Miracle-dz/UniFAR-FTUs huggingface dataset:
A slow blow up solution for the four dimensional energy critical semi linear heat equation

A BSTRACT . We consider the energy critical four dimensional semi-linear heat equation Formal computation of Filippas et al. (R. Soc. Lond. Proc. 2000) conjectures the existence of a sequence of type II blow-up solutions with various blow-up rates Schweyer (J. Funct. Anal. 2012) rigorously constructs a type II blow-up solution for the case L = 1 . In this paper, we show the existence of type II blow-up solution for L = 2 . The method here could be generalized to deal with all the cases L ≥ 2 .
Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management. " To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.
Latency Analysis of Vehicle-to-Pedestrian C-V2X Communications at Urban Street Intersections

Cellular Vehicle-to-Everything (C-V2X) technology promises to provide ultra-reliable low latency communication (URLLC) framework for connected vehicles. Connected vehicles can help us improve traffic safety, congestion and reduce fatal accidents. C-V2X promises new levels of connectivity and intelligence providing numerous features like infotainment, always-on telematics, real-time navigation, etc. with heterogeneous network architecture. C-V2X leverages existing cellular architecture and is on the way to replace DSRC/WAVE for vehicular communication. There are still some challenges which needs to be tackled before C-V2X can be deployed for efficient on-road V2X. In this paper, we look at end-to-end latency for Vehicle-to-Pedestrian (V2P) which demands ultra low latencies with reliable packet delivery ratio. To evaluate the LTE-V2P performance, we used network simulator for an urban intersection scenario where vulnerable road-side users (VRUs) are trying to cross the street and they communicate with moving vehicles. We evaluated the end-to-end latency and throughput for the given scenario and concluded that with the existing network architecture the latency is high. By utilizing multi-edge access computing (MEC) servers latency can be reduce drastically and hence can be made feasible for cellular V2P communication.
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization

This paper investigates a method for simulating natural conversation in the model training of end-to-end neural diarization (EEND). Due to the lack of any annotated real conversational dataset, EEND is usually pretrained on a large-scale simulated conversational dataset ﬁrst and then adapted to the target real dataset. Simulated datasets play an essential role in the training of EEND, but as yet there has been insufﬁcient investigation into an optimal simulation method. We thus propose a method to simulate natural conversational speech. In contrast to conventional methods, which simply combine the speech of multiple speakers, our method takes turn-taking into account. We deﬁne four types of speaker transition and sequentially arrange them to simulate natural conversations. The dataset simulated using our method was found to be statistically similar to the real dataset in terms of the silence and overlap ratios. The experimental results on two-speaker diarization using the CALLHOME and CSJ datasets showed that the simulated dataset contributes to improving the performance of EEND.
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News

Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that focus more on linguistics. Many guidelines restrict themselves to include only explicit relations or clause-based arguments. Therefore, we propose an annotation schema for event causality that addresses these concerns. We annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. Our corpus is known as the Causal News Corpus (CNC). A neural network built upon a state-of-the-art pre-trained language model performed well with 81.20% F1 score on test set, and 83.46% in 5-folds cross-validation. CNC is transferable across two external corpora: CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning. CNC also served as an effective training and pre-training dataset for the two external corpora. Lastly, we demonstrate the difficulty of our task to the layman in a crowd-sourced annotation exercise. Our annotated corpus is publicly available, providing a valuable resource for causal text mining researchers.
Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders

We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximation of maximum likelihood channel estimation. We generalize the concept of variational autoencoder (VAE) equalizers to higher order modulation formats encompassing probabilistic constellation shaping (PCS), ubiquitous in optical communications, oversampling at the receiver, and dual-polarization transmission. Besides black-box equalizers based on convolutional neural networks, we propose a model-based equalizer based on a linear butterfly filter and train the filter coefficients using the variational inference paradigm. As a byproduct, the VAE also provides a reliable channel estimation. We analyze the VAE in terms of performance and flexibility over a classical additive white Gaussian noise (AWGN) channel with inter-symbol interference (ISI) and over a dispersive linear optical dual-polarization channel. We show that it can extend the application range of blind adaptive equalizers by outperforming the state-of-the-art constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying channels. The evaluation is accompanied with a hyperparameter analysis.
Efficient Neural Neighborhood Search for Pickup and Delivery Problems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

This paper presents a new method for solving an orienteering problem (OP) by breaking it down into two parts: a knapsack problem (KP) and a traveling salesman problem (TSP). A KP solver is responsible for picking nodes, while a TSP solver is responsible for designing the proper path and assisting the KP solver in judging constraint violations. To address constraints, we propose a dual-population coevolutionary algorithm (DPCA) as the KP solver, which simultaneously maintains both feasible and infeasible populations. A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes. The model, which is trained by reinforcement learning, can capture both the structural and dynamic patterns of the given problem. The model can generalize to other instances with different scales and distributions. Experimental results show that the proposed algorithm can outperform conventional approaches in terms of training, inference, and generalization ability.
A Multiwavelength Study of the Highly Asymmetrical Debris Disk around HD 111520

