Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline.
Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. The dataset and code have been made available online.
Point Cloud Semantic Scene Completion from RGB-D Images

In this paper, we devise a novel semantic completion network, called point cloud semantic scene completion network (PCSSC-Net), for indoor scenes solely based on point clouds. Existing point cloud completion networks still suffer from their inability of fully recovering complex structures and contents from global geometric descriptions neglecting semantic hints. To extract and infer comprehensive information from partial input, we design a patch-based contextual encoder to hierarchically learn point-level, patch-level, and scene-level geometric and contextual semantic information with a divide-and-conquer strategy. Consider that the scene semantics afford a high-level clue of constituting geometry for an indoor scene environment, we articulate a semantics-guided completion decoder where semantics could help cluster isolated points in the latent space and infer complicated scene geometry. Given the fact that real-world scans tend to be incomplete as ground truth, we choose to synthesize scene dataset with RGB-D images and annotate complete point clouds as ground truth for the supervised training purpose. Extensive experiments validate that our new method achieves the state-of-the-art performance, in contrast with the current methods applied to our dataset.
Thermal Cracking of Virgin and Waste Plastics of PP and LDPE in a Semibatch Reactor under Atmospheric Pressure

Study of the decomposition kinetics, pyrolysis products, and even reaction mechanisms plays an important role for the development of polymer recycling. In the present research, the kinetics of virgin and waste polypropylene (PP) and low-density polyethylene (LDPE) were studied by a modified Coats–Redfern method. Afterward, thermal cracking of them in a semibatch reactor under atmospheric pressure in nitrogen has been investigated. Both virgin and waste plastics are decomposed at 420–460 °C, and the products have been characterized using GC, FT-IR, 1H NMR, and GC-MS. The reaction path and the degradation mechanism for the thermal cracking of polymer in this study were also discussed. The lower activation energy of waste PP and LDPE indicates that waste plastics degrade at lower initial temperature and may favor mild conditions. Due to the short residence time, the higher gaseous and liquid yields were obtained for virgin PP and LDPE. A large amount of residues for waste polymer indicates that it is a favor.
Cloud FPGA Cartography using PCIe Contention

Public cloud infrastructures allow for easy, on-demand access to FPGA resources. However, the low-level, direct access to the FPGA hardware exposes the infrastructure providers to new types of attacks. Prior work has shown that it is possible to uniquely identify the underlying hardware by creating fingerprints of the different FPGA instances that users rent from a cloud provider, but such work was not able to actually map the cloud FPGA infrastructure itself. Meanwhile, this paper demonstrates that it is possible to reverse-engineer the co-location of FPGA boards inside a cloud FPGA server using PCIe contention. Specifically, this work deduces the Non-Uniform Memory Access (NUMA) locality of FPGA boards within a server by analyzing their mutual PCIe contention during simultaneous use of the PCIe bus. In addition, experiments conducted in data centers located in several geographic regions and repeated at different times are used to calculate the probability that cloud providers allocate FPGA boards co-located in the same server to a user. This paper thus shows that it is possible to map cloud FPGA infrastructures, and learn how FPGA instances are physically co-located within a server. Consequently, this paper also highlights the importance of mitigating these novel avenues for reverse-engineering and mapping of cloud FPGA setups, as they can reveal insights about the cloud infrastructure itself, or assist other single- and multi-tenant attacks.
1.5-nm Node Surrounding Gate Transistor (SGT)-SRAM Cell with Staggered Pillar and Self-Aligned Process for Gate, Bottom Contact, and Pillar

We propose the architecture and manufacturing process of 6 surrounding-gate-transistor (SGT) SRAM cell for the 1.5nm technology node. The staggered layout and self-aligned patternings have successfully realized a transition from 5nm to 1.5nm node with the same SGT diameter. Electrical characteristics by TCAD simulation are finally demonstrated.
Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder

Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. Methods: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. Results: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.
LFP based analysis of brain injured anesthetized animals undergoing closed-loop intracortical stimulation

Acquired brain injuries, such as stroke, are a major cause of long-term disability worldwide. When an injury occurs within primary motor cortex (M1) there is both a loss of descending information to the spinal cord and a disruption in the communication within and between spared regions, which can lead to hemiparesis and other motor dysfunctions. Recently, activity-dependent stimulation (ADS), a closed loop intracortical neurostimulation technique, was shown to significantly improve behavioural recovery in rodent models following a unilateral traumatic brain injury in the primary motor cortex. While behavioural benefits of this approach have been described, the neurophysiological changes have not yet been fully characterized. In this study, we investigated how neural activity in the form of local field potential (LFP) was impacted by an ischemic lesion and how it changed following prolonged ADS sessions. Intracortical microelectrodes arrays were used to record and stimulate from the rostral forelimb area (RFA) and the primary somatosensory cortex (S1) of 12 rats (Long Evans). An ischemic injury induced with Endothelin-1 microinjections in the caudal forelimb area (CFA) was used as an injury model. We found that the ET-1 caused lesion led to widespread drop of LFP power across all the frequency bands in both the monitored areas. The lesion did not change the functional connectivity within the brain areas, while it increased the synchronization between them. Following stimulation, LFP power generally increased in both areas. Stimulation also led to a decrement in connectivity between S1 and RFA, while causing the within S1 connectivity to increase.
Domain Adaptation for Cross-Subject Emotion Recognition by Subject Clustering

