Miracle-dz/UniFAR-FTUs huggingface dataset:
Through Glass Via (TGV) Technology for RF Applications

Over the past several years there have been substantial advancements in through glass via (TGV) technology. There is an excellent opportunity to leverage TGV technology and the insulating properties of glass, to address next generation needs for RF components. Multi-bands multi-standards with carrier aggregation and WiFi and GPS coexistence make RF front end more and more complicated. In order to address the best-fit filtering solutions to RF front end, high-performance inductors and capacitors are required. For inductors, drastic performance improvement in terms of size and quality factor has been demonstrated by technology evolutions from 2D planar inductors to 3D solenoid using TGV. On top of the TGV inductors, we have successfully integrated Cu MIM (metal-insulator-metal) capacitors by using 15um thick Cu plates and a Si-N dielectric layer.
SOI technology for radio-frequency integrated-circuit applications

This paper presents a silicon-on-insulator (SOI) integration technology, including structures and processes of OFF-gate power nMOSFETs, conventional lightly doped drain (LDD) nMOSFETs, and spiral inductors for radio frequency integrated circuit (RFIC) applications. In order to improve the performance of these integrated devices, body contact under the source (to suppress floating-body effects) and salicide (to reduce series resistance) techniques were developed for transistors; additionally, locally thickened oxide (to suppress substrate coupling) and ultra-thick aluminum up to 6 /spl mu/m (to reduce spiral resistance) were also implemented for spiral inductors on high-resistivity SOI substrate. All these approaches are fully compatible with the conventional CMOS processes, demonstrating devices with excellent performance in this paper: 0.25-/spl mu/m gate-length offset-gate power nMOSFET with breakdown voltage (BV/sub DS/) /spl sim/ 22.0 V, cutoff frequency (f/sub T/)/spl sim/15.2 GHz, and maximal oscillation frequency (f/sub max/)/spl sim/8.7 GHz; 0.25-/spl mu/m gate-length LDD nMOSFET with saturation current (I/sub DS/)/spl sim/390 /spl mu/A//spl mu/m, saturation transconductance (g/sub m/)/spl sim/197 /spl mu/S//spl mu/m, cutoff frequency /spl sim/ 25.6 GHz, and maximal oscillation frequency /spl sim/ 31.4 GHz; 2/5/9/10-nH inductors with maximal quality factors (Q/sub max/) 16.3/13.1/8.95/8.59 and self-resonance frequencies (f/sub sr/) 17.2/17.7/6.5/5.8 GHz, respectively. These devices are potentially feasible for RFIC applications.
Experimentally determining the incompatibility of two qubit measurements

We describe and realize an experimental procedure for assessing the incompatibility of two qubit measurements. The experiment consists in a state discrimination task where either measurement is used according to some partial intermediate information. The success statistics of the task provides an upper bound for the amount of incompatibility of the two measurements, as it is quantified by means of their incompatibility robustness. For a broad class of unbiased and possibly noisy qubit measurements, one can make this upper bound coincide with the true value of the robustness by suitably tuning the preparation of the experiment. We demonstrate this fact in an optical setup, where the qubit states are encoded into the photons’ polarization degrees of freedom, and incompatibility is directly accessed by virtue of a refined control on the amplitude, phase and purity of the final projection stage of the measurements. Our work thus establishes the practical feasibility of a recently proposed method for the detection of quantum incompatibility.
ICON: Implicit Clothed humans Obtained from Normals

Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn an avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from each image and then combines these into an animatable avatar. Implicit functions are well suited to the first task, as they can capture details like hair and clothes. Current methods, however, are not robust to varied human poses and often produce 3D surfaces with broken or disembodied limbs, missing details, or non-human shapes. The problem is that these methods use global feature encoders that are sensitive to global pose. To address this, we propose ICON (“Tmplicit Clothed humans Obtained from Normals”), which, instead, uses local features. ICON has two main modules, both of which exploit the SMPL(-X) body model. First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals. Second, a visibility-aware implicit surface regressor produces an iso-surface of a human occupancy field. Importantly, at inference time, a feedback loop alternates between refining the SMPL(-X) mesh using the inferred clothed normals and then refining the normals. Given multiple reconstructed frames of a subject in varied poses, we use a modified version of SCANimate to produce an animatable avatar from them. Evaluation on the AGORA and CAPE datasets shows that ICON outperforms the state of the art in reconstruction, even with heavily limited training data. Additionally, it is much more robust to out-of-distribution samples, e.g., in-the-wild poses/images and out-of-frame cropping. ICON takes a step towards robust 3D clothed human reconstruction from in-the-wild images. This enables avatar creation directly from video with personalized pose-dependent cloth deformation. Models and code are available for research at https://icon.is. tue.mpg. de.
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization

Multi-armed bandit algorithms like Thompson Sampling (TS) can be used to conduct adaptive experiments, in which maximizing reward means that data is used to progressively assign participants to more effective arms. Such assignment strategies increase the risk of statistical hypothesis tests identifying a difference between arms when there is not one, and failing to conclude there is a difference in arms when there truly is one. We tackle this by introducing a novel heuristic algorithm, called TS-PostDiff (Posterior Probability of Difference). TS-PostDiff takes a Bayesian approach to mixing TS and Uniform Random (UR): the probability a participant is assigned using UR allocation is the posterior probability that the difference between two arms is 'small' (below a certain threshold), allowing for more UR exploration when there is little or no reward to be gained. We evaluate TS-PostDiff against state-of-the-art strategies. The empirical and simulation results help characterize the trade-offs of these approaches between reward, False Positive Rate (FPR), and statistical power, as well as under which circumstances each is effective. We quantify the advantage of TS-PostDiff in performing well across multiple differences in arm means (effect sizes), showing the benefits of adaptively changing randomization/exploration in TS in a"Statistically Considerate"manner: reducing FPR and increasing statistical power when differences are small or zero and there is less reward to be gained, while exploiting more when differences may be large. This highlights important considerations for future algorithm development and analysis to better balance reward and statistical analysis.
Round reaction forces and knee joint moments during basketball jab step cutting maneuver

Basketball game involves multi-directional cutting maneuvers (CMs) in response to the opponents. Lab based cutting maneuver tests involving lateral shuffling and 45 cutting, have reported high ground reaction forces (GRFs) (2.97 BW, Dayakidis and Boudolos, 2006) and external knee moments (2Nm/kg, Beiser et al. 2001) which was considered as risk factor for anterior cruciate ligament (ACL) injuries (Boden et al. 2000). A frequently used jab step CMs in a basketball game is the fake, plant and cut movements. This is particularly used by offensive players with or without the ball in hand during a tight one-on-one situation. The offensive player usually gives a quick fake to the dominant side, which involves leg planting and shoulder movement followed by speed cutting to the opposite direction of which the defender is directed. There is a lack of biomechanics information in the current literature about this highly relevant CM. There is unknown about the magnitudes of GRFs and knee moments during this jab step CM and the potential influences from different basketball footwears.
Differential Privacy in Aggregated Mobility Networks: Balancing Privacy and Utility

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This paper presents a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.
Analysis and Evaluation of Kinect-based Action Recognition Algorithms

Human action recognition still exists many challenging problems such as different viewpoints, occlusion, lighting conditions, human body size and the speed of action execution, although it has been widely used in different areas. To tackle these challenges, the Kinect depth sensor has been developed to record real time depth sequences, which are insensitive to the color of human clothes and illumination conditions. Many methods on recognizing human action have been reported in the literature such as HON4D, HOPC, RBD and HDG, which use the 4D surface normals, pointclouds, skeleton-based model and depth gradients respectively to capture discriminative information from depth videos or skeleton data. In this research project, the performance of four aforementioned algorithms will be analyzed and evaluated using five benchmark datasets, which cover challenging issues such as noise, change of viewpoints, background clutters and occlusions. We also implemented and improved the HDG algorithm, and applied it in cross-view action recognition using the UWA3D Multiview Activity dataset. Moreover, we used different combinations of individual feature vectors in HDG for performance evaluation. The experimental results show that our improvement of HDG outperforms other three state-of-the-art algorithms for cross-view action recognition.
Ruta: Dis-aggregated routing system over multi-cloud

Over the years, the SDN evolution create multiple overlay technologies which is inefficient and hard to deploy end-to-end traffic engineering services, Ruta is designed as an unified encapsulation with Segment Routing, Crypto and NAT-Traversal capabilities over UDP. Ruta could be deployed as a cloud native SDN platform globally over multi-cloud and integrated with each applications on transport layer, which provide nearly zero loss and almost less than 200ms latency to access anywhere in the world over internet.
Research on Charging and Discharging Strategies of Flywheel Energy Storage System Using Permanent Magnet Synchronous Motor

