Reviewerly/exHarmony huggingface dataset:
HemI 2.0: an online service for heatmap illustration

Abstract Recent high-throughput omics techniques have produced a large amount of biological data. Visualization of big omics data is essential to answer a wide range of biological problems. As a concise but comprehensive strategy, a heatmap can analyze and visualize high-dimensional and heterogeneous biomolecular expression data in an attractive artwork. In 2014, we developed a stand-alone software package, Heat map Illustrator (HemI 1.0), which implemented three clustering methods and seven distance metrics for heatmap illustration. Here, we significantly improved 1.0 and released the online service of HemI 2.0, in which 7 clustering methods and 22 types of distance metrics were implemented. In HemI 2.0, the clustering results and publication-quality heatmaps can be exported directly. For an in-depth analysis of the data, we further added an option of enrichment analysis for 12 model organisms, with 15 types of functional annotations. The enrichment results can be visualized in five idioms, including bubble chart, bar graph, coxcomb chart, pie chart and word cloud. We anticipate that HemI 2.0 can be a helpful web server for visualization of biomolecular expression data, as well as the additional enrichment analysis. HemI 2.0 is freely available for all users at: https://hemi.biocuckoo.org/.
A Systematic Review on Hate Speech among Children and Adolescents: Definitions, Prevalence, and Overlap with Related Phenomena

Little is known about the current state of research on the involvement of young people in hate speech. Thus, this systematic review presents findings on a) the prevalence of hate speech among children and adolescents and on hate speech definitions that guide prevalence assessments for this population; and b) the theoretical and empirical overlap of hate speech with related concepts. This review was guided by the Cochrane approach. To be included, publications were required to deal with real-life experiences of hate speech, to provide empirical data on prevalence for samples aged 5 to 21 years and they had to be published in academic formats. Included publications were full-text coded using two raters (κ = .80) and their quality was assessed. The string-guided electronic search (ERIC, SocInfo, Psycinfo, Psyndex) yielded 1,850 publications. Eighteen publications based on 10 studies met the inclusion criteria and their findings were systematized. Twelve publications were of medium quality due to minor deficiencies in their theoretical or methodological foundations. All studies used samples of adolescents and none of younger children. Nine out of 10 studies applied quantitative methodologies. Eighteen publications based on 10 studies were included. Results showed that frequencies for hate speech exposure were higher than those related to victimization and perpetration. Definitions of hate speech and assessment instruments were heterogeneous. Empirical evidence for an often theorized overlap between hate speech and bullying was found. The paper concludes by presenting a definition of hate speech, including implications for practice, policy, and research.
Multi-Frame Self-Supervised Depth with Transformers

Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearance-based features. In this paper we revisit feature matching for self-supervised monocular depth estimation, and propose a novel transformer architecture for cost volume generation. We use depth-discretized epipolar sampling to select matching candidates, and refine predictions through a series of self- and cross-attention layers. These layers sharpen the matching probability between pixel features, improving over standard similarity metrics prone to ambiguities and local minima. The refined cost volume is decoded into depth estimates, and the whole pipeline is trained end-to-end from videos using only a photometric objective. Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures. We also show that our learned cross-attention network yields representations transferable across datasets, increasing the effectiveness of pre-training strategies. Project page: https://sites.google.com/tri.global/depthformer.
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images

In this paper, we propose PETRv2, a unified framework for 3D perception from multi-view images. Based on PETR [25], PETRv2 explores the effectiveness of temporal modeling, which utilizes the temporal information of previous frames to boost 3D object detection. More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling. The 3D PE achieves the temporal alignment on object position of different frames. To support for multi-task learning (e.g., BEV segmentation and 3D lane detection), PETRv2 provides a simple yet effective solution by introducing task-specific queries, which are initialized under different spaces. PETRv2 achieves state-of-the-art performance on 3D object detection, BEV segmentation and 3D lane detection. Detailed robustness analysis is also conducted on PETR framework. Code is available at https://github.com/megvii-research/PETR.
MAXSAT Heuristics for Cost Optimal Planning

