Private learning implies quantum stability

Learning an unknown $n$-qubit quantum state $\rho$ is a fundamental challenge in quantum computing. Information-theoretically, it is known that tomography requires exponential in $n$ many copies of $\rho$ to estimate it up to trace distance. Motivated by computational learning theory, Aaronson et al. introduced many (weaker) learning models: the PAC model of learning states (Proceedings of Royal Society A'07), shadow tomography (STOC'18) for learning "shadows" of a state, a model that also requires learners to be differentially private (STOC'19) and the online model of learning states (NeurIPS'18). In these models it was shown that an unknown state can be learned "approximately" using linear-in-$n$ many copies of rho. But is there any relationship between these models? In this paper we prove a sequence of (information-theoretic) implications from differentially-private PAC learning, to communication complexity, to online learning and then to quantum stability. Our main result generalizes the recent work of Bun, Livni and Moran (Journal of the ACM'21) who showed that finite Littlestone dimension (of Boolean-valued concept classes) implies PAC learnability in the (approximate) differentially private (DP) setting. We first consider their work in the real-valued setting and further extend their techniques to the setting of learning quantum states. Key to our results is our generic quantum online learner, Robust Standard Optimal Algorithm (RSOA), which is robust to adversarial imprecision. We then show information-theoretic implications between DP learning quantum states in the PAC model, learnability of quantum states in the one-way communication model, online learning of quantum states, quantum stability (which is our conceptual contribution), various combinatorial parameters and give further applications to gentle shadow tomography and noisy quantum state learning.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.

Cascade Image Matting with Deformable Graph Refinement

Image matting refers to the estimation of the opacity of foreground objects. It requires correct contours and fine details of foreground objects for the matting results. To better accomplish human image matting tasks, we propose the Cascade Image Matting Network with Deformable Graph Refinement, which can automatically predict precise alpha mattes from single human images without any additional inputs. We adopt a network cascade architecture to perform matting from low-to-high resolution, which corresponds to coarse-to-fine optimization. We also introduce the Deformable Graph Refinement (DGR) module based on graph neural networks (GNNs) to overcome the limitations of convolutional neural networks (CNNs). The DGR module can effectively capture long-range relations and obtain more global and local information to help produce finer alpha mattes. We also reduce the computation complexity of the DGR module by dynamically predicting the neighbors and apply DGR module to higher--resolution features. Experimental results demonstrate the ability of our CasDGR to achieve state-of-the-art performance on synthetic datasets and produce good results on real human images.

Domestic activities clustering from audio recordings using convolutional capsule autoencoder network

Recent efforts have been made on domestic activities classification from audio recordings, especially the works submitted to the challenge of DCASE (Detection and Classification of Acoustic Scenes and Events) since 2018. In contrast, few studies were done on domestic activities clustering, which is a newly emerging problem. Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster. Domestic activities clustering is an effective way for unsupervised estimation of daily activities performed in home environment. In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). In the method, the deep embeddings are learned by the autoencoder in the CCAN, while the deep embeddings which belong to the same class of domestic activities are merged into a single cluster by a clustering layer in the CCAN. Evaluated on a public dataset adopted in DCASE-2018 Task 5, the results show that the proposed method outperforms state-of-the-art methods in terms of the metrics of clustering accuracy and normalized mutual information.

A Multi-parameter Persistence Framework for Mathematical Morphology

The field of mathematical morphology offers well-studied techniques for image processing. In this work, we view morphological operations through the lens of persistent homology, a tool at the heart of the field of topological data analysis. We demonstrate that morphological operations naturally form a multiparameter filtration and that persistent homology can then be used to extract information about both topology and geometry in the images as well as to automate methods for optimizing the study and rendering of structure in images. For illustration, we apply this framework to analyze noisy binary, grayscale, and color images.

Tighter expected generalization error bounds via Wasserstein distance

In this work, we introduce several expected generalization error bounds based on the Wasserstein distance. More precisely, we present full-dataset, single-letter, and random-subset bounds on both the standard setting and the randomized-subsample setting from Steinke and Zakynthinou [2020]. Moreover, we show that, when the loss function is bounded, these bounds recover from below (and thus are tighter than) current bounds based on the relative entropy and, for the standard setting, generate new, non-vacuous bounds also based on the relative entropy. Then, we show how similar bounds featuring the backward channel can be derived with the proposed proof techniques. Finally, we show how various new bounds based on different information measures (e.g., the lautum information or several $f$-divergences) can be derived from the presented bounds.

Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems

Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently. Most of the previous works select a suitable response only according to the matching degree between the query and each individual candidate response. Although good performance has been achieved, these recent works ignore the comparison among the candidate responses, which could provide rich information for selecting the most appropriate response. Intuitively, better decisions could be made when the models can get access to the comparison information among all the candidate responses. In order to leverage the comparison information among the candidate responses, in this paper, we propose a novel and plug-in Self-attention Comparison Module for retrieval-based open-domain dialog systems, called SCM. Extensive experiment results demonstrate that our proposed self-attention comparison module effectively boosts the performance of the existing retrieval-based open-domain dialog systems. Besides, we have publicly released our source codes for future research.

Multiscale Anisotropic Harmonic Filters on non Euclidean domains

This paper introduces Multiscale Anisotropic Harmonic Filters (MAHFs) aimed at extracting signal variations over non-Euclidean domains, namely 2D-Manifolds and their discrete representations, such as meshes and 3D Point Clouds as well as graphs. The topic of pattern analysis is central in image processing and, considered the growing interest in new domains for information representation, the extension of analogous practices on volumetric data is highly demanded. To accomplish this purpose, we define MAHFs as the product of two components, respectively related to a suitable smoothing function, namely the heat kernel derived from the heat diffusion equations, and to local directional information. We analyse the effectiveness of our approach in multi-scale filtering and variation extraction. Finally, we present an application to the surface normal field and to a luminance signal textured to a mesh, aiming to spot, in a separate fashion, relevant curvature changes (support variations) and signal variations.

Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases

Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the diagnosis of apple diseases. Specifically, this paper addresses the problem of sequentially optimizing for the best diagnosis, leveraging past interactions with the system and their contextual information (i.e. the evidence provided by the users). The problem of learning an online model while optimizing for its outcome is commonly addressed in the literature through a stochastic active learning paradigm - i.e. Contextual Multi-Armed Bandit (CMAB). This methodology interactively updates the decision model considering the success of each past interaction with respect to the context provided in each round. However, this information is very often partial and inadequate to handle such complex decision making problems. On the other hand, human decisions implicitly include unobserved factors (referred in the literature as unobserved confounders) that significantly contribute to the human's final decision. In this paper, we take advantage of the information embedded in the observed human decisions to marginalize confounding factors and improve the capability of the CMAB model to identify the correct diagnosis. Specifically, we propose a Counterfactual Contextual Multi-Armed Bandit, a model based on the causal concept of counterfactual. The proposed model is validated with offline experiments based on data collected through a large user study on the application. The results prove that our model is able to outperform both traditional CMAB algorithms and observed user decisions, in real-world tasks of predicting the correct apple disease.

Personal Productivity and Well-being -- Chapter 2 of the 2021 New Future of Work Report

We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.