Flywheel Energy Storage System (FESS) has become an important research field because of the energy problem. To solve the control of Permanent Magnet Synchronous Motor (PMSM) and Bi-directional power converter, both charging and discharging control strategiesthe Compound Control for charging and the Integrated Optimization Flux-Weak Control for discharging are proposed, together with the dual-loop control and simulation results. Based on these analysis and results, a high-power DSP-controlled experiment platform is set up to test the proposed strategies. Simulation and experiment results show that the FESS can run smoothly both under charging and discharging conditions so that both the strategies and the platform are effective.
On optimal convergence rates for discrete minimizers of the Gross-Pitaevskii energy in LOD spaces

In this paper we revisit a two-level discretization based on the Localized Orthogonal Decomposition (LOD). It was originally proposed in [P.Henning, A.M{\aa}lqvist, D.Peterseim. SIAM J. Numer. Anal.52-4:1525-1550, 2014] to compute ground states of Bose-Einstein condensates by finding discrete minimizers of the Gross-Pitaevskii energy functional. The established convergence rates for the method appeared however suboptimal compared to numerical observations and a proof of optimal rates in this setting remained open. In this paper we shall close this gap by proving optimal order error estimates for the $L^2$- and $H^1$-error between the exact ground state and discrete minimizers, as well as error estimates for the ground state energy and the ground state eigenvalue. In particular, the achieved convergence rates for the energy and the eigenvalue are of $6$th order with respect to the mesh size on which the discrete LOD space is based, without making any additional regularity assumptions. These high rates justify the use of very coarse meshes, which significantly reduces the computational effort for finding accurate approximations of ground states. In addition, we include numerical experiments that confirm the optimality of the new theoretical convergence rates, both for smooth and discontinuous potentials.
On the Uncertain Single-View Depths in Endoscopies

—Estimating depth from endoscopic images is a pre-requisite for a wide set of AI-assisted technologies, namely accurate localization, measurement of tumors, or identiﬁcation of non-inspected areas. As the domain speci-ﬁcity of colonoscopies –a deformable low-texture environment with ﬂuids, poor lighting conditions and abrupt sensor motions– pose challenges to multi-view approaches, single-view depth learning stands out as a promising line of research. In this paper, we explore for the ﬁrst time Bayesian deep networks for single-view depth estimation in colonoscopies. Their uncertainty quantiﬁcation offers great potential for such a critical application area. Our speciﬁc contribution is two-fold: 1) an exhaustive analysis of Bayesian deep networks for depth estimation in three different datasets, highlighting challenges and conclusions regarding synthetic–to–real domain changes and supervised vs. self-supervised methods; and 2) a novel teacher-student approach to deep depth learning that takes into account the teacher uncertainty.
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources

A classification scheme of a scientific subject gives an overview of its body of knowledge. It can also be used to facilitate access to research articles and other materials related to the subject. For example, the ACM Computing Classification System (CCS) is used in the ACM Digital Library search interface and also for indexing computer science papers. We observed that a comprehensive classification system like CCS or Mathematics Subject Classification (MSC) does not exist for Computational Linguistics (CL) and Natural Language Processing (NLP). We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject. The currently proposed taxonomy includes 334 topics and focuses on educational aspects of CL/NLP; it is based primarily, but not exclusively, on lecture notes from NLP courses. We discuss how such a taxonomy can help in various real-world applications, including tutoring platforms, resource retrieval, resource recommendation, prerequisite chain learning, and survey generation.
Chromatic Quasisymmetric Class Functions for combinatorial Hopf monoids

We study the chromatic quasisymmetric class function of a linearized combinatorial Hopf monoid. Given a linearized combinatorial Hopf monoid H , and an H -structure h on a set N , there are proper colorings of h , generalizing graph colorings and poset partitions. We show that the automorphism group of h acts on the set of proper colorings. The chromatic quasisymmetric class function enumerates the ﬁxed points of this action, weighting each coloring with a monomial. For the Hopf monoid of graphs this invariant generalizes Stanley’s chromatic symmetric function and specializes to the orbital chromatic polynomial of Cameron and Kayibi. We also introduce the ﬂag quasisymmetric class function of a balanced relative simplicial complex equipped with a group action. We show that, under certain conditions, the chromatic quasisymmetric class function of h is the ﬂag quasisymmetric class function of a balanced relative simplicial complex that we call the coloring complex of h . We use this result to deduce various inequalities for the associated orbital polynomial invariants. We apply these results to several examples related to enumerating graph colorings, poset partitions, generic functions on matroids or generalized permutohedra, and others.
Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives

Smart healthcare systems are gaining popularity with the rapid development of intelligent sensors, the Internet of Things (IoT) applications and services, and wireless communications. However, at the same time, several vulnerabilities and adversarial attacks make it challenging for a safe and secure smart healthcare system from a security point of view. Machine learning has been used widely to develop suitable models to predict and mitigate attacks. Still, the attacks could trick the machine learning models and misclassify outputs generated by the model. As a result, it leads to incorrect decisions, for example, false disease detection and wrong treatment plans for patients. In this paper, we address the type of adversarial attacks and their impact on smart healthcare systems. We propose a model to examine how adversarial attacks impact machine learning classifiers. To test the model, we use a medical image dataset. Our model can classify medical images with high accuracy. We then attacked the model with a Fast Gradient Sign Method attack (FGSM) to cause the model to predict the images and misclassify them inaccurately. Using transfer learning, we train a VGG-19 model with the medical dataset and later implement the FGSM to the Convolutional Neural Network (CNN) to examine the significant impact it causes on the performance and accuracy of the machine learning model. Our results demonstrate that the adversarial attack misclassifies the images, causing the model's accuracy rate to drop from 88% to 11%.
Dynamic density functional theory of multicomponent cellular membranes

We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within the formalism of dynamic density functional theory and can be extended to include features such as the presence of proteins and membrane deformations. This framework represents a paradigm shift by enabling simulations that can access length scales on the order of microns and time scales on the order of seconds, all while maintaining near fidelity to the underlying MD models. These length and time scales are significant for accessing biological processes associated with signaling pathways within cells. Membrane interactions with RAS, a protein implicated in roughly 30% of human cancers, are considered as an application. Simulation results are presented and verified with MD simulations, and implications of this new capability are discussed.
London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London

Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the “Knowledge of London,” the names and layout of over 26,000 streets and thousands of points of interest in London. Here we review past behavioral and neuroimaging studies of London taxi drivers, covering the structural differences in hippocampal gray matter density and brain dynamics associated with navigating London. We examine the process by which they learn the layout of London, detailing the key learning steps: systematic study of maps, travel on selected overlapping routes, the mental visualization of places and the optimal use of subgoals. Our analysis provides the first map of the street network covered by the routes used to learn the network, allowing insight into where there are gaps in this network. The methods described could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence systems to efficiently learn new environments.
Early Intervention in Psychosis services: A systematic review and narrative synthesis of the barriers and facilitators to implementation

Abstract Background Early intervention in psychosis (EIP) services target the early manifestation of psychosis and provide multidisciplinary care. They demonstrate effectiveness and cost-effectiveness. Implementation of EIP services is inconsistent and piecemeal. This systematic review and narrative synthesis aims to identify barriers and facilitators to EIP service implementation. Methods We conducted an electronic search of databases (EMBASE, Medline, Web of Science, and PsychINFO) to detect papers reporting EIP service implementation findings and associated barriers and facilitators. The search occurred between June to August 2020, and again in January 2021. Articles meeting inclusion criteria were extracted and narratively synthesized. A quality assessment was conducted using the Mixed Methods Appraisal Tool. Results Twenty-three studies were selected. The most common study design was descriptive accounts of implementation. Patient age ranged varied from 14 to 35 years. We identified three barrier and facilitator domains: (a) system; (b) services; and (c) staff, and a range of subdomains. The most frequent subdomains were “funding” and “strength of collaboration and communication between EIP and outside groups and services”. Associations between domains and subdomains were evident, particularly between systems and services. Conclusions A range of barriers and facilitators to EIP implementation exist. Some of these are generic factors germane across health systems and services, while others are specific to EIP services. A thorough prior understanding of these challenges and enablers are necessary before implementation is attempted. Accounting for these issues within local and national contexts may help predict and increase the likelihood of services’ success, stability, and longevity.