Deterministic Relay Networks with State Information

Motivated by fading channels and erasure channels, the problem of reliable communication over deterministic relay networks is studied, in which relay nodes receive a function of the incoming signals and a random network state. An achievable rate is characterized for the case in which destination nodes have full knowledge of the state information. If the relay nodes receive a linear function of the incoming signals and the state in a finite field, then the achievable rate is shown to be optimal, meeting the cut-set upper bound on the capacity. This result generalizes on a unified framework the work of Avestimehr, Diggavi, and Tse on the deterministic networks with state dependency, the work of Dana, Gowaikar, Palanki, Hassibi, and Effros on linear erasure networks with interference, and the work of Smith and Vishwanath on linear erasure networks with broadcast.Comment: 5 pages, to appear in proc. IEEE ISIT, June 200

Surgical Options for Failed Rotator Cuff Repair, except Arthroplasty: Review of Current Methods

Although the prevalence of rotator cuff tears is dependent on the size, 11% to 94% of patients experience retear or healing failure after rotator cuff repair. Treatment of patients with failed rotator cuff repair ranges widely, from conservative treatment to arthroplasty. This review article attempts to summarize the most recent and relevant surgical options for failed rotator cuff repair patients, and the outcomes of each treatment, except arthroplasty

Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.Comment: NeurIPS 202

Emittance Measurement for Beamline Extension at the PET Cyclotron

Particle-induced X-ray emission is used for determining the elemental composition of materials. This method uses low-energy protons (of several MeV), which can be obtained from high-energy (of tens MeV) accelerators. Instead of manufacturing an accelerator for generating the MeV protons, the use of a PET cyclotron has been suggested for designing the beamline for multipurpose applications, especially for the PIXE experiment, which has a dedicated high-energy (of tens MeV) accelerator. The beam properties of the cyclotron were determined at this experimental facility by using an external beamline before transferring the ion beam to the experimental chamber. We measured the beam profile and calculated the emittance using the pepper-pot method. The beam profile was measured as the beam current using a wire scanner, and the emittance was measured as the beam distribution at the beam dump using a radiochromic film. We analyzed the measurement results and are planning to use the results obtained in the simulations of external beamline and aligned beamline components. We will consider energy degradation after computing the beamline simulation. The experimental study focused on measuring the emittance from the cyclotron, and the results of this study are presented in this paper

Central vs. Peripheral Vision during a Singe-Leg Drop Jump: Implications of Dynamics and Patellofemoral Joint Stress

Landing on a single-leg without receiving direct visual information (e.g., not looking at the ground) may increase the risk of injury. We examined whether visual focus contributed to the changing lower-extremity dynamics and patellofemoral joint stress during a single-leg drop jump task. Twenty healthy volunteers visited the laboratory for three separate sessions. During each session, participants randomly performed either of two types of a single-leg drop jump task from a 30 cm high wooden box. Subsequently, participants looked at the landing spot (central vision condition) or kept their heads up (peripheral vision condition) when performing the task. Sagittal and frontal plane lower-extremity joint angles and joint moments (in the ankle, knee, and hip), including the vertical ground reaction force, and patellofemoral joint stress during the first landing phase (from initial contact to peak knee flexion) were compared. Greater ankle inversion and hip adduction were observed when landing with the peripheral vision condition. However, the magnitudes were negligeable (Cohen’s d effect siz