Reviewerly/exHarmony huggingface dataset:
EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms.
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing

Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multi-level semantics while keeping the efficiency high. AttaNet consists of two primary modules: Strip Attention Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging images with low segmentation accuracy, there are a significantly larger amount of vertical strip areas than horizontal ones, SAM utilizes a striping operation to reduce the complexity of encoding global context in the vertical direction drastically while keeping most of contextual information, compared to the non-local approaches. Moreover, AFM follows a cross-level aggregation strategy to limit the computation, and adopts an attention strategy to weight the importance of different levels of features at each pixel when fusing them, obtaining an efficient multi-level representation. We have conducted extensive experiments on two semantic segmentation benchmarks, and our network achieves different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9% mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on ADE20K as well.
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data

Maintaining consistent personas is essential for dialogue agents.Although tremendous advancements have been brought, the limitedscale of annotated persona-dense data are still barriers towards training robust and consistent persona-based dialogue models.In this work, we show how the challenges can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERTover-BERT (BoB) model.Specifically, the model consists of a BERT-based encoder and two BERT-based decoders, where one decoder is for response generation, and another is for consistency understanding.In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second decoder in an unlikelihood manner.Under different limited data settings, both automatic and human evaluations demonstrate that the proposed model outperforms strong baselines in response quality and persona consistency.
Person Re-identification using Heterogeneous Local Graph Attention Networks

Recently, some methods have focused on learning local relation among parts of pedestrian images for person re-identification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation among parts from different images, which results in incomplete local relation information. In this paper, we propose a novel deep graph model named Heterogeneous Local Graph Attention Networks (HLGAT) to model the inter-local relation and the intra-local relation in the completed local graph, simultaneously. Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features. As for the inter-local relation, we propose the attention regularization loss to constrain the attention weights based on the identities of local features in order to describe the inter-local relation accurately. As for the intra-local relation, we propose to inject the contextual information into the attention weights to consider structure information. Extensive experiments on Market-1501, CUHK03, DukeMTMC-reID and MSMT17 demonstrate that the proposed HLGAT outperforms the state-of-the-art methods.
Universality of Dicke superradiance in arrays of quantum emitters

Abstract Dicke superradiance is an example of emergence of macroscopic quantum coherence via correlated dissipation. Starting from an initially incoherent state, a collection of excited atoms synchronizes as they decay, generating a macroscopic dipole moment and emitting a short and intense pulse of light. While well understood in cavities, superradiance remains an open problem in extended systems due to the exponential growth of complexity with atom number. Here we show that Dicke superradiance is a universal phenomenon in ordered arrays. We present a theoretical framework – which circumvents the exponential complexity of the problem – that allows us to predict the critical distance beyond which Dicke superradiance disappears. This critical distance is highly dependent on the dimensionality and atom number. Our predictions can be tested in state of the art experiments with arrays of neutral atoms, molecules, and solid-state emitters and pave the way towards understanding the role of many-body decay in quantum simulation, metrology, and lasing.
Noisy Channel Language Model Prompting for Few-Shot Text Classification

We introduce a noisy channel approach for language model prompting in few-shot text classification.Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input given the label, and are thereby required to explain every word in the input.We use channel models for recently proposed few-shot learning methods with no or very limited updates to the language model parameters, via either in-context demonstration or prompt tuning.Our experiments show that, for both methods, channel models significantly outperform their direct counterparts, which we attribute to their stability, i.e., lower variance and higher worstcase accuracy.We also present extensive ablations that provide recommendations for when to use channel prompt tuning instead of other competitive methods (e.g., direct head tuning): channel prompt tuning is preferred when the number of training examples is small, labels in the training data are imbalanced, or generalization to unseen labels is required.
Quantum technology for military applications

Abstract Quantum technology is an emergent and potentially disruptive discipline, with the ability to affect many human activities. Quantum technologies are dual-use technologies, and as such are of interest to the defence and security industry and military and governmental actors. This report reviews and maps the possible quantum technology military applications, serving as an entry point for international peace and security assessment, ethics research, military and governmental policy, strategy and decision making. Quantum technologies for military applications introduce new capabilities, improving effectiveness and increasing precision, thus leading to ‘quantum warfare’, wherein new military strategies, doctrines, policies and ethics should be established. This report provides a basic overview of quantum technologies under development, also estimating the expected time scale of delivery or the utilisation impact. Particular military applications of quantum technology are described for various warfare domains (e.g. land, air, space, electronic, cyber and underwater warfare and ISTAR—intelligence, surveillance, target acquisition and reconnaissance), and related issues and challenges are articulated.
Dual Contrastive Learning for General Face Forgery Detection