We observed the nearly edge-on debris disk system HD 111520 at the HJ and K1 near-infrared (NIR) bands using both the spectral and polarization modes of the Gemini Planet Imager. With these new observations, we have performed an empirical analysis in order to better understand the disk morphology and its highly asymmetrical nature. We find that the disk features a large brightness and radial asymmetry, most prominent at shorter wavelengths. We also find that the radial location of the peak polarized intensity differs on either side of the star by 11 au, suggesting that the disk may be eccentric, although, such an eccentricity does not fully explain the large brightness and radial asymmetry observed. Observations of the disk halo with the Hubble Space Telescope also show the disk to be warped at larger separations, with a bifurcation feature in the northwest, further suggesting that there may be a planet in this system creating an asymmetrical disk structure. Measuring the disk color shows that the brighter extension is bluer compared to the dimmer extension, suggesting that the two sides have different dust grain properties. This finding, along with the large brightness asymmetry, are consistent with the hypothesis that a giant impact occurred between two large bodies in the northern extension of the disk, although confirming this based on NIR observations alone is not feasible. Follow-up imaging with the Atacama Large Millimeter/submillimeter Array to resolve the asymmetry in the dust mass distribution is essential in order to confirm this scenario.
Effect of underbody diffuser on the aerodynamic drag of vehicles in convoy

Abstract Presented work is extension to previous studies of convoying where prime focus was to study effect of intervehicular spacing and upperbody geometry of vehicles on aerodynamics of convoy. Current study examines the effect of underbody diffuser on coefficient of drag (Cd) of convoy of two reference car bodies (Ahmed body). CFD analysis of convoy is done using Shear-Stress-Transport model under moving ground conditions. The length of lead body’s diffuser is taken as 0.222 m with diffuser angle (degrees) of 0 (no diffuser), 3, 5, 7, 9, 15, 20, 25 and 30 each at intervehicular spacing of 0.25 and 0.75 Ahmed body length. Each configuration thus resulting was analyzed with lead body backlite angle of 25° (pre-critical) and 35° (post-critical) with follow body’s backlite angle remaining 25°. CFD analysis are conducted after performing two validation analyses from previous studies. To understand the flow features developed on Ahmed body due to an underbody diffuser a preliminary analysis is done on an isolated body with 25° and 35° backlite angles by applying each diffuser angle used in current study. After analysis of convoy, drag on lead and follow vehicles is found to be also dependent on the axial vortices formed due to diffuser in addition to those from backlite surface of lead body. Average drag on cases with diffuser is found to be lesser than the no diffuser cases up to a certain diffuser angle. Thus applying diffuser has a potential for reducing the overall drag on convoy.
Safe Delivery of Critical Services in Areas with Volatile Security Situation via a Stackelberg Game Approach

Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threat-ening. The current COVID pandemic and the need to vaccinate have added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vacci-nation) centers that balance physical security and desired (vaccine) service coverage with limited resources. We set-up the problem as a Stackelberg game between the centers operator (defender) and an adversary, where the set of centers is not ﬁxed a pri-ori but is part of the decision output. This results in a mixed combinatorial and continuous optimization problem. As part of our scalable approximation of this problem, we provide a fundamental contribution by identifying general duality conditions of switching max and min when both discrete and continuous variables are involved. We perform detailed experiments to show that the solution proposed is scalable in practice.
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

Session-based recommender systems (SBR) are becoming increasingly popular because they can predict user interests without relying on long-term user profile and support login-free recommendation. Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental context to the server, raising the public concern about privacy. On-device recommender systems circumvent these two issues with cost-conscious settings and local inference. However, due to the limited memory and computing resources, on-device recommender systems are confronted with two fundamental challenges: (1) how to reduce the size of regular models to fit edge devices? (2) how to retain the original capacity? Previous research mostly adopts tensor decomposition techniques to compress regular recommendation models with low compression rates so as to avoid drastic performance degradation. In this paper, we explore ultra-compact models for next-item recommendation, by loosing the constraint of dimensionality consistency in tensor decomposition. To compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher). The extensive experiments on two benchmarks demonstrate that, with 30x size reduction, the compressed model almost comes with no accuracy loss, and even outperforms its uncompressed counterpart. The code is released at https://github.com/xiaxin1998/OD-Rec.
Quantum theory of Weyl-invariant scalar-tensor gravity