The cost of an optimal delete relaxed plan, known as h+, is a powerful admissible heuristic but is in general intractable to compute. In this paper we examine the problem of computing h+ by encoding it as a MAXSAT problem. We develop a new encoding that utilizes constraint generation to support the computation of a sequence of increasing lower bounds on h+. We show a close connection between the computations performed by a recent approach for solving MAXSAT and a hitting set approach recently proposed for computing h+. Using this connection we observe that our MAXSAT computation can be initialized with a set of landmarks computed by LM-cut. By judicious use of MAXSAT solving along with a technique of lazy heuristic evaluation we obtain speedups for finding optimal plans over LM-cut on a number of domains. Our approach enables the exploitation of continued progress in MAXSAT solving, and also makes it possible to consider computing or approximating heuristics that are even more informed that h+ by, for example, adding some information about deletes back into the encoding.
Automatically-Generated Convex Region Decomposition for Real-Time Spatial Agent Navigation in Virtual Worlds

This paper presents a new method for decomposing environments of complex geometry into a navigation mesh represented by bounding geometry and a connectivity graph for real-time agent usage in virtual worlds. This is accomplished by the generation of a well-defined and high-coverage set of convex navigable regions and the connected gateways between them. The focus of this paper is a new automated algorithm developed for decomposing a 2D representation of world-space into arbitrary sided high-order polygons. The DEACCON (Decomposition of Environments for the Creation of Convex-region Navigation-meshes) algorithm works by seeding a 2D polygonal representation of world-space with a series of quads. Each quad is then provided with the opportunity to grow to its maximum extent before encountering an obstruction. DEACCON implements an automatic subdividing system to convert quads into higher-order polygons while still maintaining the convex property. This allows for the generation of navigation meshes with high degrees of coverage while still allowing the use of large navigation regions, providing for easier agent navigation in virtual worlds. Compared to the Space-filling Volumes and Hertel-Mehlhorn navigation mesh decomposition methods, DEACCON provides more complete coverage, controllable mesh sizes, and better overall algorithmic control to desired decomposition quality with an improvement in agent navigation speed due to better decompositions.
Speaking with Your Sidekick: Understanding Situated Speech in Computer Role Playing Games

Speech and natural language are natural and convenient ways to interact with artificial characters. Current use of language in games, however, is limited to menu systems and interplayer communication. To achieve smooth linguistic communication with synthetic agents, research should focus on how language connects to the situation in which it occurs. Taking account of the physical scene (where is the speaker located, what is around her, when does she speak?) as well as the functional aspects of the situation (why did he choose to speak? What are his likely plans?) can disambiguate the linguistic signal in form and content. We present a game environment to collect time synchronized speech and action streams, to visualize these data and to annotate them at different stages of processing. We further sketch a framework for situated speech understanding on such data, taking into account aspects of the physical situation as well as the plans players follow. Our results show that this combination of influences achieves remarkable improvements over the individual situation models despite the very noisy and spontaneous nature of the speech involved. This work provides a basis for developing characters that use situated natural spoken language to communicate meaningfully with human players.
Cost-Optimal Factored Planning: Promises and Pitfalls

Factored planning methods aim to exploit locality to efficiently solve large but "loosely coupled" planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish a priori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored cost-optimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method runs in polynomial time under conditions similar to those considered in previous work, but not only under those conditions. Thus, what constitutes an essential measure of "factorability" remains obscure.
Symbolic Variable Elimination for Discrete and Continuous Graphical Models

Probabilistic reasoning in the real-world often requires inference incontinuous variable graphical models, yet there are few methods for exact, closed-form inference when joint distributions are non-Gaussian. To address this inferential deficit, we introduce SVE -- a symbolic extension of the well-known variable elimination algorithm to perform exact inference in an expressive class of mixed discrete and continuous variable graphical models whose conditional probability functions can be well-approximated as piecewise combinations of polynomials with bounded support. Using this representation, we show that we can compute all of the SVE operations exactly and in closed-form, which crucially includes definite integration w.r.t. multivariate piecewise polynomial functions. To aid in the efficient computation and compact representation of this solution, we use an extended algebraic decision diagram (XADD) data structure that supports all SVE operations. We provide illustrative results for SVE on probabilistic inference queries inspired by robotics localization and tracking applications that mix various continuous distributions; this represents the first time a general closed-form exact solution has been proposed for this expressive class of discrete/continuous graphical models.
Probabilistic Dimensionality Reduction via Structure Learning

