on the MethSurv database, we found that two SFXN1 CpG sites (5′-UTR-S_Shelf-cg06573254 and TSS200-Island-cg17647431), two SFXN2 CpG sites (3′-UTR-Open_Sea-cg04774043 and Body-Open_Sea-cg18994254), one SFXN3 CpG site (Body-S_Shelf-cg17858697), and nine SFXN5 CpG sites (1stExon;5′-UTR-Island-cg03856450, Body-Open_Sea-cg04016113, Body-Open_Sea-cg04197631, Body-Open_Sea-cg07558704, Body-Open_Sea-cg08383863, Body-Open_Sea-cg10040131, Body-Open_Sea-cg10588340, Body-Open_Sea-cg17046766, and Body-Open_Sea-cg22830638) were significantly related to the prognosis of BC patients. According to the ENCORI database, four negative regulatory miRNAs for SFXN1 (hsa-miR-22-3p, hsa-miR-140-5p, hsa-miR-532-5p, and hsa-miR-582-3p) and four negative regulatory miRNAs for SFXN2 (hsa-miR-9-5p, hsa-miR-34a-5p, hsa-miR-532-5p, and hsa-miR-885-5p) were related to poor prognosis for BC patients. This study suggests that SFXN1 and SFXN2 are valuable biomarkers and treatment targets for patients with BC.
BL-GARCH models with elliptical distributed innovations

In this work, we discuss the class of bilinear GARCH (BL-GARCH) models that are capable of capturing simultaneously two key properties of non-linear time series: volatility clustering and leverage effects. It has often been observed that the marginal distributions of such time series have heavy tails; thus we examine the BL-GARCH model in a general setting under some non-normal distributions. We investigate some probabilistic properties of this model and we conduct a Monte Carlo experiment to evaluate the small-sample performance of the maximum likelihood estimation (MLE) methodology for various models. Finally, within-sample estimation properties were studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects. The main results suggest that the Student-t BL-GARCH seems highly appropriate to describe the S&P 500 daily returns.
Determinants of adoption of electronic payment by small and medium-sized enterprises (SMEs) in Cameroon

This paper aims to identify the determinants of the adoption of electronic payment in SMEs in Cameroon. It considers data collected from 117 SMEs. The methodology involves principal component analysis and ordinal logistic regression. The results obtained are at least twofold. First, the leading electronic payment tools adopted by these companies are mobile money, card and Internet payments. Second, their choice can be explained on the one hand by the characteristics of these payment tools such as convenience and cost of use, and on the other hand by contingency factors such as integration level and ICT’s mastery level of the manager. Accordingly, companies should be encouraged to use electronic payment tools based on the driving role they could play in providing basic infrastructure and guarantee financially secure transactions between economic agents. The original contribution of this paper is at least twofold. Firstly, methodologically, this study uses a combination of qualitative and quantitative methods and ordinal logistic regression. Secondly, while previous studies were limited to mobile money adoptions, this study further integrated online and card payments in the analyses.
Becoming self-sufficient: a longitudinal person-centered analysis of financial identity and adult status during emerging adulthood

ABSTRACT Because finances play an important part in managing the demands of adult social roles, we investigate the formation of financial identity and its association with perceived adult status. Drawing from Marcia’s (1966) operationalization of four identity statuses (i.e., foreclosed, achieved, diffused, moratorium) and adapted to the financial domain, we performed Latent Transition Analysis (LTA) using longitudinal data gathered from a cohort of college students in the United States at four time points (N = 672) covering an eight-year period (2008–2016). A key study finding is empirical directional evidence of financial identity as a predictor of adult status. In addition, findings show that, over time, individuals commit more strongly to a certain financial identity, which may in turn factor significantly in the process of achieving adult self-sufficiency. Finally, we distinguish three distinct financial identity profiles based on the way that participants made decisions related to financial matters during the transition to adulthood.
A high-efficiency Wallace tree based multi-trit multiplier in CNTFET technology

ABSTRACT In this paper, a multi-digit ternary (multi-trit) Wallace tree-based multiplier in CNTFET technology is presented. In this work, a modified 1-trit multiplier is applied to reduce the number of the CNTFETs where it uses unary operators to estimate the product and the carry. An optimised architecture of a multi-digit ternary adder is also presented. It is applied at the last stage of the multiplier to reduce the overall propagation delay. Hence, higher speed is achieved spending a few more number of CNTFETs. The proposed design is simulated in HSPICE using Stanford University’s 32 nm CNFET technology model. Simulation results show significant improvements in power delay products and hardware utilisation. The proposed 3-trit multiplier applies 750 number of CNTFETs where the PDP is 75 fJ. This represents an approximately 20% improvement in the PDP while more than 100 CNTFETs are saved. For further evaluation, the proposed structure is also extended to 6, 9 and 12-trit multipliers.
SoK: Metadata-Protecting Communication Systems

Protecting metadata of communications has been an area of active research since the dining cryptographers problem was introduced by David Chaum in 1988. The Snowden revelations from 2013 resparked research in this direction. Consequently over the last decade we have witnessed a flurry of novel systems designed to protect metadata of users' communications online. However, such systems leverage different assumptions and design choices to achieve their goal; resulting in a scattered view of the desirable properties, potential vulnerabilities, and limitations of existing metadata-protecting communication systems (MPCS). In this work we survey 31 systems targeting metadata-protected communications, and present a unified view of the current state of affairs. We provide two different taxonomies for existing MPCS, first into four different categories by the precise type of metadata protections they offer, and next into six families based on the core techniques that underlie them. By contrasting these systems we identify potential vulnerabilities, as well as subtle privacy implications of design choices of existing MPCS. Furthermore, we identify promising avenues for future research for MPCS, and desirable properties that merit more attention.