Miracle-dz/UniFAR-FTUs huggingface dataset:
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. This work addresses this challenge by proposing a graph attention network method with customization, called Net2, to estimate individual net length before cell placement. Its accuracy-oriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than 1000× faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics.
Does Attentional Processes Modulate Bimodal Interference in V-EAR Synaesthesia?

Even though the prevalence of synaesthetic-like ‘Visually-Evoked Auditory Responses’ (V-EAR) in the general population is probably immense by all standards, it is hardly ever mentioned in the literature on general synaesthetic phenomena. We decided to try to reproduce earlier findings that about 1/5 experience motion-conjured sound percepts. We did so with a good margin (27% of our participants claimed to ‘hear’ motion). We were also interested in whether these V-EAR experiencers would show of the enhanced visual sequence discrimination capabilities reported in two earlier studies. This finding was not successfully replicated. Neither could we manage to find any intuitively reasonable connections between V-EAR, attention processes and destructive interference between incongruently sequenced bimodal stimuli. Instead, we found evidence of constructive interference between incongruently sequenced bimodal stimuli, something that might be due to the ‘auditory driving’ effect. We discuss these findings within the theoretical frameworks as proposed by earlier research on ‘classic’ synaesthesia. (Less)
The driving factors of continuance online shopping : Gender differences in behaviour among students in Saudi Arabia

This study proposes a revised technology acceptance model that integrates expectation confirmation theory to measure student gender differences with regard to continuance online shopping intentions in Saudi Arabia. The 234-respondent sample consists of 61.5% women and 38.5% men. A structural equation model confirms model fit. Perceived usefulness, enjoyment, and subjective norms are determinants of online shopping continuance in Saudi Arabia. Both male and female groups are equivalent. The structural weights are also largely equivalent, but the regression paths from subjective norms to enjoyment and subjective norms to continuance intention (in the men sample) and perceived usefulness to continuous intention (in the women sample) are not supported. The model was operationally generalized across the whole of Saudi Arabia. This research moves beyond online shopping intentions and includes factors affecting online shopping continuance. The research model explains 71% of the intention to continue shopping online. This research suggests that online strategies cannot ignore either the direct or indirect gender differences on continuance intentions in Saudi Arabia. The model can be generalized across Saudi Arabia.
Machine learning for risk analysis of Urinary Tract Infection in people with dementia

The Urinary Tract Infections (UTIs) are one of the top reasons for unplanned hospital admissions in people with dementia, and if detected early, they can be timely treated. However, the standard UTI diagnosis tests, e.g. urine tests, will be only taken if the patients are clinically suspected of having UTIs. This causes a delay in diagnosis and treatment of the conditions and in some cases like people with dementia, the symptoms can be difficult to observe. Delay in detection and treatment of dementia is one of the key reasons for unplanned hospital admissions in people with dementia. To address these issues, we have developed a technology-assisted monitoring system, which is a Class 1 medical device. The system uses off-the-shelf and low-cost in-home sensory devices to monitor environmental and physiological data of people with dementia within their own homes. We have designed a machine learning model to use the data and provide risk analysis for UTIs. We use a semi-supervised learning model which leverage the environmental data, i.e. the data collected from the motion sensors, smart plugs and network-connected body temperature monitoring devices in the home, to detect patterns that can show the risk of UTIs. Since the data is noisy and partially labelled, we combine the neural networks and probabilistic neural networks to train an auto-encoder, which is to extract the general representation of the data. We will demonstrate our smart home management by videos/online, and show how our model can pick up the UTI related patterns.
Human Trust Modeling for Bias Mitigation in Artificial Intelligence

Human-in-the-loop model-building processes are increasingly popular as they incorporate human intuition and not easily externalized domain knowledge. However, we argue that the inclusion of the human, and in particular direct model manipulations, result in a high risk of creating biased models and results. We present a new approach of “Human Trust Modeling” that lets systems model the users’ intentions and deduce whether they have understood the underlying modeling processes and act coherently. Using this trust model, systems can enable or disable and encourage or discourage interactions to mitigate bias.
Nose to Glass: Looking In to Get Beyond

