Reviewerly/exHarmony huggingface dataset:
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction.At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams.In this paper, we present a new formulation of attention via the lens of the kernel.To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs.This new formulation gives us a better way to understand individual components of the Transformer's attention, such as the better way to integrate the positional embedding.Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer's attention.As an example, we propose a new variant of Transformer's attention which models the input as a product of symmetric kernels.This approach achieves competitive performance to the current state of the art model with less computation.In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.
Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes

The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other. We note that existing methods implicitly address this requirement and leave it to a data-driven pipeline to figure out what makes a subordinate class different from the others. This results in two major limitations: First, the network focuses on the most obvious distinctions between classes and overlooks more subtle inter-class variations. Second, the chance of misclassifying a given sample in any of the negative classes is considered equal, while in fact, confusions generally occur among only the most similar classes. Here, we propose to explicitly force the network to find the subtle differences among closely related classes. In this pursuit, we introduce two key novelties that can be easily plugged into existing end-to-end deep learning pipelines. On one hand, we introduce “diversification block” which masks the most salient features for an input to force the network to use more subtle cues for its correct classification. Concurrently, we introduce a “gradient-boosting” loss function that focuses only on the confusing classes for each sample and therefore moves swiftly along the direction on the loss surface that seeks to resolve these ambiguities. The synergy between these two blocks helps the network to learn more effective feature representations. Comprehensive experiments are performed on five challenging datasets. Our approach outperforms existing methods using similar experimental setting on all five datasets.
Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.
ANHIR: Automatic Non-Rigid Histological Image Registration Challenge

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.
First-principles analysis of cross-resonance gate operation

We present a comprehensive theoretical study of the cross-resonance gate operation covering estimates for gate parameters and gate error as well as analyzing spectator qubits and multiqubit frequency collisions. Starting from the Josephson nonlinearity and by accounting for the eigenstates renormalization, due to counterrotating terms, we derive a starting model for the cross-resonance gate with modified qubit-qubit interaction and drive matrix elements. Employing time-dependent Schrieffer-Wolff perturbation theory, we derive an effective Hamiltonian for the cross-resonance gate with estimates for the gate parameters calculated up to the fourth order in drive amplitude. The model with renormalized eigenstates lead to 10%--15% relative correction of the effective gate parameters compared to Kerr theory. We find that gate operation is strongly dependent on the ratio of qubit-qubit detuning and anharmonicity. In particular, we characterize five distinct regions of operation, and propose candidate parameter choices for achieving high gate speed and low coherent gate error when the cross-resonance tone is equipped with an echo pulse sequence. Furthermore, we generalize our method to include a third spectator qubit and characterize possible detrimental multiqubit frequency collisions.
Contact-tracing apps and alienation in the age of COVID-19

Using a core idea of critical social theory, alienation, we interrogate the failure in the design and adoption of a Stop-COVID app in France. We analyse the political and scientific discourse, to develop an understanding of the conditions giving rise to this failure in this unprecedented moment. We argue that the digital-first solutionist approach taken by the government failed because, as in all Western countries, most stakeholders were alienated from the reality of the COVID-19 pandemic and lacked concrete knowledge of it. Furthermore, the French government and its COVID-19 council excluded relevant scientific experts in favour of quantitative modelling based on abstract partial knowledge. This along with coercion and lack of transparency about the app, reinforced alienation, undermined effectiveness in managing the crisis and resulted in the digital design failure. We suggest that such alienation will prevail in the COVID-19 era characterised by regimes of control, rampant abusive location tracking, and data collection, and where public officials are more concerned with managing effects than seeking causal explanations. The digital-first solutionist approach was adopted, not because digital solutions (to contact tracing) are superior to traditional ones, but by default due to alienation and lack of interdisciplinary cooperation.
The nexus between financial inclusion and economic development: Comparison of old and new EU member countries

The main objective of this study is to investigate the impact of financial inclusion and trade openness on the economic development of 27 European Union (EU) nations. The sample countries are classified into low-income, high-income, old-EU, and new-EU members; this distinction allows us to provide more constructive and practical policy implications. Yearly data from 1995 to 2015 and panel econometric techniques were used. The results show that access, depth, efficiency, and the overall development of financial institutions have significant positive impact on economic growth in both the full sample and sub-samples. We also find that capital, labour, energy consumption and trade openness play an important role in driving economic growth across these panels. Moreover, the impact of financial inclusion on economic output is more significant in low-income and new-EU member countries than in high-income and old-EU countries. Based on these findings, detail policy implications for the entire EU, as well as for its sub-groups, are provided.
A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.
Utility is in the Eye of the User: A Critique of NLP Leaderboards

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).
Skeleton-aware networks for deep motion retargeting

We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set. We leverage the fact that different homeomorphic skeletons may be reduced to a common primal skeleton by a sequence of edge merging operations, which we refer to as skeletal pooling. Thus, our main technical contribution is the introduction of novel differentiable convolution, pooling, and unpooling operators. These operators are skeleton-aware, meaning that they explicitly account for the skeleton's hierarchical structure and joint adjacency, and together they serve to transform the original motion into a collection of deep temporal features associated with the joints of the primal skeleton. In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons. Thus, retargeting can be achieved simply by encoding to, and decoding from this latent space. Our experiments show the effectiveness of our framework for motion retargeting, as well as motion processing in general, compared to existing approaches. Our approach is also quantitatively evaluated on a synthetic dataset that contains pairs of motions applied to different skeletons. To the best of our knowledge, our method is the first to perform retargeting between skeletons with differently sampled kinematic chains, without any paired examples.
Deterministic multi-qubit entanglement in a quantum network

