Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201

Dynamic characteristic analysis of a roller bearing rotor system

In order to study the dynamic characteristics of a rotor system incorporated with roller bearings, a detailed finite element model of the rotor system was established. Both the roller bearings and pedestals were considered. Then investigations on dynamic characteristics of the rotor system were conducted, in which critical speeds and unbalance response of the rotor system were obtained and analyzed. Following conclusions can be reached from the numerical results: 1) the second and third critical speeds of the rotor system are 25260 rpm and 113400 rpm, both of which are outside of the operating speed range 31800 rpm-55320 rpm. 2) Within the operating speed range 31800 rpm-55320 rpm, unbalance response of both the centrifugal impeller and the turbine disk are smaller than the corresponding clearances. However, with increasing rotational speed, amplitude of the unbalance response for the centrifugal impeller increases while that of the turbine disk decreases

Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation

A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior works strictly assume that each source domain shares the identical group of classes with the target domain, which could hardly be guaranteed as the target label space is not observable. In this paper, we consider a more versatile setting of MSDA, namely Generalized Multi-source Domain Adaptation, wherein the source domains are partially overlapped, and the target domain is allowed to contain novel categories that are not presented in any source domains. This new setting is more elusive than any existing domain adaptation protocols due to the coexistence of the domain and category shifts across the source and target domains. To address this issue, we propose a variational domain disentanglement (VDD) framework, which decomposes the domain representations and semantic features for each instance by encouraging dimension-wise independence. To identify the target samples of unknown classes, we leverage online pseudo labeling, which assigns the pseudo-labels to unlabeled target data based on the confidence scores. Quantitative and qualitative experiments conducted on two benchmark datasets demonstrate the validity of the proposed framework

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.Comment: This paper is accepted in AAAI 202

Superconductivity at 22.3 K in SrFe2-xIrxAs2

By substituting the Fe with the 5d-transition metal Ir in SrFe2As2, we have successfully synthesized the superconductor SrFe2-xIrxAs2 with Tc = 22.3 K at x = 0.5. X-ray diffraction indicates that the material has formed the ThCr2Si2-type structure with a space group I4/mmm. The temperature dependence of resistivity and dc magnetization both reveal sharp superconducting transitions at around 22 K. An estimate on the diamagnetization signal reveals a high Meissner shielding volume. Interestingly, the normal state resistivity exhibits a roughly linear behavior up to 300 K. The superconducting transitions at different magnetic fields were also measured yielding a slope of -dHc2/dT = 3.8 T/K near Tc. Using the Werthamer-Helfand-Hohenberg (WHH) formula, the upper critical field at zero K is found to be about 58 T. Counting the possible number of electrons doped into the system in SrFe2-xIrxAs2, we argue that the superconductivity in the Ir-doped system is different from the Co-doped case, which should add more ingredients to the underlying physics of the iron pnictide superconductors.Comment: 4 pages, 4 figure

“What” and “when” predictions modulate auditory processing in a mutually congruent manner

Introduction: Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if “beat” timing predictions selectively interact with “what” predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. Methods: To address these questions, our study manipulated “what” and “when” predictions at different levels – (local) interval-defining and (global) beat-defining – within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Results: Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of “what” predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of “what” and “when” predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Discussion: Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing

"What" and "when" predictions modulate auditory processing in a mutually congruent manner

Introduction: Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if “beat” timing predictions selectively interact with “what” predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. Methods: To address these questions, our study manipulated “what” and “when” predictions at different levels – (local) interval-defining and (global) beat-defining – within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Results: Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of “what” predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of “what” and “when” predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Discussion: Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing