Stereotactic Electroencephalography (SEEG)

Drug resistant epilepsy (DRE) is not an uncommon clinical condition. DRE could cause disabling seizures and even sudden unexpected death in epilepsy (SUDEP). Pre-surgical evaluation is necessary to for surgical treatment to cure or palliative epilepsy. If feasible, surgical excision of an epileptic focus provides the best chance of cure. However, the standard non-invasive workup could not always identify the epileptic focus. Stereotactic EEG (SEEG) is an invasive EEG that could provide the spatial and temporal progression of epileptic discharge so that we could localize or lateralise the epileptic focus more easily. This chapter aims to illustrate the principle of SEEG, the methods of SEEG electrode insertion, the usual white matter tract pathway that epileptic discharge progresses. It also discusses the therapeutic use of SEEG in lesioning with radiofrequency ablation (RFA), as well as the future potential as part of the brain-computer interface (BCI)

CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions.Comment: 11 pages, 6 figures, accepted by iccv 2023 non-camera-ready versio

Overview of Radiosurgery for Intracranial Meningiomas

Meningiomas are the second common Central Nervous System (CNS) neoplasm, and are the most common benign intracranial tumor. They approximately constitute up to 30% of all intracranial tumors. They arise from the arachnoidal coverings of brain. Presentation varies and depends on size, number and location of tumors. Symptoms include those related to increased in intracranial pressure, local irritative features including seizure and local pressure effect to eloquent areas, white matter tracts and cranial nerves. Management of meningiomsa is always challenging and multi-disciplinary approaches includes surgery, radiotherapy and possible chemotherapy and immunotherapy. Among radiation therapy treatment, stereotactic radiosurgery (SRS) or stereotactic radiosurgery (SRT) is getting the popularity compared to traditional conformal radiotherapy with comparable tumor control rate

Wrong Turn in Cyberspace: Using ICANN to Route Around the APA and the Constitution

The Internet relies on an underlying centralized hierarchy built into the domain name system (DNS) to control the routing for the vast majority of Internet traffic. At its heart is a single data file, known as the root. Control of the root provides singular power in cyberspace. This Article first describes how the United States government found itself in control of the root. It then describes how, in an attempt to meet concerns that the United States could so dominate an Internet chokepoint, the U. S. Department of Commerce (DoC) summoned into being the Internet Corporation for Assigned Names and Numbers (ICANN), a formally private nonprofit California corporation. DoC then signed contracts with ICANN in order to clothe it with most of the U. S. government\u27s power over the DNS, and convinced other parties to recognize ICANN\u27s authority. ICANN then took regulatory actions that the U. S. Department of Commerce was unable or unwilling to make itself, including the imposition on all registrants of Internet addresses of an idiosyncratic set of arbitration rules and procedures that benefit third-party trademark holders. Professor Froomkin then argues that the use of ICANN to regulate in the stead of an executive agency violates fundamental values and policies designed to ensure democratic control over the use of government power, and sets a precedent that risks being expanded into other regulatory activities. He argues that DoC\u27s use of ICANN to make rules either violates the APA\u27s requirement for notice and comment in rulemaking and judicial review, or it violates the Constitution\u27s nondelegation doctrine. Professor Froomkin reviews possible alternatives to ICANN, and ultimately proposes a decentralized structure in which the namespace of the DNS is spread out over a transnational group of policy partners with DoC

Educational Usage of Mobile Devices: Differences Between Postgraduate and Undergraduate Students

The rapid increase of smartphone usage in recent years has provided students the opportunity to participate in mobile learning (m-learning) anywhere, anytime. Academic institutions are also following this trend to launch many m-learning services. This article investigates the differences of the user needs between undergraduate (UG) and postgraduate (PG) students though an online survey with 140 Library Information Systems (LIS) subjects in a Japanese university in order to provide solid foundations for future m-learning studies. We find that UG and PG students do not show significant differences in adopting m-learning by smartphones despite the fact that they have different learning patterns. The m-learning frequencies of smartphones generally range from weekly to monthly, where using search engines is the most frequent, and reading academic resources is the least frequent. They tend to use these services for handling their daily routines (such as search engine, social networks) rather than their academic activities (such as using online databases to search for academic materials). Further, the results also show that content displaying issues (e.g., small display screen, text unable to enlarge) are barriers for most subjects in using these m-learning services

TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses

3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent years. However, these methods only use the detection boxes of the current frame to obtain trajectory-box association results, which makes it impossible for the tracker to recover objects missed by the detector. In this paper, we present TrajectoryFormer, a novel point-cloud-based 3D MOT framework. To recover the missed object by detector, we generates multiple trajectory hypotheses with hybrid candidate boxes, including temporally predicted boxes and current-frame detection boxes, for trajectory-box association. The predicted boxes can propagate object's history trajectory information to the current frame and thus the network can tolerate short-term miss detection of the tracked objects. We combine long-term object motion feature and short-term object appearance feature to create per-hypothesis feature embedding, which reduces the computational overhead for spatial-temporal encoding. Additionally, we introduce a Global-Local Interaction Module to conduct information interaction among all hypotheses and models their spatial relations, leading to accurate estimation of hypotheses. Our TrajectoryFormer achieves state-of-the-art performance on the Waymo 3D MOT benchmarks.Comment: 10 pages, 3 figure

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subnets. Subsequent explorations decompose supernet weights via a particular criterion, e.g., gradient matching, to reduce the interference; yet they suffer from huge computational cost and low space separability. In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. NAS-LID evaluates the geometrical properties of architectures by calculating the low-cost LID features layer-by-layer, and the similarity characterized by LID enjoys better separability compared with gradients, which thus effectively reduces the interference among subnets. Extensive experiments on NASBench-201 indicate that NAS-LID achieves superior performance with better efficiency. Specifically, compared to the gradient-driven method, NAS-LID can save up to 86% of GPU memory overhead when searching on NASBench-201. We also demonstrate the effectiveness of NAS-LID on ProxylessNAS and OFA spaces. Source code: https://github.com/marsggbo/NAS-LID.Comment: Accepted by AAAI2023, AutoML, NA

Is comfort food really good for the soul? A Replication of Troisi and Gabriel's (2011) Study 2

The supplementary file contains the questionnaires and scales, consent form, and methodology used.</p

VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner. The information of the frame triplet is iteratively fused onto the center frame. To extend TROF for handling more frames, we further propose a MOtion Propagation (MOP) module that bridges multiple TROFs and propagates motion features between adjacent TROFs. With the iterative flow estimation refinement, the information fused in individual TROFs can be propagated into the whole sequence via MOP. By effectively exploiting video information, VideoFlow presents extraordinary performance, ranking 1st on all public benchmarks. On the Sintel benchmark, VideoFlow achieves 1.649 and 0.991 average end-point-error (AEPE) on the final and clean passes, a 15.1% and 7.6% error reduction from the best-published results (1.943 and 1.073 from FlowFormer++). On the KITTI-2015 benchmark, VideoFlow achieves an F1-all error of 3.65%, a 19.2% error reduction from the best-published result (4.52% from FlowFormer++). Code is released at \url{https://github.com/XiaoyuShi97/VideoFlow}