With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns. Previous works always formulate face forgery detection as a classiﬁcation problem based on cross-entropy loss, which emphasizes category-level differences rather than the essential discrepancies between real and fake faces, limiting model generalization in unseen domains. To address this issue, we propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designed contrastive learning at different granularities to learn generalized feature representation. Concretely, combined with the hard sample selection strategy, Inter-Instance Contrastive Learning (Inter-ICL) is ﬁrst proposed to promote task-related discriminative features learning by especially constructing instance pairs. Moreover, to further explore the essential discrepancies, Intra-Instance Contrastive Learning (Intra-ICL) is introduced to focus on the local content inconsistencies prevalent in the forged faces by constructing local region pairs inside instances. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art competitors. Our Code is available at https://github.com/Tencent/TFace.git.
Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.
LongT5: Efficient Text-To-Text Transformer for Long Sequences

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models.In this paper, we present LongT5, a new model that explores the effects of scaling both the input length and model size at the same time.Specifically, we integrate attention ideas from long-input transformers (ETC), and adopt pretraining strategies from summarization pretraining (PEGASUS) into the scalable T5 architecture.The result is a new attention mechanism we call Transient Global (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs.We are able to achieve state-ofthe-art results on several summarization and question answering tasks, as well as outperform the original T5 models on these tasks.We have open sourced our architecture and training code, as well as our pre-trained model checkpoints.
Active Learning for Deep Object Detection via Probabilistic Modeling

Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image’s informativeness using only the classification head. In this paper, we propose a novel deep active learning approach for object detection. Our approach relies on mixture density networks that estimate a probabilistic distribution for each localization and classification head’s output. We explicitly estimate the aleatoric and epistemic uncertainty in a single forward pass of a single model. Our method uses a scoring function that aggregates these two types of uncertainties for both heads to obtain every image’s informativeness score. We demonstrate the efficacy of our approach in PASCAL VOC and MS-COCO datasets. Our approach outperforms single-model based methods and performs on par with multi-model based methods at a fraction of the computing cost. Code is available at https://github.com/NVlabs/AL-MDN.
On the Identification of Noise Covariances and Adaptive Kalman Filtering: A New Look at a 50 Year-Old Problem

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown. Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this paper. We first present the necessary and sufficient condition for unknown noise covariance estimation; these conditions are related to the rank of a matrix involving the auto and cross-covariances of a weighted sum of innovations, where the weights are the coefficients of the minimal polynomial of the closed-loop system transition matrix of a stable, but not necessarily optimal, Kalman filter. We present an optimization criterion and a novel six-step approach based on a successive approximation, coupled with a gradient algorithm with adaptive step sizes, to estimate the steady-state Kalman filter gain, the unknown noise covariance matrices, as well as the state prediction (and updated) error covariance matrix. Our approach enforces the structural assumptions on unknown noise covariances and ensures symmetry and positive definiteness of the estimated covariance matrices. We provide several approaches to estimate the unknown measurement noise covariance R via post-fit residuals, an approach not yet exploited in the literature. The validation of the proposed method on five different test cases from the literature demonstrates that the proposed method significantly outperforms previous state-of-the-art methods. It also offers a number of novel machine learning motivated approaches, such as sequential (one sample at a time) and mini-batch-based methods, to speed up the computations.
New publicly verifiable computation for batch matrix multiplication

