Reviewerly/exHarmony huggingface dataset:
Template-free Prompt Tuning for Few-shot NER

Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words.However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity spans.In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates.Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position.Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre-trained models can easily adapt to.While avoiding the complicated template-based process, the proposed LM objective also reduces the gap between different objectives used in pre-training and fine-tuning, thus it can better benefit the few-shot performance.Experimental results demonstrate the effectiveness of the proposed method over bert-tagger and template-based method under few-shot settings.Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template-based method.
Salp Swarm Optimization: A critical review

In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optimization performance. However, the original work was characterized by some conceptual and mathematical flaws, which influenced all ensuing papers on the subject. In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by this algorithm. We also propose a mathematically correct version of SSO, named Amended Salp Swarm Optimizer (ASSO) that fixes all the discussed problems. We benchmarked the performance of ASSO on a set of tailored experiments, showing that it is able to achieve better results than the original SSO. Finally, we performed an extensive study aimed at understanding whether SSO and its variants provide advantages compared to other metaheuristics. The experimental results, where SSO cannot outperform simple well-known metaheuristics, suggest that the scientific community can safely abandon SSO.
First quantum computer to pack 100 qubits enters crowded race

But IBM’s latest quantum chip and its competitors face a long path towards making the machines useful. But IBM’s latest quantum chip and its competitors face a long path towards making the machines useful.
Improving and Simplifying Pattern Exploiting Training

Recently, pre-trained language models (LMs) have achieved strong performance when finetuned on difficult benchmarks like Super-GLUE.However, performance can suffer when there are very few labeled examples available for fine-tuning.Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning.However, PET uses task-specific unlabeled data.In this paper, we focus on few shot learning without any unlabeled data and introduce ADAPET, which modifies PET's objective to provide denser supervision during fine-tuning.As a result, ADAPET outperforms PET on Su-perGLUE without any task-specific unlabeled data.
Dual-Element, Two-Dimensional Atom Array with Continuous-Mode Operation

Quantum processing architectures that include multiple qubit modalities offer compelling strategies for high-fidelity operations and readout, quantum error correction, and a path for scaling to large system sizes. Such hybrid architectures have been realized for leading platforms, including superconducting circuits and trapped ions. Recently, a new approach for constructing large, coherent quantum processors has emerged based on arrays of individually trapped neutral atoms. However, these demonstrations have been limited to arrays of a single atomic element where the identical nature of the atoms makes crosstalk-free control and nondemolition readout of a large number of atomic qubits challenging. Here we introduce a dual-element atom array with individual control of single rubidium and cesium atoms. We demonstrate their independent placement in arrays with up to 512 trapping sites and observe negligible crosstalk between the two elements. Furthermore, by continuously reloading one atomic element while maintaining an array of the other, we demonstrate a new continuous operation mode for atom arrays without any off-time. Our results enable avenues for auxiliary-qubit-assisted quantum protocols such as quantum nondemolition measurements and quantum error correction, as well as continuously operating quantum processors and sensors.1 MoreReceived 29 October 2021Accepted 4 February 2022DOI:https://doi.org/10.1103/PhysRevX.12.011040Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasHybrid quantum systemsQuantum information architectures & platformsTechniquesAtom & ion coolingCooling & trappingOptical tweezersQuantum InformationAtomic, Molecular & Optical
End-to-End Handwritten Paragraph Text Recognition Using a Vertical Attention Network

Unconstrained handwritten text recognition remains challenging for computer vision systems. Paragraph text recognition is traditionally achieved by two models: the first one for line segmentation and the second one for text line recognition. We propose a unified end-to-end model using hybrid attention to tackle this task. This model is designed to iteratively process a paragraph image line by line. It can be split into three modules. An encoder generates feature maps from the whole paragraph image. Then, an attention module recurrently generates a vertical weighted mask enabling to focus on the current text line features. This way, it performs a kind of implicit line segmentation. For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph. We achieve state-of-the-art character error rate at paragraph level on three popular datasets: 1.91% for RIMES, 4.45% for IAM and 3.59% for READ 2016. Our code and trained model weights are available at https://github.com/FactoDeepLearning/VerticalAttentionOCR.
Nested sampling for physical scientists

