Spectral and Spatial Dependence of Diffuse Optical Signals in Response to Peripheral Nerve Stimulation

Using non-invasive, near-infrared spectroscopy we have previously reported optical signals measured at or around peripheral nerves in response to their stimulation. Such optical signals featured amplitudes on the order of 0.1% and peaked about 100 ms after peripheral nerve stimulation in human subjects. Here, we report a study of the spatial and spectral dependence of the optical signals induced by stimulation of the human median and sural nerves, and observe that these optical signals are: (1) unlikely due to either dilation or constriction of blood vessels, (2) not associated with capillary bed hemoglobin, (3) likely due to blood vessel(s) displacement, and (4) unlikely due to fiber-skin optical coupling effects. We conclude that the most probable origin of the optical response to peripheral nerve stimulation is from displacement of blood vessels within the optically probed volume, as a result of muscle twitch in adjacent areas.National Institutes of Health (R01-NS059933); U.S. Army Medical Acquisition Activity (W81XWH-07-2-0011

Dynamics of Transport Infrastructure, Exports and Economic Growth in the United States

This paper focuses on the dynamic relationships among transport infrastructure, exports and economic growth in the United States using a multivariate time-series analysis. Results suggest that the formation of highways and streets affects economic growth indirectly through enhancing the capital stock of non-transport infrastructure and crowding in private capital. The reverse causality from economic output to highway and street infrastructure is observed. Aggregate capital stock of non-transport infrastructure, excluding national defense, has sustainable positive effects on economic output and exports over a number of years. Empirical evidence also shows that highway and street infrastructure and non-transport infrastructure Granger cause exports

Detail-Preserving Controllable Deformation from Sparse Examples

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Antenna enhanced graphene THz emitter and detector

Recent intense electrical and optical studies of graphene have pushed the material to the forefront of optoelectronic research. Of particular interest is the few terahertz (THz) frequency regime where efficient light sources and highly sensitive detectors are very challenging to make. Here we present THz sources and detectors made with graphene field effect transistors (GFETs) enhanced by a double-patch antenna and an on-chip silicon lens. We report the first experimental observation of 1-3 THz radiation from graphene, as well as four orders of magnitude performance improvements in a GFET thermoelectric detector operating at ~2 THz. The quantitative analysis of the emitting power and its unusual charge density dependence indicate significant non-thermal contribution from the GFET. The polarization resolved detection measurements with different illumination geometries allow for detailed and quantitative analysis of various factors that contribute to the overall detector performance. Our experimental results represent a significant advance towards practically useful graphene THz devices

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.</p

A Unified Quantum NOT Gate

We study the feasibility of implementing a quantum NOT gate (approximate) when the quantum state lies between two latitudes on the Bloch's sphere and present an analytical formula for the optimized 1-to-$M$ quantum NOT gate. Our result generalizes previous results concerning quantum NOT gate for a quantum state distributed uniformly on the whole Bloch sphere as well as the phase covariant quantum state. We have also shown that such 1-to-$M$ optimized NOT gate can be implemented using a sequential generation scheme via matrix product states (MPS)

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. Previous work on fair GNN models and techniques are discussed in terms of whether they focus on improving fairness during a preprocessing step, during training, or in a post-processing phase. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed

Impact of coronavirus disease 2019 (COVID-19) outbreak quarantine, isolation, and lockdown policies on mental health and suicide

The novel coronavirus disease (COVID-19) pandemic has made a huge impact on people\u27s physical and mental health, and it remains a cause of death for many all over the world. To prevent the spread of coronavirus infection, different types of public health measures (social isolation, quarantine, lockdowns, and curfews) have been imposed by governments. However, mental health experts warn that the prolonged lockdown, quarantine, or isolation will create a “second pandemic” with severe mental health issues and suicides. The quarantined or isolated people may suffer from various issues such as physical inactivity, mental health, economic and social problems. As with the SARS outbreak in 2003, many suicide cases have been reported in connection with this current COVID-19 pandemic lockdown due to various factors such as social stigma, alcohol withdrawal syndrome, fear of COVID infection, loneliness, and other mental health issues. This paper provides an overview of risk factors that can cause suicide and outlines possible solutions to prevent suicide in this current COVID-19 pandemic

Bias and Fairness in Large Language Models: A Survey

Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere. Despite this success, these models can learn, perpetuate, and amplify harmful social biases. In this paper, we present a comprehensive survey of bias evaluation and mitigation techniques for LLMs. We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing, defining distinct facets of harm and introducing several desiderata to operationalize fairness for LLMs. We then unify the literature by proposing three intuitive taxonomies, two for bias evaluation, namely metrics and datasets, and one for mitigation. Our first taxonomy of metrics for bias evaluation disambiguates the relationship between metrics and evaluation datasets, and organizes metrics by the different levels at which they operate in a model: embeddings, probabilities, and generated text. Our second taxonomy of datasets for bias evaluation categorizes datasets by their structure as counterfactual inputs or prompts, and identifies the targeted harms and social groups; we also release a consolidation of publicly-available datasets for improved access. Our third taxonomy of techniques for bias mitigation classifies methods by their intervention during pre-processing, in-training, intra-processing, and post-processing, with granular subcategories that elucidate research trends. Finally, we identify open problems and challenges for future work. Synthesizing a wide range of recent research, we aim to provide a clear guide of the existing literature that empowers researchers and practitioners to better understand and prevent the propagation of bias in LLMs