It has been recently observed that neural networks, unlike kernel methods, enjoy a reduced sample complexity when the distribution is isotropic (i.e., when the covariance matrix is the identity). We find that this sensitivity to the data distribution is not exclusive to neural networks, and the same phenomenon can be observed on the class of quadratic classifiers (i.e., the sign of a quadratic polynomial) with a nuclear-norm constraint. We demonstrate this by deriving an upper bound on the Rademacher Complexity that depends on two key quantities: (i) the intrinsic dimension, which is a measure of isotropy, and (ii) the largest eigenvalue of the second moment (covariance) matrix of the distribution. Our result improves the dependence on the dimension over the best previously known bound and precisely quantifies the relation between the sample complexity and the level of isotropy of the distribution.

We study the effect of stochasticity in on-policy policy optimization, and make the following four contributions.

While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter. In this paper, we tackle this problem by building on a modification of the generative adversarial networks (GANs) framework. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. The key idea is to use a significantly modified GAN model to learn to generate counterfactual outcomes, which can then be used to learn an inference model, using standard supervised methods, capable of estimating these counterfactuals for a new sample. To address the challenges presented by shifting to continuous interventions, we propose a novel architecture for our discriminator - we build a hierarchical discriminator that leverages the structure of the continuous intervention setting. Moreover, we provide theoretical results to support our use of the GAN framework and of the hierarchical discriminator. In the experiments section, we introduce a new semi-synthetic data simulation for use in the continuous intervention setting and demonstrate improvements over the existing benchmark models.

Autonomous agents that control user interfaces to accomplish human tasks are emerging. Leveraging LLMs to power such agents has been of special interest, but unless fine-tuned on human-collected task demonstrations, performance is still relatively low. In this work we study whether fine-tuning alone is a viable approach for building real-world UI control agents. To this end we collect and release a new dataset, AndroidControl, consisting of 15,283 demonstrations of everyday tasks with Android apps. Compared to existing datasets, each AndroidControl task instance includes both high and low-level human-generated instructions, allowing us to explore the level of task complexity an agent can handle.

In this section, in addition to multimodal feature refinement described in the main manuscript, we present three different strategies to construct item-item similarity graphs for textual and visual modalities, as edge weights in each similarity graph are not naturally defined unlike the case of user-item interactions. A. Cosine Similarity Cosine similarity is one of the straightforward approach to calculating similarity between two vectors. We perform kNN sparsification [?] to extract high similarity scores in the similarity matrix: top-k(S B. Pearson Correlation Coefficient Pearson correlation coefficient [?] can be adopted to construct item-item similarity graphs for multiple modalities. C. Gaussian Kernel According to [?], item-item similarity graphs can be constructed using a Gaussian kernel:) ( The best and second-best performers are highlighted in bold and underline, respectively. Figure 1: The effect of β and γ hyperparameters for three benchmark datasets, where the horizontal and vertical axes indicate the value of each hyperparameter and the performance in NDCG@20, respectively.

It has been wide discussed on how to develop algorithms allow large batches, so that one could train neural networks in a distributed environment. The paper investigates the effect of network width on the performance of large-batch training both theoretically and experimentally. The authors claim that with the same number of parameters, it is more likely to train neural networks using proper large batches easily with a wide network architecture. The theoretical support on 2-layers linear/nonlinear networks and multilayer linear networks is also given. The paper is well-written and easy to follow.

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership.