We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey developments in implementing efficient NS algorithms in practice in high-dimensions, including methods for sampling from the so-called constrained prior. We outline the ways in which NS may be applied and describe the application of NS in three scientific fields in which the algorithm has proved to be useful: cosmology, gravitational-wave astronomy, and materials science. We close by making recommendations for best practice when using NS and by summarizing potential limitations and optimizations of NS.
Avoiding Barren Plateaus Using Classical Shadows

Variational quantum algorithms are augmented by an additional subroutine that efficiently detects signatures of barren plateaus -- unfavorable parameter regions that prevent optimization due to vanishing gradients.
A Comparative Study of Demographic Attribute Inference in Twitter

Social media platforms have become a major gateway to receive and analyze public opinions. Understandingusers can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes,such as ethnicity, gender, age, among others,have been extensively applied to characterize social mediausers. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles.Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ postsand train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals:self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names.We provide a comparative study of these signalsside-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age.We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training modelsand evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.
Multi-Objective Service Composition with QoS Dependencies

Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.
Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud

Secure data deduplication can significantly reduce the communication and storage overheads in cloud storage services, and has potential applications in our big data-driven society. Existing data deduplication schemes are generally designed to either resist brute-force attacks or ensure the efficiency and data availability, but not both conditions. We are also not aware of any existing scheme that achieves accountability, in the sense of reducing duplicate information disclosure (e.g., to determine whether plaintexts of two encrypted messages are identical). In this paper, we investigate a three-tier cross-domain architecture, and propose an efficient and privacy-preserving big data deduplication in cloud storage (hereafter referred to as EPCDD). EPCDD achieves both privacy-preserving and data availability, and resists brute-force attacks. In addition, we take accountability into consideration to offer better privacy assurances than existing schemes. We then demonstrate that EPCDD outperforms existing competing schemes, in terms of computation, communication and storage overheads. In addition, the time complexity of duplicate search in EPCDD is logarithmic.
Achieving high-fidelity single-qubit gates in a strongly driven charge qubit with 1/f charge noise

Abstract Charge qubits formed in double quantum dots represent quintessential two-level systems that enjoy both ease of control and efficient readout. Unfortunately, charge noise can cause rapid decoherence, with typical single-qubit gate fidelities falling below 90%. Here we develop analytical methods to study the evolution of strongly driven charge qubits, for general and 1/ f charge-noise spectra. We show that special pulsing techniques can simultaneously suppress errors due to strong driving and charge noise, yielding single-qubit gates with fidelities above 99.9%. These results demonstrate that quantum dot charge qubits provide a potential route to high-fidelity quantum computation.
On the Complexity of Quantum Circuit Compilation

Quantum circuit compilation (QCC) is an important problem in the emerging field of quantum computing. The problem has a strong relevance to combinatorial search, as solving approaches recently published include constraint programming and temporal planning. In this paper, we focus on a complexity analysis of quantum circuit compilation. We formulate a makespan optimization problem based on QCC, and prove that the problem is NP-complete. To the best of our knowledge, this is the first study on the theoretical complexity of QCC.
DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function

Traditional collaborative filtering techniques suffer from the data sparsity problem in practice. That is, only a small proportion of all items in the recommender system occur in a user's rated item list. However, in order to retrieve items meeting a user's interest, all possible candidate items should be investigated. To address this problem, this paper proposes a recommendation approach called DeepRec, based on feedforward deep neural network learning with item embedding and weighted loss function. Specifically, item embedding learns numerical vectors for item representation, and weighted loss function balances popularity and novelty of recommended items. Moreover, it introduces two strategies, i.e. sampling by random (Ran-Strategy) and sampling by distribution (Pro-Strategy), to leave one item as output and the remaining as input from each user's historically rated item list. Max-pooling and average-pooling are employed to combine individual item vectors to derive users’ input vectors for feedforward deep neural network learning. Experiments on the App dataset and the Last.fm dataset demonstrate that the proposed DeepRec approach is superior to state-of-the-art techniques in recommending Apps and songs in terms of accuracy and diversity as well as complexity.
GraphProtector: A Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.
Asymmetric encryption of multiple-image based on compressed sensing and phase-truncation in cylindrical diffraction domain

