OpenRaiser/Intern-Atlas huggingface dataset:
Pandemics are catalysts of scientific novelty: Evidence from COVID-19
Venue: arXiv cs.DL 2021
Year: 2021

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pre-trained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
Beneficial Perturbations Network for Defending Adversarial Examples
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art machine learning models. The reason is that neural networks fail to accommodate the distribution drift of the input data caused by adversarial perturbations. Here, we present a new solution - Beneficial Perturbation Network (BPN) - to defend against adversarial attacks by fixing the distribution drift. During training, BPN generates and leverages beneficial perturbations (somewhat opposite to well-known adversarial perturbations) by adding new, out-of-network biasing units. Biasing units influence the parameter space of the network, to preempt and neutralize future adversarial perturbations on input data samples. To achieve this, BPN creates reverse adversarial attacks during training, with very little cost, by recycling the training gradients already computed. Reverse attacks are captured by the biasing units, and the biases can in turn effectively defend against future adversarial examples. Reverse attacks are a shortcut, i.e., they affect the network's parameters without requiring instantiation of adversarial examples that could assist training. We provide comprehensive empirical evidence showing that 1) BPN is robust to adversarial examples and is much more running memory and computationally efficient compared to classical adversarial training. 2) BPN can defend against adversarial examples with negligible additional computation and parameter costs compared to training only on clean examples; 3) BPN hurts the accuracy on clean examples much less than classic adversarial training; 4) BPN can improve the generalization of the network 5) BPN trained only with Fast Gradient Sign Attack can generalize to defend PGD attacks.
PERF-Net: Pose Empowered RGB-Flow Net
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, many works in the video action recognition literature have shown that two stream models (combining spatial and temporal input streams) are necessary for achieving state of the art performance. In this paper we show the benefits of including yet another stream based on human pose estimated from each frame -- specifically by rendering pose on input RGB frames. At first blush, this additional stream may seem redundant given that human pose is fully determined by RGB pixel values -- however we show (perhaps surprisingly) that this simple and flexible addition can provide complementary gains. Using this insight, we then propose a new model, which we dub PERF-Net (short for Pose Empowered RGB-Flow Net), which combines this new pose stream with the standard RGB and flow based input streams via distillation techniques and show that our model outperforms the state-of-the-art by a large margin in a number of human action recognition datasets while not requiring flow or pose to be explicitly computed at inference time. The proposed pose stream is also part of the winner solution of the ActivityNet Kinetics Challenge 2020.
Engineering In-place (Shared-memory) Sorting Algorithms
Venue: arXiv cs.DC 2021
Year: 2021

We present sorting algorithms that represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. A part of the speed advantage is due to the feature to work in-place. Previously, the in-place feature often implied performance penalties. Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our comparison-based algorithm, In-place Superscalar Samplesort (IPS$^4$o), combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best in-place parallel comparison-based competitor by almost a factor of three. IPS$^4$o also outperforms the best comparison-based competitors in the in-place or not in-place, parallel or sequential settings. IPS$^4$o even outperforms the best integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new in-place radix sorter turns out to be the best algorithm. Claims to have the, in some sense, "best" sorting algorithm can be found in many papers which cannot all be true. Therefore, we base our conclusions on extensive experiments involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude. This confirms the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications.
Privacy Enhancement via Dummy Points in the Shuffle Model
Venue: arXiv cs.CR 2021
Year: 2021

The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization.In the shuffle model, a shuffler is utilized to break the link between the user identity and the message uploaded to the data analyst. Since less noise needs to be introduced to achieve the same privacy guarantee, following this paradigm, the utility of privacy-preserving data collection is improved. We propose DUMP (\underline{DUM}my-\underline{P}oint-based), a framework for privacy-preserving histogram estimation in the shuffle model. The core of DUMP is a new concept of \emph{dummy blanket}, which enables enhancing privacy by just introducing \textit{points }on the user side and further improving the utility of the shuffle model.We instantiate DUMP by proposing two protocols: pureDUMP and mixDUMP, and conduct a comprehensive experimental evaluation to compare them with existing protocols. The experimental results show that, under the same privacy guarantee, (1) the proposed protocols have significant improvements in communication efficiency over all existing multi-message protocols, by at least 3 orders of magnitude; (2) they achieve competitive utility, while the only known protocol (Ghazi \textit{et al.}, PMLR 2020) having better utility than ours employs hard-to-exactly-sample distributions which are vulnerable to floating-point attacks (CCS 2012).
Utility is in the Eye of the User: A Critique of NLP Leaderboards
Venue: arXiv cs.CL 2021
Year: 2021

Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models. While this leaderboard paradigm has been remarkably successful, a historical focus on performance-based evaluation has been at the expense of other qualities that the NLP community values in models, such as compactness, fairness, and energy efficiency. In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory. We frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them. With this framing, we formalize how leaderboards -- in their current form -- can be poor proxies for the NLP community at large. For example, a highly inefficient model would provide less utility to practitioners but not to a leaderboard, since it is a cost that only the former must bear. To allow practitioners to better estimate a model's utility to them, we advocate for more transparency on leaderboards, such as the reporting of statistics that are of practical concern (e.g., model size, energy efficiency, and inference latency).
Multiple Intelligent Reflecting Surfaces Assisted Cell-Free MIMO Communications
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate an intelligent reflecting surface (IRS) assisted cell-free multiple input multiple output (MIMO) communication system, where distributed multiple IRSs are dedicated deployed to assist distributed multiple base stations (BSs) for cooperative transmission. Our objective is to maximize the achievable sum-rate of the cell-free system by jointly optimizing the active transmit beamforming matrices at BSs and the passive reflecting beamforming matrices at IRSs, while the constraints on the maximum transmit power of each BS and the phase shift of each IRS element are satisfied. We propose an efficient framework to jointly design the BSs, the IRSs, and the user equipment (UEs). As a compromise approach, we first transform the non-convex problem into an equivalent form based on the fractional programming methods and then decompose the reformulated problem into two subproblems and solve them alternately. Particularly, we propose a Lagrangian dual sub-gradient based algorithm to solve the subproblem of optimizing the active transmit beamforming with nearly closed-form solutions. We reformulate the subproblem of optimizing the passive reflecting beamforming as a constant modulus constrained quadratic programming (CMC-QP) problem. We first provide two feasible solutions by proposing a pair of relaxation-based algorithms. We also develop a low-complexity alternating sequential optimization (ASO) algorithm to obtain closed-form solutions. All three algorithms are guaranteed to converge to locally optimal solutions. Simulation results demonstrate that the proposed algorithms achieve considerable performance improvements compared with the benchmark schemes.
How Do You Want Your Greedy: Simultaneous or Repeated?
Venue: arXiv cs.DS 2021
Year: 2021

We present SimultaneousGreedys, a deterministic algorithm for constrained submodular maximization. At a high level, the algorithm maintains $\ell$ solutions and greedily updates them in a simultaneous fashion. SimultaneousGreedys achieves the tightest known approximation guarantees for both $k$-extendible systems and the more general $k$-systems, which are $(k+1)^2/k = k + \mathcal{O}(1)$ and $(1 + \sqrt{k+2})^2 = k + \mathcal{O}(\sqrt{k})$, respectively. This is in contrast to previous algorithms, which are designed to provide tight approximation guarantees in one setting, but not both. We also improve the analysis of RepeatedGreedy, showing that it achieves an approximation ratio of $k + \mathcal{O}(\sqrt{k})$ for $k$-systems when allowed to run for $\mathcal{O}(\sqrt{k})$ iterations, an improvement in both the runtime and approximation over previous analyses. We demonstrate that both algorithms may be modified to run in nearly linear time with an arbitrarily small loss in the approximation. Both SimultaneousGreedys and RepeatedGreedy are flexible enough to incorporate the intersection of $m$ additional knapsack constraints, while retaining similar approximation guarantees: both algorithms yield an approximation guarantee of roughly $k + 2m + \mathcal{O}(\sqrt{k+m})$ for $k$-systems and SimultaneousGreedys enjoys an improved approximation guarantee of $k+2m + \mathcal{O}(\sqrt{m})$ for $k$-extendible systems. To complement our algorithmic contributions, we provide a hardness result which states that no algorithm making polynomially many oracle queries can achieve an approximation better than $k + 1/2 + \varepsilon$. We also present SubmodularGreedy.jl, a Julia package which implements these algorithms and may be downloaded at https://github.com/crharshaw/SubmodularGreedy.jl . Finally, we test the effectiveness of these algorithms on real datasets.
Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data
Venue: arXiv cs.DC 2021
Year: 2021

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the \emph{existing} parallel algorithms is $Ω(mn+2r\frac{q}{p})$, where $m$ and $n$ are the dimensions of the theoretical database matrix, $q$ and $r$ are dimensions of spectra, and $p$ is the number of processors. We further prove that communication-optimal strategy with fast-memory $\sqrt{M} = mn + \frac{2qr}{p}$ can achieve $Ω({\frac{2mnq}{p}})$ but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of \emph{provable}, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems.
Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols
Venue: arXiv cs.HC 2021
Year: 2021

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to readers when and where they need them most. We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols. A usability study showed that the tool helps researchers of all experience levels read papers. Furthermore, researchers were eager to have ScholarPhi's definitions available to support their everyday reading.
Secure Aggregation with Heterogeneous Quantization in Federated Learning
Venue: arXiv cs.IT 2021
Year: 2021

Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates to the same level of quantization. This severely degrades their performance due to lack of adaptation to available bandwidth at different users. We propose three schemes that allow secure model aggregation while using heterogeneous quantization. This enables the users to adjust their quantization proportional to their available bandwidth, which can provide a substantially better trade-off between the accuracy of training and the communication time. The proposed schemes are based on a grouping strategy by partitioning the network into groups, and partitioning the local model updates of users into segments. Instead of applying aggregation protocol to the entire local model update vector, it is applied on segments with specific coordination between users. We theoretically evaluate the quantization error for our schemes, and also demonstrate how our schemes can be utilized to overcome Byzantine users.
Global convergence of Negative Correlation Extreme Learning Machine
Venue: arXiv cs.LG 2021
Year: 2021

Ensemble approaches introduced in the Extreme Learning Machine (ELM) literature mainly come from methods that relies on data sampling procedures, under the assumption that the training data are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based on the Negative Correlation Learning (NCL) framework, called Negative Correlation Extreme Learning Machine (NCELM). This model works in two stages: i) different ELMs are generated as base learners with random weights in the hidden layer, and ii) a NCL penalty term with the information of the ensemble prediction is introduced in each ELM minimization problem, updating the base learners, iii) second step is iterated until the ensemble converges. Although this NCL ensemble method was validated by an experimental study with multiple benchmark datasets, no information was given on the conditions about this convergence. This paper mathematically presents the sufficient conditions to guarantee the global convergence of NCELM. The update of the ensemble in each iteration is defined as a contraction mapping function, and through Banach theorem, global convergence of the ensemble is proved.
Bayesian Meta-reinforcement Learning for Traffic Signal Control
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, there has been increasing amount of interest around meta reinforcement learning methods for traffic signal control, which have achieved better performance compared with traditional control methods. However, previous methods lack robustness in adaptation and stability in training process in complex situations, which largely limits its application in real-world traffic signal control. In this paper, we propose a novel value-based Bayesian meta-reinforcement learning framework BM-DQN to robustly speed up the learning process in new scenarios by utilizing well-trained prior knowledge learned from existing scenarios. This framework is based on our proposed fast-adaptation variation to Gradient-EM Bayesian Meta-learning and the fast-update advantage of DQN, which allows for fast adaptation to new scenarios with continual learning ability and robustness to uncertainty. The experiments on restricted 2D navigation and traffic signal control show that our proposed framework adapts more quickly and robustly in new scenarios than previous methods, and specifically, much better continual learning ability in heterogeneous scenarios.
Herding stochastic autonomous agents via local control rules and online global target selection strategies
Venue: arXiv cs.MA 2021
Year: 2021

In this Paper we propose a simple yet effective set of local control rules to make a group of "herder agents" collect and contain in a desired region an ensemble of non-cooperative stochastic "target agents" in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. Extensive numerical simulations confirm the effectiveness of the approach and are complemented by a more realistic validation on commercially available robotic agents via ROS.
Predicting User Engagement Status for Online Evaluation of Intelligent Assistants
Venue: arXiv cs.CL 2021
Year: 2021

Evaluation of intelligent assistants in large-scale and online settings remains an open challenge. User behavior-based online evaluation metrics have demonstrated great effectiveness for monitoring large-scale web search and recommender systems. Therefore, we consider predicting user engagement status as the very first and critical step to online evaluation for intelligent assistants. In this work, we first proposed a novel framework for classifying user engagement status into four categories -- fulfillment, continuation, reformulation and abandonment. We then demonstrated how to design simple but indicative metrics based on the framework to quantify user engagement levels. We also aim for automating user engagement prediction with machine learning methods. We compare various models and features for predicting engagement status using four real-world datasets. We conducted detailed analyses on features and failure cases to discuss the performance of current models as well as challenges.
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
Venue: arXiv cs.RO 2021
Year: 2021

6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting, which are sensitive to grasp synthesis errors. In this work, we propose a new method for learning closed-loop control policies for 6D grasping. Our policy takes a segmented point cloud of an object from an egocentric camera as input, and outputs continuous 6D control actions of the robot gripper for grasping the object. We combine imitation learning and reinforcement learning and introduce a goal-auxiliary actor-critic algorithm for policy learning. We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects. Our videos and code can be found at https://sites.google.com/view/gaddpg .
Modeling all alternative solutions for highly renewable energy systems
Venue: arXiv cs.CE 2021
Year: 2021

As the world is transitioning towards highly renewable energy systems, advanced tools are needed to analyze such complex networks. Energy system design is, however, challenged by real-world objective functions consisting of a blurry mix of technical and socioeconomic agendas, with limitations that cannot always be clearly stated. As a result, it is highly likely that solutions which are techno-economically suboptimal will be preferable. Here, we present a method capable of determining the continuum containing all techno-economically near-optimal solutions, moving the field of energy system modeling from discrete solutions to a new era where continuous solution ranges are available. The presented method is applied to study a range of technical and socioeconomic metrics on a model of the European electricity system. The near-optimal region is found to be relatively flat allowing for solutions that are slightly more expensive than the optimum but better in terms of equality, land use, and implementation time.