Seq2seq is All You Need for Coreference Resolution

Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that such models are not necessary. We finetune a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation. Despite the extreme simplicity, our model outperforms or closely matches the best coreference systems in the literature on an array of datasets. We also propose an especially simple seq2seq approach that generates only tagged spans rather than the spans interleaved with the original text. Our analysis shows that the model size, the amount of supervision, and the choice of sequence representations are key factors in performance.Comment: EMNLP 202

Vehicle and crew scheduling : the general problem and a case study of the MBTA

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1995.Includes bibliographical references (p. 163-172).by Wen-Cheng Chang.M.S

AF17 Facilitates Dot1a Nuclear Export and Upregulates ENaC-Mediated Na+ Transport in Renal Collecting Duct Cells

Our previous work in 293T cells and AF17-/- mice suggests that AF17 upregulates expression and activity of the epithelial Na+ channel (ENaC), possibly by relieving Dot1a-AF9-mediated repression. However, whether and how AF17 directly regulates Dot1a cellular distribution and ENaC function in renal collecting duct cells remain unaddressed. Here, we report our findings in mouse cortical collecting duct M-1 cells that overexpression of AF17 led to preferential distribution of Dot1a in the cytoplasm. This effect could be blocked by nuclear export inhibitor leptomycin B. siRNA-mediated depletion of AF17 caused nuclear accumulation of Dot1a. AF17 overexpression elicited multiple effects that are reminiscent of aldosterone action. These effects include 1) increased mRNA and protein expression of the three ENaC subunits (α, β and γ) and serum- and glucocorticoid inducible kinase 1, as revealed by real-time RT-qPCR and immunoblotting analyses; 2) impaired Dot1a-AF9 interaction and H3 K79 methylation at the αENaC promoter without affecting AF9 binding to the promoter, as evidenced by chromatin immunoprecipitation; and 3) elevated ENaC-mediated Na+ transport, as analyzed by measurement of benzamil-sensitive intracellular [Na+] and equivalent short circuit current using single-cell fluorescence imaging and an epithelial Volt-ohmmeter, respectively. Knockdown of AF17 elicited opposite effects. However, combination of AF17 overexpression or depletion with aldosterone treatment did not cause an additive effect on mRNA expression of the ENaC subunits. Taken together, we conclude that AF17 promotes Dot1a nuclear export and upregulates basal, but not aldosterone-stimulated ENaC expression, leading to an increase in ENaC-mediated Na+ transport in renal collecting duct cells

Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the \u27freshness\u27 of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than B. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio 4 4/7 for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average

Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.Comment: Accepted by CVPR 202

Relightable Neural Human Assets from Multi-view Gradient Illuminations

Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are not readily used in relighting problems. To promote research in both fields, in this paper, we present UltraStage, a new 3D human dataset that contains more than 2,000 high-quality human assets captured under both multi-view and multi-illumination settings. Specifically, for each example, we provide 32 surrounding views illuminated with one white light and two gradient illuminations. In addition to regular multi-view images, gradient illuminations help recover detailed surface normal and spatially-varying material maps, enabling various relighting applications. Inspired by recent advances in neural representation, we further interpret each example into a neural human asset which allows novel view synthesis under arbitrary lighting conditions. We show our neural human assets can achieve extremely high capture performance and are capable of representing fine details such as facial wrinkles and cloth folds. We also validate UltraStage in single image relighting tasks, training neural networks with virtual relighted data from neural assets and demonstrating realistic rendering improvements over prior arts. UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks. The dataset is available at https://miaoing.github.io/RNHA.Comment: Project page: https://miaoing.github.io/RNH

Comparison of initial learning algorithms for long short-term memory method on real-time respiratory signal prediction

AimThis study aimed to examine the effect of the weight initializers on the respiratory signal prediction performance using the long short-term memory (LSTM) model.MethodsRespiratory signals collected with the CyberKnife Synchrony device during 304 breathing motion traces were used in this study. The effectiveness of four weight initializers (Glorot, He, Orthogonal, and Narrow-normal) on the prediction performance of the LSTM model was investigated. The prediction performance was evaluated by the normalized root mean square error (NRMSE) between the ground truth and predicted respiratory signal.ResultsAmong the four initializers, the He initializer showed the best performance. The mean NRMSE with 385-ms ahead time using the He initializer was superior by 7.5%, 8.3%, and 11.3% as compared to that using the Glorot, Orthogonal, and Narrow-normal initializer, respectively. The confidence interval of NRMSE using Glorot, He, Orthogonal, and Narrow-normal initializer were [0.099, 0.175], [0.097, 0.147], [0.101, 0.176], and [0.107, 0.178], respectively.ConclusionsThe experiment results in this study indicated that He could be a valuable initializer in the LSTM model for the respiratory signal prediction

AF17 Competes With AF9 for Binding to DOT1A to up-Regulate Transcription of Epithelial NA\u3csup\u3e+\u3c/sup\u3e Channel α

We previously reported that Dot1a*AF9 complex represses transcription of the epithelial Na+ channel subunit α (α-ENaC) gene in mouse inner medullary collecting duct mIMCD3 cells and mouse kidney. Aldosterone relieves this repression by down-regulating the complex through various mechanisms. Whether these mechanisms are sufficient and conserved in human cells or can be applied to other aldosterone-regulated genes remains largely unknown. Here we demonstrate that human embryonic kidney 293T cells express the three ENaC subunits and all of the ENaC transcriptional regulators examined. These cells respond to aldosterone and display benzamil-sensitive Na+ currents, as measured by whole-cell patch clamping. We also show that AF17 and AF9 competitively bind to the same domain of Dot1a in multiple assays and have antagonistic effects on expression of an α-ENaC promoter-luciferase construct. Overexpression of Dot1a or AF9 decreased mRNA expression of the ENaC subunits and their transcriptional regulators and reduced benzamil-sensitive Na+ currents. AF17 over-expression caused the opposite effects, accompanied by redirection of Dot1a from the nucleus to the cytoplasm and reduction in histone H3 K79 methylation. The nuclear export inhibitor leptomycin B blocked the effect of AF17 overexpression on H3 K79 hypomethylation. RNAi-mediated knockdown of AF17 yielded nuclear enrichment of Dot1a and histone H3 K79 hypermethylation. As with AF9, AF17 displays nuclear and cytoplasmic co-localization with Sgk1. Therefore, AF17 competes with AF9 to bind Dot1a, decreases Dot1a nuclear expression by possibly facilitating its nuclear export, and relieves Dot1a*AF9-mediated repression of α-ENaC and other target genes

A multi-agent-based approach for the impacts analysis of passenger flow on platforms in metro stations considering train operations

Impacts analysis of train operation on passenger flow in metro stations is an important and fundamental requirement to improve the operational efficiency and ensure passengers a high level of service. This study aims at large metro stations where thousands of passengers are moving, boarding or alighting and the complicated interactions among passengers and between passengers and other entities like stairways or trains take place all the time. A multi-agent-based approach is developed from the investigation of movement characteristics of passengers to meet the above requirement and deal with such interactions. The simulation scenarios considering the various conditions of train operations are performed in the case studies of a metro station in Beijing (China) to prove the feasibility of the proposed approach, which is useful to formulate and evaluate the operation schemes of trains