Quantum entanglement is a key resource for quantum computation and quantum communication \cite{Nielsen2010}. Scaling to large quantum communication or computation networks further requires the deterministic generation of multi-qubit entanglement \cite{Gottesman1999,Duan2001,Jiang2007}. The deterministic entanglement of two remote qubits has recently been demonstrated with microwave photons \cite{Kurpiers2018,Axline2018,Campagne2018,Leung2019,Zhong2019}, optical photons \cite{Humphreys2018} and surface acoustic wave phonons \cite{Bienfait2019}. However, the deterministic generation and transmission of multi-qubit entanglement has not been demonstrated, primarily due to limited state transfer fidelities. Here, we report a quantum network comprising two separate superconducting quantum nodes connected by a 1 meter-long superconducting coaxial cable, where each node includes three interconnected qubits. By directly connecting the coaxial cable to one qubit in each node, we can transfer quantum states between the nodes with a process fidelity of $0.911\pm0.008$. Using the high-fidelity communication link, we can prepare a three-qubit Greenberger-Horne-Zeilinger (GHZ) state \cite{Greenberger1990,Neeley2010,Dicarlo2010} in one node and deterministically transfer this state to the other node, with a transferred state fidelity of $0.656\pm 0.014$. We further use this system to deterministically generate a two-node, six-qubit GHZ state, globally distributed within the network, with a state fidelity of $0.722\pm0.021$. The GHZ state fidelities are clearly above the threshold of $1/2$ for genuine multipartite entanglement \cite{Guhne2010}, and show that this architecture can be used to coherently link together multiple superconducting quantum processors, providing a modular approach for building large-scale quantum computers \cite{Monroe2014,Chou2018}.
Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer

The purpose of this study was to examine the results of the prediction of breast cancer, which have been classified based on two types of breast cancer, malignant and benign. The method used in this research is the k-NN algorithm with normalization of min-max and Z-score, the programming language used is the R language. The conclusion is that the highest k accuracy value is k = 5 and k = 21 with an accuracy rate of 98% in the normalization method using the min-max method. Whereas for the Z-score method the highest accuracy is at k = 5 and k = 15 with an accuracy rate of 97%. Thus the min-max normalization method in this study is considered better than the normalization method using the Z-score. The novelty of this research lies in the comparison between the two min-max normalizations and the Z-score normalization in the k-NN algorithm.
An optical neural network using less than 1 photon per multiplication

Deep learning has become a widespread tool in both science and industry. However, continued progress is hampered by the rapid growth in energy costs of ever-larger deep neural networks. Optical neural networks provide a potential means to solve the energy-cost problem faced by deep learning. Here, we experimentally demonstrate an optical neural network based on optical dot products that achieves 99% accuracy on handwritten-digit classification using ~3.1 detected photons per weight multiplication and ~90% accuracy using ~0.66 photons (~2.5 × 10-19 J of optical energy) per weight multiplication. The fundamental principle enabling our sub-photon-per-multiplication demonstration-noise reduction from the accumulation of scalar multiplications in dot-product sums-is applicable to many different optical-neural-network architectures. Our work shows that optical neural networks can achieve accurate results using extremely low optical energies.
Contextualized Perturbation for Textual Adversarial Attack

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs.This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure.CLARE builds on a pre-trained masked language model and modifies the inputs in a contextaware manner.We propose three contextualized perturbations, Replace, Insert and Merge, that allow for generating outputs of varied lengths.CLARE can flexibly combine these perturbations and apply them at any position in the inputs, and is thus able to attack the victim model more effectively with fewer edits.Extensive experiments and human evaluation demonstrate that CLARE outperforms the baselines in terms of attack success rate, textual similarity, fluency and grammaticality.
Cross-Domain Recommendation: Challenges, Progress, and Prospects

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising prospects in CDR.
Demonstration of a High-Fidelity cnot Gate for Fixed-Frequency Transmons with Engineered <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Z</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:math> Suppression

Improving two-qubit gate performance and suppressing crosstalk are major, but often competing, challenges to achieving scalable quantum computation. In particular, increasing the coupling to realize faster gates has been intrinsically linked to enhanced crosstalk due to unwanted two-qubit terms in the Hamiltonian. Here, we demonstrate a novel coupling architecture for transmon qubits that circumvents the standard relationship between desired and undesired interaction rates. Using two fixed frequency coupling elements to tune the dressed level spacings, we demonstrate an intrinsic suppression of the static $ZZ$, while maintaining large effective coupling rates. Our architecture reveals no observable degradation of qubit coherence ($T_1,T_2 > 100~\mu s$) and, over a factor of 6 improvement in the ratio of desired to undesired coupling. Using the cross-resonance interaction we demonstrate a 180~ns single-pulse CNOT gate, and measure a CNOT fidelity of 99.77(2)$\%$ from interleaved randomized benchmarking.
PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carry out the large-scale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers. In addition, in order to gain more insight into the TIR pedestrian tracker, we divide its functions into three components: feature extractor, motion model, and observation model. Then, we conduct three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research.