We propose an alternative probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework, we present a new model, which is able to learn a set of embedding points in a low-dimensional space by retaining the inherent structure from high-dimensional data. The objective function of this new model can be equivalently interpreted as two coupled learning problems, i.e., structure learning and the learning of projection matrix. Inspired by this interesting interpretation, we propose another model, which finds a set of embedding points that can directly form an explicit graph structure. We proved that the model by learning explicit graphs generalizes the reversed graph embedding method, but leads to a natural interpretation from Bayesian perspective. This can greatly facilitate data visualization and scientific discovery in downstream analysis. Extensive experiments are performed that demonstrate that the proposed framework is able to retain the inherent structure of datasets and achieve competitive quantitative results in terms of various performance evaluation criteria.
Privacy-Preserving Aggregate Queries for Optimal Location Selection

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
An Effective Classification-Based Framework for Predicting Cloud Capacity Demand in Cloud Services

The rapid development of pay-as-you-go cloud services motivates the increasing number of cloud resource demands. However, the volatile demands bring new challenges for current techniques to minimize the cost of cloud capacity planning and VM provisioning while satisfying the customer demands. The service vendors will incur enormous revenue loss within the long-term inappropriate planning, especially when the demands fluctuate abruptly and frequently. In this paper, we cast the cloud capacity planning as a classification problem and propose an integrated framework, which effectively predicts the abrupt changing demands, to reduce the cost of cloud resource provisioning. In this framework, we first apply Piecewise Linear Representation to segment the time series of cloud resource demands for labeling the changing trend of each period. Second, Weighted SVM is leveraged to fit the statistical information and the label of each period and predict the changing trend of the following period. Finally, an incremental learning strategy is utilized to ensure the low cost of updating the model using the upcoming requests. We evaluate our framework on the IBM Smart Cloud Enterprise (SCE) trace data and the experimental results show the effectiveness of our proposed framework.
Provably Secure and Lightweight Identity-Based Authenticated Data Sharing Protocol for Cyber-Physical Cloud Environment

Secure and efficient file storage and sharing via authenticated physical devices remain challenging to achieve in a cyber-physical cloud environment, particularly due to the diversity of devices used to access the services and data. Thus in this paper, we present a lightweight identity-based authenticated data sharing protocol to provide secure data sharing among geographically dispersed physical devices and clients. The proposed protocol is demonstrated to resist chosen-ciphertext attack (CCA) under the hardness assumption of decisional-Strong Diffie-Hellman (SDH) problem. We also evaluate the performance of the proposed protocol with existing data sharing protocols in terms of computational overhead, communication overhead, and response time.
Studying Bad Updates of Top Free-to-Download Apps in the Google Play Store

Developers always focus on delivering high-quality updates to improve, or maintain the rating of their apps. Prior work has studied user reviews by analyzing all reviews of an app. However, this app-level analysis misses the point that users post reviews to provide their feedback on a certain update. For example, two bad updates of an app with a history of good updates would not be spotted using app-level analysis. In this paper, we examine reviews at the update-level to better understand how users perceive bad updates. We focus our study on the top 250 bad updates (i.e., updates with the highest increase in the percentage of negative reviews relative to the prior updates of the app) from 26,726 updates of 2,526 top free-to-download apps in the Google Play Store. We find that feature removal and UI issues have the highest increase in the percentage of negative reviews. Bad updates with crashes and functional issues are the most likely to be fixed by a later update. However, developers often do not mention these fixes in the release notes. Our work demonstrates the necessity of an update-level analysis of reviews to capture the feelings of an app's user-base about a particular update.
Camera calibration method based on optimal polarization angle