Brought into the public discourse through investigative work by journalists and scholars, awareness of algorithmic harms is at an all-time high. An increasing amount of research has been conducted under the banner of enhancing responsible artificial intelligence (AI), with the goal of addressing, alleviating, and eventually mitigating the harms brought on by the roll out of algorithmic systems. Nonetheless, implementation of such tools remains low. Given this gap, this paper offers a modest proposal: that the field, particularly researchers concerned with responsible research and innovation, may stand to gain from supporting and prioritising more ethnographic work. This embedded work can flesh out implementation frictions and reveal organisational and institutional norms that existing work on responsible artificial intelligence AI has not yet been able to offer. In turn, this can contribute to more insights about the anticipation of risks and mitigation of harm. This paper reviews similar empirical work typically found elsewhere, commonly in science and technology studies and safety science research, and lays out challenges of this form of inquiry.
Natural versus Recombinant Viral Antigens in SARS-CoV-2 Serology: Challenges in Optimizing Laboratory Diagnosis of COVID-19

OBJECTIVES: COVID-19 is a public health emergency of international concern whose detection in recovered asymptomatic patients is dependent on accurate diagnosis as it enables the estimation of the susceptibility of the population to the infection. This demand has resulted in the development of several commercial assays employing recombinant proteins, but the results of these assays are not reliable as they do not involve comparison with natural viral antigens. We independently used the SARS-CoV-2 whole viral antigen (WVA) and recombinant nucleocapsid protein (rNP) to develop in-house ELISAs for IgG detection; the results of these ELISAs were then compared to obtain reliable results. METHODS: WVA and rNP ELISAs were performed on COVID-19 negative sera from patients before the pandemic in Brazil, and on RT-qPCR-positive or SARS-CoV-2-IgG against rNP and IgG against WVA-positive samples from recently infected patients in Sao Paulo, Brazil. RESULTS: Both ELISAs detected a large fraction of infected patients but exhibited certain drawbacks. Higher signals and lower numbers of false-negatives were observed in rNP ELISA; however, a higher fraction of false-positives was observed in control groups. A high number of false-negatives was observed with WVA ELISA. Correlating the results of rNP and WVA ELISAs resulted in improved performance for COVID-19 diagnosis. CONCLUSION: The choice of antigen is an important aspect in optimizing the laboratory diagnosis of COVID-19. The use of rNP ELISA for the detection of anti-SARS-CoV-2 IgG antibodies seems promising, but comparison of the results with those of WVA ELISA is crucial for accurate test development prior to commercialization. IgG serology using several assays, and with the spectral patterns of SARS-CoV-2, resulted in confusing information that must be clarified before the establishment of diagnostic serology criteria.
Teledyne’s high performance infrared detectors for Space missions

Teledyne is one of the world’s leaders in the provision of infrared sensors for Earth science, planetary science, and astronomy. This paper presents Teledyne’s focal plane array technologies that provide high performance for space missions, with sections on substrate-removed HgCdTe (that provides simultaneous visible / IR detection) and fully depleted HgCdTe (that enables detectors to be operated at significantly increased temperatures). We give examples of the image sensors provided for Euclid, WFIRST, AVIRIS-NG, ECOSTRESS, and NEOCam.
Title Tyrosine phosphorylation of SGEF regulates RhoG activity andcell migration

SGEF and Ephexin4 are members of the Ephexin subfamily of RhoGEFs that specifically activate the small GTPase RhoG. It is reported that Ephexin1 and Ephexin5, two well-characterized Ephexin subfamily RhoGEFs, are tyrosine-phosphorylated by Src, and that their phosphorylation affect their activities and functions. In this study, we show that SGEF, but not Ephexin4, is tyrosine-phosphorylated by Src. Tyrosine phosphorylation of SGEF suppresses its interaction with RhoG, the elevation of RhoG activity, and SGEF-mediated promotion of cell migration. We identified tyrosine 530 (Y530), which is located within the Dbl homology domain, as a major phosphorylation site of SGEF by Src, and Y530F mutation blocked the inhibitory effect of Src on SGEF. Taken together, these results suggest that the activity of SGEF is negatively regulated by tyrosine phosphorylation of the DH domain.
Predicting Growth in Reading Ability. from Children's Exposure to Print