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 20, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 20, 2022 NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK Spettroscopia e Chemiometria Weekly News 20, 2022 NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK Near-Infrared Spectroscopy (NIRS) "Agriculture : Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture" LINK "Blood discrimination based on NIR spectroscopy and BP neural network combined with genetic algorithm" LINK "Automated surface mapping via unsupervised learning and classification of Mercury Visible-Near-Infrared reflectance spectra" LINK "At-line and inline prediction of droplet size in mayonnaise with near-infrared spectroscopy" LINK "Use of near-infrared spectroscopy and chemometrics for fast discrimination of Sargassum fusiforme" LINK " Chemometric studies of hops degradation at different storage forms using UVVis, NIRS and UPLC analyses" LINK "Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour" LINK "Foods : Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data" LINK "Near infrared spectroscopy to evaluate the effect of a hybrid exercise programme on peripheral muscle metabolism in patients with intermittent claudication: an …" LINK "Plants : Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy" LINK "Teknologi Near Infrared Reflectance Spectroscopy (NIRS) dan Metode Kemometri untuk Deteksi Pemalsuan Minyak Nilam" LINK "Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue--A Salmon Case Study" LINK "Multi-information based on ATR-FTIR and FT-NIR for identification and evaluation for different parts and harvest time of Dendrobium officinale with chemometrics" LINK "Application of near infrared spectroscopy to predict contents of various lactones in chromatographic process of Ginkgo Folium" LINK "A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy" LINK "The application of NIR spectroscopy in moisture determining of vegetable seeds" seedtesting seedquality NIRS methodDevelopment Calibration solution content analysis seed grain grains LINK "Aplicación de imágenes hiperespectrales (HSI-NIR) para la determinación de estrés hídrico en hojas de patata." LINK "Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks" LINK "Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea" LINK "Prediction of Wheat Quality Parameters Combining Raman, Fluorescence and Near‐Infrared Spectroscopy (NIRS)" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging" LINK "Single-domain nearinfrared protein provides a scaffold for antigen-dependent fluorescent nanobodies" LINK "Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK "Application of infrared spectroscopic techniques to cheese authentication: A review" LINK "Estimation of grain quality parameters in rice for highthroughput screening with nearinfrared spectroscopy and deep learning" LINK Hyperspectral Imaging (HSI) "Multispectral camera system design for replacement of hyperspectral cameras for detection of aflatoxin B1" LINK "Hyperspectral Imaging for cherry tomato" LINK "A novel 3D convolutional neural network model with supervised spectral regression for recognition of hyperspectral images of colored wool fiber" LINK "Channel and band attention embedded 3D CNN for model development of hyperspectral image in object-scale analysis" LINK "A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits" LINK "Learning Multiscale Temporal-Spatial-Spectral Features via a Multi-path Convolutional LSTM Neural Network for Change Detection with Hyperspectral Images" LINK "The relationship between the spatial pattern of lakeside wetlands and water quality utilizing UAV hyperspectral remote sensing" LINK "Building spectral catalogue for salt marsh vegetation, hyperspectral and multispectral remote sensing" LINK "Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review" LINK "A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications" LINK "Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process" LINK "Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview" LINK Chemometrics and Machine Learning "Chemometrics for Raman Spectroscopy Harmonization" LINK "Determination of active ingredients in alcoholbased gel by spectroscopic techniques and chemometric analysis" LINK "Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data" LINK Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding project page: sota FID(7.27 on COCO), without ever training on COCO, human raters find Imagen samples to be on par with the COCO data itself in image-text alignment LINK "Validação prática de modelos de infravermelho próximo para tomate: sólidos solúveis e acidez" LINK Facts "Does active sitting provide more physiological changes than traditional sitting and standing workstations?"

The study examines the trade-offs in using automated technology to limit the spread of disinformation online. It presents options (from self-regulatory to legislative) to regulate automated content recognition (ACR) technologies in this context. Special attention is paid to the opportunities for the European Union as a whole to take the lead in setting the framework for designing these technologies in a way that enhances accountability and transparency and respects free speech. The present project reviews some of the key academic and policy ideas on technology and disinformation and highlights their relevance to European policy. Chapter 1 introduces the background to the study and presents the definitions used.

Summary: Immune system mediated injury rather than the virus entering and killing brain cells may explain why people experience long-term consequences associated with COVID-19 infection. COVID-19 may be primarily a respiratory illness, but its reach extends far beyond the lungs. Since the pandemic's onset, it has become clear to neurologists that the pervasive disease can impact even our most precious organ--the brain. The neurologic and psychiatric complications of COVID-19 are incredibly diverse and sometimes persist long after patients recover from their initial infections. Studying the mechanisms behind how these complications arise is urgently needed for helping those struggling with lingering symptoms, writes Serena Spudich, MD, Gilbert H. Glaser Professor of Neurology, in her "Perspective" article published in Science on January 20.