28 research outputs found
A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path
Arising from structural graph theory, treewidth has become a focus of study
in fixed-parameter tractable algorithms in various communities including
combinatorics, integer-linear programming, and numerical analysis. Many NP-hard
problems are known to be solvable in time, where is the treewidth of the input
graph. Analogously, many problems in P should be solvable in time; however, due to the lack of appropriate tools,
only a few such results are currently known. [Fom+18] conjectured this to hold
as broadly as all linear programs; in our paper, we show this is true:
Given a linear program of the form , and a width- tree decomposition of a graph related to , we
show how to solve it in time where is the number of variables and is
the relative accuracy. Combined with recent techniques in vertex-capacitated
flow [BGS21], this leads to an algorithm with run-time. Besides being the first of its
kind, our algorithm has run-time nearly matching the fastest run-time for
solving the sub-problem (under the assumption that no fast matrix
multiplication is used).
We obtain these results by combining recent techniques in interior-point
methods (IPMs), sketching, and a novel representation of the solution under a
multiscale basis similar to the wavelet basis
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
To address the communication burden issues associated with federated learning
(FL), decentralized federated learning (DFL) discards the central server and
establishes a decentralized communication network, where each client
communicates only with neighboring clients. However, existing DFL methods still
suffer from two major challenges: local inconsistency and local heterogeneous
overfitting, which have not been fundamentally addressed by existing DFL
methods. To tackle these issues, we propose novel DFL algorithms, DFedADMM and
its enhanced version DFedADMM-SAM, to enhance the performance of DFL. The
DFedADMM algorithm employs primal-dual optimization (ADMM) by utilizing dual
variables to control the model inconsistency raised from the decentralized
heterogeneous data distributions. The DFedADMM-SAM algorithm further improves
on DFedADMM by employing a Sharpness-Aware Minimization (SAM) optimizer, which
uses gradient perturbations to generate locally flat models and searches for
models with uniformly low loss values to mitigate local heterogeneous
overfitting. Theoretically, we derive convergence rates of and in the non-convex setting for DFedADMM and
DFedADMM-SAM, respectively, where represents the spectral gap of the
gossip matrix. Empirically, extensive experiments on MNIST, CIFAR10 and
CIFAR100 datesets demonstrate that our algorithms exhibit superior performance
in terms of both generalization and convergence speed compared to existing
state-of-the-art (SOTA) optimizers in DFL.Comment: 24 page
User independent Emotion Recognition with Residual Signal-Image Network
User independent emotion recognition with large scale physiological signals
is a tough problem. There exist many advanced methods but they are conducted
under relatively small datasets with dozens of subjects. Here, we propose
Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal
images to classify human emotion. We first apply convex optimization-based EDA
(cvxEDA) to decompose signals and mine the static and dynamic emotion changes.
Then, we transform decomposed signals to images so that they can be effectively
processed by CNN frameworks. The Res-SIN combines individual emotion features
and external emotion benchmarks to accelerate convergence. We evaluate our
approach on the PMEmo dataset, the currently largest emotional dataset
containing music and EDA signals. To the best of author's knowledge, our method
is the first attempt to classify large scale subject-independent emotion with
7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate
the reliability of our model and the binary classification accuracy of 73.65%
and 73.43% on arousal and valence dimension can be used as a baseline
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
Dynamic facial expression recognition (FER) databases provide important data
support for affective computing and applications. However, most FER databases
are annotated with several basic mutually exclusive emotional categories and
contain only one modality, e.g., videos. The monotonous labels and modality
cannot accurately imitate human emotions and fulfill applications in the real
world. In this paper, we propose MAFW, a large-scale multi-modal compound
affective database with 10,045 video-audio clips in the wild. Each clip is
annotated with a compound emotional category and a couple of sentences that
describe the subjects' affective behaviors in the clip. For the compound
emotion annotation, each clip is categorized into one or more of the 11
widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness,
surprise, contempt, anxiety, helplessness, and disappointment. To ensure high
quality of the labels, we filter out the unreliable annotations by an
Expectation Maximization (EM) algorithm, and then obtain 11 single-label
emotion categories and 32 multi-label emotion categories. To the best of our
knowledge, MAFW is the first in-the-wild multi-modal database annotated with
compound emotion annotations and emotion-related captions. Additionally, we
also propose a novel Transformer-based expression snippet feature learning
method to recognize the compound emotions leveraging the expression-change
relations among different emotions and modalities. Extensive experiments on
MAFW database show the advantages of the proposed method over other
state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database
is publicly available from https://mafw-database.github.io/MAFW.Comment: This paper has been accepted by ACM MM'2
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Online video streaming has fundamental limitations on the transmission
bandwidth and computational capacity and super-resolution is a promising
potential solution. However, applying existing video super-resolution methods
to online streaming is non-trivial. Existing video codecs and streaming
protocols (\eg, WebRTC) dynamically change the video quality both spatially and
temporally, which leads to diverse and dynamic degradations. Furthermore,
online streaming has a strict requirement for latency that most existing
methods are less applicable. As a result, this paper focuses on the rarely
exploited problem setting of online streaming video super resolution. To
facilitate the research on this problem, a new benchmark dataset named
LDV-WebRTC is constructed based on a real-world online streaming system.
Leveraging the new benchmark dataset, we proposed a novel method specifically
for online video streaming, which contains a convolution and Look-Up Table
(LUT) hybrid model to achieve better performance-latency trade-off. To tackle
the changing degradations, we propose a mixture-of-expert-LUT module, where a
set of LUT specialized in different degradations are built and adaptively
combined to handle different degradations. Experiments show our method achieves
720P video SR around 100 FPS, while significantly outperforms existing
LUT-based methods and offers competitive performance compared to efficient
CNN-based methods
Stress in Regulation of GABA Amygdala System and Relevance to Neuropsychiatric Diseases
The amygdala is an almond-shaped nucleus located deep and medially within the temporal lobe and is thought to play a crucial role in the regulation of emotional processes. GABAergic neurotransmission inhibits the amygdala and prevents us from generating inappropriate emotional and behavioral responses. Stress may cause the reduction of the GABAergic interneuronal network and the development of neuropsychological diseases. In this review, we summarize the recent evidence investigating the possible mechanisms underlying GABAergic control of the amygdala and its interaction with acute and chronic stress. Taken together, this study may contribute to future progress in finding new approaches to reverse the attenuation of GABAergic neurotransmission induced by stress in the amygdala
Sensors and Data Processing Techniques for Future Medicine
Varieties of innovative and high precision sensors have been developed and became available for versatile application. Such sensors, when combined with data processing techniques of artificial intelligence, can make a huge impact on healthcare technologies. That is, a system can screen symptoms such as infection, cardiovascular failure, and major depressive disorders, just as experienced physicians diagnose with stethoscope and percussion.Published versio