A longitudinal study of growth in reading ability employed two new indicators of print exposure that use a checklist-with-foils logic and that have very brief administration times. Subjects, 52 boys and 46 girls from 4 different fourthand fifth-grade classes in a religiously-affiliated private school, completed the Title Recognition Test (TRT) and the Author Recognition Test (ART) (in which they identified titles and authors of actual children's books interspersed with false titles). Subjects' scores on standardized reading tests taken during their third and fifth grades were also analyzed. Results indicated that individual differences in third to fifth grade growth in reading were significantly related to subjects' scores on the TRT and the ART. Findings suggest that the extent to which individuals engage in literacy activities is a significant contributor to developed reading ability. Further studies of the cognitive consequences of literacy could be facilitated by the use of the easily administered indicators of print exposure examined in this study. (Three tables of data are included; two appendixes containing the author and title recognition test items and the percentage recognition for each item, and 39 references are attached.) (RS) **W********R****************V****************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ***A*Rw**A*****ft*******tnn****tt**********w***************************** Predicting Growth in Reading Ability. from Children's Exposure to Print
A Simple and Flexible Dynamic Approach to Foreign Direct Investment Growth: The Canada-United States Relationship in the Context of Free Trade

In the post-war period the world economy has seen the rise and expansion of regional trading blocs and regional economic integration.1 The prime example has been the evolution of the European Union since the early 1950s; also significant has been the creation of the Asia-Pacific Economic Cooperation Area in 1989, and development of the North American free trade area, dating also from 1989. This paper will focus on the Canada-United States relationship. While the intentions of the partners to a free trade agreement are clear in the case of trade, the effect on foreign direct investment (FDI) is ambiguous. The question this paper addresses is that of how the North American free trade agreements have affected US foreign investment behaviour in Canada.
Semi-supervised learning: A role for similarity in generalization-based learning of relational categories

Research on semi-supervised category learning has been sparse despite its representativeness of naturalistic category learning and potential applications. Most of the semisupervised literature to date has focused on establishing the phenomenon. These efforts have produced mixed results and have explored a relatively limited set of learning circumstances. In the current work, we contribute a novel investigation of semi-supervised learning by extending the paradigm to relational category learning and evaluating the role that item similarity plays in the effectiveness of unsupervised learning opportunities. Our results show firstever evidence of semi-supervised learning in the induction of relational categories and, further, that the similarity between supervised and unsupervised examples critically dictates whether benefits of unsupervised exposures accrue. We conclude with implications and future directions.
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have attracted considerable research interest recently. Especially when it comes to the realm of Internet of Things, the UAVs with Internet connectivity are one of the main demands. Furthermore, the energy constraint i.e. battery limit is a bottle-neck of the UAVs that can limit their applications. We try to address and solve the energy problem. Therefore, a path planning method for a cellular-connected UAV is proposed that will enable the UAV to plan its path in an area much larger than its battery range by getting recharged in certain positions equipped with power stations (PSs). In addition to the energy constraint, there are also no-fly zones; for example, due to Air to Air (A2A) and Air to Ground (A2G) interference or for lack of necessary connectivity that impose extra constraints in the trajectory optimization of the UAV. No-fly zones determine the infeasible areas that should be avoided. We have used a reinforcement learning (RL) hierarchically to extend typical short-range path planners to consider battery recharge and solve the problem of UAVs in long missions. The problem is simulated for the UAV that flies over a large area, and Q-learning algorithm could enable the UAV to find the optimal path and recharge policy.
Eigenvalue-corrected Natural Gradient Based on a New Approximation

Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers. A recently proposed method, Eigenvalue-corrected Kronecker Factorization (EKFAC) (George et al., 2018), proposes an interpretation of viewing natural gradient update as a diagonal method, and corrects the inaccurate re-scaling factor in the Kronecker-factored eigenbasis. Gao et al. (2020) considers a new approximation to the natural gradient, which approximates the Fisher information matrix (FIM) to a constant multiplied by the Kronecker product of two matrices and keeps the trace equal before and after the approximation. In this work, we combine the ideas of these two methods and propose Trace-restricted Eigenvalue-corrected Kronecker Factorization (TEKFAC). The proposed method not only corrects the inexact re-scaling factor under the Kronecker-factored eigenbasis, but also considers the new approximation method and the effective damping technique proposed in Gao et al. (2020). We also discuss the differences and relationships among the Kronecker-factored approximations. Empirically, our method outperforms SGD with momentum, Adam, EKFAC and TKFAC on several DNNs.
Deep Extragalactic VIsible Legacy Survey (DEVILS): SED fitting in the D10-COSMOS field and the evolution of the stellar mass function and SFR–M⋆ relation