With the prevalence of cloud computing, the resource constrained clients are trended to outsource their computation-intensive tasks to the cloud server. Although outsourcing computation paradigm brings many benefits for both clients and cloud server, it causes some security challenges. In this paper, we focus on the outsourcing computation of matrix multiplication, and propose a new publicly verifiable computation scheme for batch matrix multiplication. Different from traditional matrix computation outsourcing model, the outsourcing task of our scheme is to compute MXi for group of clients, where Xi is a private matrix chosen by different clients and M is a public matrix given by a data center beforehand. Based on the two techniques of privacy-preserving matrix transformation and matrix digest, our scheme can protect the secrecy of the client's private matrix Xi and dramatically reduce the computation cost in both the key generation and the computing phases. Security analysis shows that the proposed scheme can achieve the desired security properties under the co-computational Diffie-Hellman assumption. We also provide the experimental evaluation that demonstrates the efficiency of our scheme.
Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management.However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA).Big Data complexity is the first factor hampering the full potential of BDA.The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach.This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development.Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker.In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification.Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform.Driven by customers' requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform.The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.
Online Location Trace Privacy: An Information Theoretic Approach

We consider the problem of protecting individual user's location privacy at the trace-level and study the privacy-utility trade-off, which has key applications in privacy-preserving location-based service. Existing works on Location Privacy Protection Mechanisms (LPPMs) have mainly focused on protecting single location, without taking into account the temporal correlations among locations within the trace, which can lead to higher privacy leakage when considering the whole trace. However, to date, there lacks a formal framework to quantify the trace-level location privacy leakage, and a practical mechanism to release location traces in an optimal and online manner. In this paper, we endeavor to solve this problem using an information-theoretic approach. We first propose a location trace privacy metric based on the mutual information between the original and released trace in an offline setting, and formulate the optimal location trace release problem that minimizes trace-level privacy leakage given a utility constraint. We also propose a privacy metric to capture trace-level privacy leakage in an online setting. As directly computing these metrics incur exponential complexity w.r.t. the trace length, we obtain upper and lower bounds on the trace-level privacy leakage by exploiting the Markov structure of the temporal location correlations, which are efficiently computable. The proposed upper bounds enable us to derive efficient online solutions (i.e., LPPMs) by modifying Blahut-Arimoto algorithm in rate-distortion theory. Then we validate the proposed upper and lower bounds and the actual leakage of our LPPM through extensive experiments over both synthetic and real-world location data sets. Our results show the superiority of our LPPM over existing LPPMs in terms of trace-level privacy-utility tradeoff, which is more conspicuous when the location trace is more correlated.
Find-Fix-Finish-Exploit-Analyze (F3EA) meta-heuristic algorithm: An effective algorithm with new evolutionary operators for global optimization

A novel population-based evolutionary meta-heuristic algorithm is introduced, which imitates the Find-Fix-Finish-Exploit-Analyze (F3EA) targeting process. It considers the surface of the objective function as the battlefield and executes Find-Fix-Finish-Exploit-Analyze steps in an iterative manner. Following the radar detection rationale, a new evolutionary selection operator is introduced during the Find step. It is justified how to model the Fix step as a one-dimensional optimization problem to attain a local search operator. To produce a new solution by the Finish step, the target solution selected in the Find step is actioned artificially. This is an adaptive mutation stage, in which the position of the new potential solution is identified via modeling of projectile motion. The Exploit step takes over opportunities provided by mating the generated solution and its parent solution. Finally, the Analyze step, updates the population. Extensive experiments are conducted based on engineering optimization problems and a large set of benchmark functions for performance assessment, sensitivity analysis of the control parameters, and effectiveness analysis of different steps of the algorithm. Results of statistical tests signify that equipping the algorithm with new selection, mutation and local search operators makes it effective and efficient enough to exceed or match the best of rivals.
An Attribute Credential Based Public Key Scheme for Fog Computing in Digital Manufacturing

In order to meet low latency, service sensitive and location awareness requirements of digital manufacturing, fog computing is introduced to be an intermediate layer between industrial Internet of Things and cloud. The distributed, dynamic characteristics and the collaboration requirement make it face many new security and privacy issues that cannot be solved by the traditional public key or symmetric cryptosystem. For addressing them, a registered but anonymous attribute credential is designed to manage the network entities. Based on it, an attribute credential based public key cryptography (AC-PKC) is constructed to provide flexible key management by taking the advantage of the certificate-less public key cryptography and the combination property of the elliptic curve cryptography. Encryption, authentication, and access control with privacy preserving can be realized on the basic operations of AC-PKC, which can meet various security requirements of fog computing based digital manufacturing. The performance analyses and comparison with the existing public key schemes and attribute based encryption solutions show that the proposed scheme can work flexibly at a relatively low cost.