We perform a manifestly covariant quantization of a Weyl invariant, i.e., a locally scale invariant, scalar-tensor gravity in the extended de Donder gauge condition (or harmonic gauge condition) for general coordinate invariance and a new scalar gauge for Weyl invariance within the framework of BRST formalism. It is shown that choral symmetry, which is a Poincar´e-like IOSp (8 | 8) supersymmetry in case of Einstein gravity, is extended to a Poincar´e-like IOSp (10 | 10) supersymmetry. We point out that there is a gravitational conformal symmetry in quantum gravity and account for how conventional conformal symmetry in a ﬂat Minkowski space-time is related to the gravitational conformal symmetry. Moreover, we examine the mechanism of spontaneous symmetry breakdown of the choral symmetry, and show that the gravitational conformal symmetry is spontaneously broken to the Poincar´e symmetry and the corresponding massless Nambu-Goldstone bosons are the graviton and the dilaton. We also prove the unitarity of the physical S-matrix on the basis of the BRST quartet mechanism.
Parallel Synthesis for Autoregressive Speech Generation

Autoregressive neural vocoders have achieved outstanding performance in speech synthesis tasks such as text-to-speech and voice conversion. An autoregressive vocoder predicts a sample at some time step conditioned on those at previous time steps. Though it synthesizes natural human speech, the iterative generation inevitably makes the synthesis time proportional to the utterance length, leading to low efficiency. Many works were dedicated to generating the whole speech sequence in parallel and proposed GAN-based, flow-based, and score-based vocoders. This paper proposed a new thought for the autoregressive generation. Instead of iteratively predicting samples in a time sequence, the proposed model performs frequency-wise autoregressive generation (FAR) and bit-wise autoregressive generation (BAR) to synthesize speech. In FAR, a speech utterance is split into frequency subbands, and a subband is generated conditioned on the previously generated one. Similarly, in BAR, an 8-bit quantized signal is generated iteratively from the first bit. By redesigning the autoregressive method to compute in domains other than the time domain, the number of iterations in the proposed model is no longer proportional to the utterance length but to the number of subbands/bits, significantly increasing inference efficiency. Besides, a post-filter is employed to sample signals from output posteriors; its training objective is designed based on the characteristics of the proposed methods. Experimental results show that the proposed model can synthesize speech faster than real-time without GPU acceleration. Compared with baseline vocoders, the proposed model achieves better MUSHRA results and shows good generalization ability for unseen speakers and 44 kHz speech.
Gerrymandering Individual Fairness

Individual fairness, proposed by Dwork et al., is a fairness measure that is supposed to prevent the unfair treatment of individuals on the subgroup level, and to overcome the problem that group fairness measures are susceptible to manipulation, or gerrymandering. The goal of the present paper is to explore the extent to which it is possible to gerrymander individual fairness itself. It will be proved that gerrymandering individual fairness in the context of predicting scores is possible. It will also be argued that individual fairness provides a very weak notion of fairness for some choices of feature space and metric. Finally, it will be discussed how the general idea of individual fairness may be preserved by formulating a notion of fairness that allows us to overcome some of the problems with individual fairness identified here and elsewhere.
The SCORE normalization, especially for highly heterogeneous network and text data

SCORE was introduced as a spectral approach to network community detection. Since many networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) approach to community detection may perform unsatisfactorily. SCORE alleviates the effect of degree heterogeneity by introducing a new normalization idea in the spectral domain and makes OSC more effective. SCORE is easy to use and computationally fast. It adapts easily to new directions and sees an increasing interest in practice. In this paper, we review the basics of SCORE, the adaption of SCORE to network mixed membership estimation and topic modeling, and the application of SCORE in real data, including two datasets on the publications of statisticians. We also review the theoretical 'ideology' underlying SCORE. We show that in the spectral domain, SCORE converts a simplicial cone to a simplex, and provides a simple and direct link between the simplex and network memberships. SCORE attains an exponential rate and a sharp phase transition in community detection, and achieves optimal rates in mixed membership estimation and topic modeling.
Task-Induced Representation Learning

In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for effective reinforcement learning (RL) from high-dimensional inputs. Unsupervised representation learning approaches based on reconstruction, prediction or contrastive learning have shown substantial learning efficiency gains. Yet, they have mostly been evaluated in clean laboratory or simulated settings. In contrast, real environments are visually complex and contain substantial amounts of clutter and distractors. Unsupervised representations will learn to model such distractors, potentially impairing the agent's learning efficiency. In contrast, an alternative class of approaches, which we call task-induced representation learning, leverages task information such as rewards or demonstrations from prior tasks to focus on task-relevant parts of the scene and ignore distractors. We investigate the effectiveness of unsupervised and task-induced representation learning approaches on four visually complex environments, from Distracting DMControl to the CARLA driving simulator. For both, RL and imitation learning, we find that representation learning generally improves sample efficiency on unseen tasks even in visually complex scenes and that task-induced representations can double learning efficiency compared to unsupervised alternatives. Code is available at https://clvrai.com/tarp.