We present catalogues of stellar masses, star formation rates, and ancillary stellar population parameters for galaxies spanning $0 2.6$, we see evidence of a bend in the relation at low redshifts ($z<0.45$). This suggests evolution in both the normalisation and shape of the SFR-$M_\star$ relation since cosmic noon. It is significant that we only clearly see this bend when combining our new DEVILS measurements with consistently derived values for lower redshift galaxies from the Galaxy And Mass Assembly (GAMA) survey: this shows the power of having consistent treatment for galaxies at all redshifts.
Velocity Dispersion of the GD-1 Stellar Stream

Tidally dissolved globular clusters form thin stellar streams that preserve a historical record of their past evolution. We report a radial velocity dispersion of 2.1 ± 0.3 km s−1 in the GD-1 stellar stream using a sample of 43 spectroscopically confirmed members. The GD-1 velocity dispersion is constant over the surveyed ≈15° span of the stream. We also measured velocity dispersion in the spur adjacent to the main GD-1 stream, and found a similar value at the tip of the spur. Surprisingly, the region of the spur closer to the stream appears dynamically colder than the main stream. An unperturbed model of the GD-1 stream has a velocity dispersion of ≈0.5 km s−1, indicating that GD-1 has undergone dynamical heating. Stellar streams arising from globular clusters, which prior to their arrival in the Milky Way, orbited a dwarf galaxy with a cored density profile are expected to have experienced the amount of heating required to match the velocity dispersion observed in GD-1. This suggests that GD-1 has been accreted and that imprints of its original host galaxy, including the inner slope of its dark matter halo, remain observable in the stream today.
Defining the distinction: neural and cognitive correlates of apathy & depression in prodromal huntington’s disease

Huntington’s Disease (HD) is a debilitating genetic disorder characterized by motor, cognitive and psychiatric abnormalities. Among these psychiatric symptoms, apathy and depression are some of the most common mood symptoms in the pre-diagnosed population (prHD). This study examined the role of apathy and depression and their neural and cognitive correlates. We compared apathy and depression scores to measures of cognitive control, language, and motor symptoms in a sample of individuals with prHD. Additionally, we compared apathy and depression scores to activation in the default mode network, caudate, putamen, and supplementary motor area (SMA) during a timing task. We found that as apathy increases, cognitive control decreases. Increased activation in the SMA, during the task, was related to increased apathy. Increased depression was related to increased putamen activation. Our results further support the distinction between apathy and depression. Understanding the nature of this distinction can aid in development of interventions. INDEX WORDS: prodromal Huntington’s Disease, fMRI, apathy, depression, cognitive control DEFINING THE DISTINCTION: NEURAL AND COGNITIVE CORRELATES OF APATHY & DEPRESSION IN PRODROMAL HUNTINGTON’S DISEASE
A survey of benchmarking frameworks for reinforcement learning

Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.
Decomposition of group-velocity-locked-vector-dissipative-soliton and the recombination

1. Jiangsu Key Laboratory of Advanced Laser Materials and Devices, Jiangsu Collaborative Innovation Center of Advanced Laser Technology and Emerging Industry, School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, Jiangsu 221116 China 2. School of Engineering and Materials Science, Queen Mary University of London, London, UK 3. Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, 518060, China * Corresponding author: zhaoluming@jsnu.edu. cn
Robust Learning Model Predictive Control for Periodically Correlated Building Control

Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems. To fully exploit their potential while ensuring occupant comfort, a robust control scheme is required to handle various uncertainties, such as external weather and occupant behaviour. However, prominent patterns, especially periodicity, are widely seen in most sources of uncertainty. This paper incorporates this correlated structure into the learning model predictive control framework, in order to learn a global optimal robust control scheme for building operations.