The accurate estimation of camera internal and external parameters is one of the keys to high-precision camera calibration. At present, the mainstream Zhang's calibration method requires the use of a checkerboard to achieve high-precision calibration result. Therefore, the precise extraction of the pixel coordinates at the corner points on the checkerboard is one of the key technologies. The traditional methods are to improve the corner extraction algorithm, but ignore the imaging process of the calibration images. It is difficult to detect and eliminate the high-light regions in the process of calibrating image, and the existence of high-light regions will cause low accuracy of camera calibration. Therefore, a camera calibration method based on optimal polarization information is proposed in this paper. The method first uses a Stokes variable linear feedback through a CCD camera equipped with a polarizer to obtain a checkerboard calibration image at an optimal polarization angle in different spatial positions. Then use the sub-pixel level detection algorithm and Gaussian fitting method to locate the corner points in the image and to solve the sub-pixel coordinates of the corner points precisely. At last, the two-dimensional pixel coordinates of the corner points in each checkerboard image are extracted to perform camera calibration. The experimental results show that the coordinates of corner points of calibration plate image based on the optimal polarization angle have about 1 pixel difference compared with the coordinates of the calibration plate corner points under natural illumination, and the slopes between the parallel lines of the fitted are closer, meet the high precision calibration requirement of camera.
Excited-state quantum phase transition and the quantum-speed-limit time

We investigate the influence of an excited-state quantum phase transition (ESQPT) on the quantum speed limit (QSL) time of an open quantum system. The system consists of a central qubit coupled to a spin environment modeled by a Lipkin-Meshkov-Glick (LMG) Hamiltonian. We show that when the coupling between qubit and environment is such that the latter is located at the ESQPT critical energy the qubit evolution shows a remarkable slowdown, marked by an abrupt peak in the QSL time. Interestingly, this slowdown at the ESQPT critical point is induced by the singular behavior of the qubit decoherence rate, being independent of the Markovian nature of the environment.
FPGA Resource Pooling in Cloud Computing

Cloud providers have started to deploy various FPGA accelerators in their datacenters because the performance of many applications can be significantly improved by implementing their core routines in FPGAs. In conventional datacenters with FPGA accelerated servers, if a tenant wants to use FPGA accelerators, it requests for a VM instance residing in a server equipped with an FPGA accelerator. This paradigm to integrate FPGA into Cloud leads to poor resource sharing of the precious FPGA resources. In this paper, we propose FPGAPooling, an FPAG-enabled Cloud system where all FPGA accelerators are managed as a single resource pool and shared among all VMs. For a VM, instead of requesting the Cloud to run the VM on an FPGA accelerated server, at run time, when a VM needs to use FPGA acceleration, it requests an FPGA accelerator from the pool. After the VM finishes using the FPGA accelerator, it releases the FPGA accelerator back to the pool. We design a centralized scheduler to handle acceleration requests from VMs and assign each request to an idle FPGA accelerator at run time; We implemented a system prototype on IBM's OpenPower Cloud system. The key challenging of FPGAPooling is scheduling. We designed and implemented a group of scheduling algorithms for the FPGAPooling system. With extensive evaluations on both a small testbed and a large-scale simulation, we found that our algorithms can improve the average and tail job completion time by up to 7 and 4 times, respectively.
Qubit Measurement by Multichannel Driving

We theoretically propose and experimentally implement a method of measuring a qubit by driving it close to the frequency of a dispersively coupled bosonic mode. The separation of the bosonic states corresponding to different qubit states begins essentially immediately at maximum rate, leading to a speedup in the measurement protocol. Also the bosonic mode can be simultaneously driven to optimize measurement speed and fidelity. We experimentally test this measurement protocol using a superconducting qubit coupled to a resonator mode. For a certain measurement time, we observe that the conventional dispersive readout yields close to 100% higher average measurement error than our protocol. Finally, we use an additional resonator drive to leave the resonator state to vacuum if the qubit is in the ground state during the measurement protocol. This suggests that the proposed measurement technique may become useful in unconditionally resetting the resonator to a vacuum state after the measurement pulse.