In this paper, an asymmetric multiple-image encryption algorithm based on compressed sensing and cylindrical diffraction is proposed. Firstly, these multiple gray scale images are compressed by compressed sensing in separately. Then single amplitude ciphertext is obtained by asymmetric operation of phase truncation after cylindrical diffraction. This method not only can achieve multiple-image encryption with a small amount of data, but also can prevent information disclosure. Moreover, the proposed method has high security because it can resist the phase-retrieval attack. Numerical simulations have demonstrated the security and effectiveness of the proposed method.
Forecasting financial crashes with quantum computing

A key problem in financial mathematics is the forecasting of financial crashes: If we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem. Financial crashes are inherently difficult to predict, even for a regulator which has complete information about the financial system. In this Rapid Communication, we show how this problem can be handled by quantum annealers. More specifically, we map the equilibrium condition of a toy-model financial network to the ground-state problem of a spin-1/2 quantum Hamiltonian with two-body interactions, i.e., a quadratic unconstrained binary optimization problem. The equilibrium market values of institutions after a sudden shock to the network can then be calculated via adiabatic quantum computation and, more generically, by quantum annealers. Our procedure could be implemented on near-term quantum processors, thus providing a potentially more efficient way to assess financial equilibrium and predict financial crashes.
Spatially entangled photon-pair generation using a partial spatially coherent pump beam

We demonstrate experimental generation of spatially-entangled photon-pairs by spontaneous parametric down conversion (SPDC) using a partial spatially coherent pump beam. By varying the spatial coherence of the pump, we show its influence on the downconverted photon's spatial correlations and on their degree of entanglement, in excellent agreement with theory. We then exploit this property to produce pairs of photons with a specific degree of entanglement by tailoring of the pump coherence length. This work thus unravels the fundamental transfer of coherence occuring in SPDC processes, and provides a simple experimental scheme to generate photon-pairs with a well-defined degree of spatial entanglement, which may be useful for quantum communication and information processing
Super Sparse Convolutional Neural Networks

To construct small mobile networks without performance loss and address the over-fitting issues caused by the less abundant training datasets, this paper proposes a novel super sparse convolutional (SSC) kernel, and its corresponding network is called SSC-Net. In a SSC kernel, every spatial kernel has only one non-zero parameter and these non-zero spatial positions are all different. The SSC kernel can effectively select the pixels from the feature maps according to its non-zero positions and perform on them. Therefore, SSC can preserve the general characteristics of the geometric and the channels’ differences, resulting in preserving the quality of the retrieved features and meeting the general accuracy requirements. Furthermore, SSC can be entirely implemented by the “shift” and “group point-wise” convolutional operations without any spatial kernels (e.g., “3×3”). Therefore, SSC is the first method to remove the parameters’ redundancy from the both spatial extent and the channel extent, leading to largely decreasing the parameters and Flops as well as further reducing the img2col and col2img operations implemented by the low leveled libraries. Meanwhile, SSC-Net can improve the sparsity and overcome the over-fitting more effectively than the other mobile networks. Comparative experiments were performed on the less abundant CIFAR and low resolution ImageNet datasets. The results showed that the SSC-Nets can significantly decrease the parameters and the computational Flops without any performance losses. Additionally, it can also improve the ability of addressing the over-fitting problem on the more challenging less abundant datasets.
HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.
Hierarchical Macro Strategy Model for MOBA Game AI

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated crossagent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.