On the Optimal Recovery of Graph Signals

Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery, a mathematical framework for learning a function from observational data that adopts a worst-case perspective tied to model assumptions on the function to be learned. Earlier work in the optimal recovery literature has shown that minimizing a regularized objective produces optimal solutions for a general class of problems, but did not fully identify the regularization parameter. Our main contribution provides a way to compute regularization parameters that are optimal or near-optimal (depending on the setting), specifically for graph signal processing problems. Our results offer a new interpretation for classical optimization techniques in graph-based learning and also come with new insights for hyperparameter selection. We illustrate the potential of our methods in numerical experiments on several semi-synthetic graph signal processing datasets.Comment: This paper has been accepted by 14th International conference on Sampling Theory and Applications (SampTA 2023

Scaling Forward Gradient With Local Losses

Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks. However, the standard forward gradient algorithm, when applied naively, suffers from high variance when the number of parameters to be learned is large. In this paper, we propose a series of architectural and algorithmic modifications that together make forward gradient learning practical for standard deep learning benchmark tasks. We show that it is possible to substantially reduce the variance of the forward gradient estimator by applying perturbations to activations rather than weights. We further improve the scalability of forward gradient by introducing a large number of local greedy loss functions, each of which involves only a small number of learnable parameters, and a new MLPMixer-inspired architecture, LocalMixer, that is more suitable for local learning. Our approach matches backprop on MNIST and CIFAR-10 and significantly outperforms previously proposed backprop-free algorithms on ImageNet.Comment: 30 pages, tech repor

Measuring Ward-Based Multidisciplinary Healthcare Team Functioning: A Validation Study of the Team Functioning Assessment Tool (TFAT)

The team functioning assessment tool (TFAT) has been shown to be a reliable behavioral marker tool for assessing nontechnical skills that are critical to the success of ward-based healthcare teams. This paper aims to refine and shorten the length of the TFAT to improve usability, and establish its reliability and construct validity. Psychometric testing based on 110 multidisciplinary healthcare teams demonstrated that the TFAT is a reliable and valid tool for measuring team members' nontechnical skills in regards to Clinical Planning, Executive Tasks, and Team Functioning. Providing support for concurrent validity, high TFAT ratings were predicted by low levels of organizational constraints and high levels of group potency. There was also partial support for the negative relationships between time pressure, leadership ambiguity, and TFAT ratings. The paper provides a discussion on the applicability of the tool for assessing multidisciplinary healthcare team functioning in the context of improving team effectiveness and patient safety for ward-based hospital teams

Analysis of the strut and feed blockage effects in radio telescopes with compact UWB feeds

The international radio astronomy community is currently pursuing the development of a giant radio telescope known as the Square Kilometre Array (SKA). The SKA reference design consists of several wideband antenna technologies, including reflector antennas fed with novel multi-beam Phased Array Feeds (PAF) and/or wide band Single Pixel Feeds (SPFs) that can operate at frequencies from 1 to 10 GHz [1], [2]. The baseline of this design represents an array of several hundred to a few thousand reflector antennas of 15-m diameter and that will realize sensitivity of 10,000 m 2/K. During the past years, several different reflector and feed concepts have been proposed and examined, but only a small number of these design options (that have a sufficient level of maturity) will be built and tested in a set-up that is closely resembling the final SKA system [3]. These tests are aimed to evaluate the overall system performance as well as construction and operational costs. The final choices for the dish and feed evaluation tests might include: (i) off-set Gregorian and axi-symmetric reflector antennas and; (ii) an optimized octave corrugated horn and the single-pixel wideband feeds such as quad-ridged horn and Eleven antenna [2], [4]

An energy-efficient off-loading scheme for low latency in collaborative edge computing

Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals' tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as “big task”) may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption

A clinical, biological, and biomaterials perspective into tendon injuries and regeneration

Tendon injury is common and debilitating, and it is associated with long-term pain and ineffective healing. It is estimated to afflict 25% of the adult population and is often a career-ending disease in athletes and racehorses. Tendon injury is associated with high morbidity, pain, and long-term suffering for the patient. Due to the low cellularity and vascularity of tendon tissue, once damage has occurred, the repair process is slow and inefficient, resulting in mechanically, structurally, and functionally inferior tissue. Current treatment options focus on pain management, often being palliative and temporary and ending in reduced function. Most treatments available do not address the underlying cause of the disease and, as such, are often ineffective with variable results. The need for an advanced therapeutic that addresses the underlying pathology is evident. Tissue engineering and regenerative medicine is an emerging field that is aimed at stimulating the body's own repair system to produce de novo tissue through the use of factors such as cells, proteins, and genes that are delivered by a biomaterial scaffold. Successful tissue engineering strategies for tendon regeneration should be built on a foundation of understanding of the molecular and cellular composition of healthy compared with damaged tendon, and the inherent differences seen in the tissue after disease. This article presents a comprehensive clinical, biological, and biomaterials insight into tendon tissue engineering and regeneration toward more advanced therapeutics

Intergranular precipitation and chemical fluctuations in an additively manufactured 2205 duplex stainless steel

Fluctuations in energy distribution during additive manufacturing (AM) can result in spatial and temporal thermal transients. These transients can lead to complexities, most significantly when alloys with multi phases are subjected to AM. Here we unveil such complexities in a duplex stainless steel, where we report an unanticipated formation of a Ni-Mn-Si rich phase at grain boundaries and a local fluctuation in Cr and Fe concentrations in regions close to grain boundaries, providing Cr-rich precursors for Cr2N formation after laser powder bed fusion (LPBF). The formation of these phases is believed to be due to severe thermal gyrations and thermal stresses associated with LPBF resulting in a high-volume fraction of ferrite supersaturated with N and Ni, and a high density of dislocations accelerating diffusion and phase transformations