The high inter-subject variability in emotional EEG activities has posed great challenges for practical EEG-based affective computing applications. The recently popular domain adaptation strategy seemed to be a promising technique for addressing this issue, by minimizing the discrepancy of EEG data from different subjects. The present study proposed and implemented an extended Domain Adaptation method by introducing Subject Clustering (DASC). By clustering subjects based on the similarity of their emotion-specific EEG activities, the DASC method could make a flexible use of the available source domain information towards an optimized target domain application. Using the publicly available EEG dataset of DEAP, the DASC method achieved an average accuracy of $73.9\pm 13.5\%$ and $68.8\pm 11.2\%$ for binary classifications of the high or low levels of valence and arousal. Comparison with the state-of-the-art performance as well as the ablation experiments suggest the proposed DASC method as an effective extension to the conventional domain adaptation methods for EEG-based emotion recognition.
Why Did Polls or OPOs Get Forecast Wrong? Perceived Bias and Inference of Bias Impact

The failed election forecasts based on polls and Online Public Opinions(OPOs) cast doubts on the predictability of big data. In this paper, we try to examine whether polls and OPOs reveal vote preferences. Taking the 2016 US presidential election as an example, we adopt the dynamic linear models to trace two candidates' popular votes by fusing measurements from social media, news, prediction market, and polls. The interesting findings we obtained are as follows. First, with the estimations of basic dynamic linear model, we find that both the polls and OPOs are subject to measurement bias. Second, to characterize the bias, we add a time-varying parameter into the basic dynamic linear model. Via Markov Chain Monte Carlo(MCMC) estimations, we find that although polls and news failed to reflect the victory of Donald Trump, they did reflect candidates' popularity nationwide. Compared with polls and news, OPOs have larger deviations in revealing candidates' national popularity, but they are closer to the electoral vote outcome. Third, we find that there are patterns in the same type of measurement bias, no matter in direction or magnitude. The above three findings highlight the necessity of fusion method using multi-source heterogeneous data to make election forecasts. The fusion is expected to cancel out different types of measurement bias. The findings also indicate that both the polls and OPOs reveal the public opinions although the measurement bias exists. It is useful to adjust measurements extracted from polls and OPOs for specific forecast models.
Steering Angle Prediction Techniques for Autonomous Ground Vehicles: A Review

Unintentional lane departure accidents are one of the biggest reasons for the causalities that occur due to human errors. By incorporating lane-keeping features in vehicles, many accidents can be avoided. The lane-keeping system operates by auto-steering the vehicle in order to keep it within the desired lane, despite of changes in road conditions and other interferences. Accurate steering angle prediction is crucial to keep the vehicle within the road boundaries, which is a challenging task. The main difficulty in this regard is to identify the drivable road area on heterogeneous road types varying in color, texture, illumination conditions, and lane marking types. This strenuous problem can be addressed by two approaches, namely, ‘computer-vision-based approach’ and ‘imitation-learning-based approach’. To the best of our knowledge, at present, there is no such detailed review study covering both the approaches and their related optimization techniques. This comprehensive review attempts to provide a clear picture of both approaches of steering angle prediction in the form of step by step procedures. The taxonomy of steering angle prediction has been presented in the paper for a better comprehension of the problem. We have also discussed open research problems at the end of the paper to help the researchers of this area to discover new research horizons.
Experimental Demonstration of Gbps OFDM with 13m long for OWC Systems

In this paper, an experimental demonstration of gigabit optical wireless communication system using orthogonal frequency division multiplexing (OFDM) modulation format is reported. Intensity modulation with direct detection (IM/DD) is employed. In each OFDM symbol, the bandwidth of the analog electrical signal is 400 MHz. The subcarriers are modulated by 32-quadrature amplitude modulation (QAM). The maximum likelihood (ML) phase estimation and compensation are employed. 150 mW blue light laser with the lambda of 405 nm is adopted. As a result, a gigabit per second can be realized, where the communication distances, in door scenario, of 13m is successfully transmitted.
Transistor Self-Heating: The Rising Challenge for Semiconductor Testing

Quantum confinement in 3-D device structure together with the newly employed materials like silicon-germanium (SiGe) in advanced technologies (e.g., FinFET, nanowire, nanosheets, etc.) makes transistors seriously suffer from localized self-heating effects in which generated heat within the transistor’s channel is trapped inside. This is mainly due to the much lower channel and surrounding material thermal conductivity and hence lower ability for heat dissipation along with the firm isolation needed for better gate control. Self-heating effects strongly accelerate transistor aging and all the underlying defect generation mechanisms leading to serious reliability problems during the early life of chips. The key challenge in transistor self-heating when it comes to semiconductor testing is the profound difficulty in measuring self-heating directly as generated heat is trapped inside the transistor. Failing in capturing self-heating phenomenon during IC testing would later lead to chips malfunctions at run-time and hence early life failures because of reliability degradations and failure mechanisms will be unexpectedly accelerated akin to excessive internal temperatures. In this paper, we investigate the impact of self-heating effects on n-type and p-type FinFET transistors calibrated with Intel 14 nm measurement data using mature Technology CAD (TCAD) simulations. Then, the industry standard compact model for FinFET technologies (BSIM-CMG) is carefully calibrated to accurately model and reproduce all measurements. This enables circuit’s designers, for the first time, to accurately investigate how emerging self-heating effects in transistors impacts the performance and power of large circuits. This opens new doors for developing novel Design-for-Testing methods that effectively reveal self-heating effects and increase the yield of chips.
Multiple Solutions Based Particle Swarm Optimization for Cluster-Head-Selection in Wireless-Sensor-Network

Wireless sensor network (WSN) has a significant role in wide range of scientific and industrial applications. In WSN, within the operation area of sensor nodes the nodes are randomly deployed. The constraint related to energy is considered as one of the major challenges for WSN, which may not only affect the sensor nodes efficiency but also influences the operational capabilities of the network. Therefore, numerous attempts of researches have been proposed to counter this energy problem in WSN. Hierarchical clustering approaches are popular techniques that offered the efficient consumption of the energy in WSN. In addition to this, it is understood that the optimum choice of sensor as cluster head can critically help to reduce the energy consumption of the sensor node. In recent years, metaheuristic optimization is used as a proposed technique for the optimal selection of cluster heads. Furthermore, it is noteworthy here that proposed techniques should be efficient enough to provide the optimal solution for the given problem. Therefore, in this regard, various attempts are made in the form of modified versions or new metaheuristic algorithms for optimization problems. The research in the paper offered a modified version of particle-swarm-optimization (PSO) for the optimal selection of sensor nodes as cluster heads. The performance of the suggested algorithm is experimented and compared with the renowned optimization techniques. The proposed approach produced better results in the form of residual energy, number of live nodes, sum of dead nodes, and convergence rate.
Training the bladder how to void: A noninvasive spinal neuromodulation case study

Spinal cord injury results in neurogenic lower urinary tract dysfunction (NLUTD) leading to reduced bladder capacity, loss of bladder sensation and inability to initiate bladder voiding. Transcutaneous Electrical Spinal Cord Neuromodulation (TESCoN) was developed to activate and retrain the spinal circuits to enable function. In this pilot study, we developed a novel method to retrain the bladder by externally filling and voluntarily emptying the bladder with saline in the presence of TESCoN. We successfully demonstrated the use of TESCoN in improving bladder capacity, regaining sensation and enabling voluntary voiding with increased voiding efficiency in an individual with complete chronic paralysis.
Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) of modelling <inline-formula> <tex-math notation="LaTeX">$(mR^{2})$ </tex-math></inline-formula> were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE = 7.387 g kg<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$mR^{2}=0.446$ </tex-math></inline-formula>). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula> and the prediction accuracy of <inline-formula> <tex-math notation="LaTeX">$R^{2}~(pR^{2})$ </tex-math></inline-formula> is 0.723. For SVR, the pRMSE is 4.638 g kg<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula>, and the <inline-formula> <tex-math notation="LaTeX">$pR^{2}$ </tex-math></inline-formula> is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.
Serial Decoding of Macaque Intracortical Activity for Feedforward Control of Coherent Sequential Reach

Serial movement is crucial for interacting with complex environments, and invasive brain-machine interfaces (BMIs) are currently being developed to restore motor function by decoding neural responses. However, coherent sequential movement decoding in complex behavior is still poorly understood. Although current closed-loop BMIs decoders enable subjects to drive equipment step by step to approach multiple targets and complete serial action, such a control strategy is inflexible and unnatural. Here, we propose an open-loop sequential decoding algorithm under a synchronized BMI framework to achieve high-performance sequential movement decoding, providing a potential solution for BMIs in complex movement reconstruction.