Reviewerly/exHarmony huggingface dataset:
Machine Learning Transfer Efficiencies for Noisy Quantum Walks

Abstract Quantum effects are known to provide an advantage in particle transfer across networks. In order to achieve this advantage, requirements on both a graph type and a quantum system coherence must be found. Here, it is shown that the process of finding these requirements can be automated by learning from simulated examples. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. The machine learning approach is applied to study noisy quantum walks on cycle graphs of different sizes. It is found that it is possible to predict the existence of quantum advantage for the entire decoherence parameter range, even for graphs outside of the training set. The results are of importance for demonstration of advantage in quantum experiments and pave the way toward automating scientific research and discoveries.
Experimental and Theoretical Study of Thermodynamic Effects in a Quantum Annealer

Quantum devices are affected by intrinsic and environmental noises.An in-depth characterization of noise effects is essential for exploiting noisy quantum computing.To this end, we studied the energy dissipative behavior of a quantum annealer via experiments and numerical simulations.Our investigation adopts a recently proposed technique that interpolates between pure quantum dynamics and pure thermodynamics.Experiments were conducted on a quantum annealer with an anneal pause function, which inserts a thermal relaxation period into the annealing schedule by pausing the transverse field, which is a source of quantum fluctuation.After investigating the special Hamiltonian that characterizes the quantum thermodynamics of the system, we then observed enhancement of thermodynamic signature depending on the anneal pause parameter.The time development of the state vector, observed in the open quantum simulation, provides rich information for investigating phenomena beyond energy-gap analysis.We identified a special eigenstate bridges ground states far-separated in Hilbert space and the transfer probabilities from one ground state to another.This finding can improve the sampling uniformity by reducing the sampling bias in finding the classical ground states in the quantum annealer.Our study does not only characterize the open quantum phenomenon of the specific Hamiltonian but also demonstrates the usefulness of the method in investigating noisy quantum devices.
Demonstration of a 6 state-4 state reference frame independent channel for quantum key distribution

We study a reference frame independent (RFI) quantum key distribution (QKD) protocol using six states for Alice and only four states for Bob, while previous RFI protocols require a six state analyzer for Bob. Our protocol can generate a secure key for any possible phase of the entangled state provided the variation is small compared to the measurement rate, as shown by our numerical key rate analysis. We perform a proof-of-principle experiment using polarization entangled photon pairs. In the presence of a varying rotational phase, we obtain a consistently low error rate of less than 4%, indicating the feasibility of this protocol for QKD. Our RFI protocol is hence beneficial but not limited to applications in a satellite or mobile free-space QKD, where a communication node must limit the resources and restrict the number of measured states to four instead of six.
Tensorized Embedding Layers

The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting.We introduce a novel way of parameterizing embedding layers based on the Tensor Train decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance.We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.
What Can We Do to Improve Peer Review in NLP?

Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious.We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons.There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.
Learnability for the Information Bottleneck

The Information Bottleneck (IB) method provides an insightful and principled approach for balancing compression and prediction for representation learning. The IB objective I ( X ; Z ) - β I ( Y ; Z ) employs a Lagrange multiplier β to tune this trade-off. However, in practice, not only is β chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between β , learnability, the intrinsic nature of the dataset and model capacity. In this paper, we show that if β is improperly chosen, learning cannot happen—the trivial representation P ( Z | X ) = P ( Z ) becomes the global minimum of the IB objective. We show how this can be avoided, by identifying a sharp phase transition between the unlearnable and the learnable which arises as β is varied. This phase transition defines the concept of IB-Learnability. We prove several sufficient conditions for IB-Learnability, which provides theoretical guidance for choosing a good β . We further show that IB-learnability is determined by the largest confident, typical and imbalanced subset of the examples (the conspicuous subset), and discuss its relation with model capacity. We give practical algorithms to estimate the minimum β for a given dataset. We also empirically demonstrate our theoretical conditions with analyses of synthetic datasets, MNIST and CIFAR10.
The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.
Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory

Abstract Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound categorical model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification

Automated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim.Existing models either (i) concatenate all the evidence sentences, leading to the inclusion of redundant and noisy information; or (ii) process each claim-evidence sentence pair separately and aggregate all of them later, missing the early combination of related sentences for more accurate claim verification.Unlike the prior works, in this paper, we propose Hierarchical Evidence Set Modeling (HESM), a framework to extract evidence sets (each of which may contain multiple evidence sentences), and verify a claim to be supported, refuted or not enough info, by encoding and attending the claim and evidence sets at different levels of hierarchy.Our experimental results show that HESM outperforms 7 state-of-the-art methods for fact extraction and claim verification.
A matter of framing: The impact of linguistic formalism on probing results

Deep pre-trained contextualized encoders like BERT (Devlin et al., 2019) demonstrate remarkable performance on a range of downstream tasks.A recent line of research in probing investigates the linguistic knowledge implicitly learned by these models during pretraining.While most work in probing operates on the task level, linguistic tasks are rarely uniform and can be represented in a variety of formalisms.Any linguistics-based probing study thereby inevitably commits to the formalism used to annotate the underlying data.Can the choice of formalism affect probing results?To investigate, we conduct an in-depth cross-formalism layer probing study in role semantics.We find linguistically meaningful differences in the encoding of semantic role-and proto-role information by BERT depending on the formalism and demonstrate that layer probing can detect subtle differences between the implementations of the same linguistic formalism.Our results suggest that linguistic formalism is an important dimension in probing studies and should be investigated along with the commonly used cross-task and cross-lingual experimental settings.
Future of blockchain in healthcare: potential to improve the accessibility, security and interoperability of electronic health records

The lack of accessibility to medical records for both patients and clinicians has long been recognised as a barrier to transparent and efficient healthcare.[1][1] While electronic health record (EHR) systems help address this issue somewhat, many of these systems are heterogeneous, demonstrate
<b>Using GMT</b> for 2D and 3D Modeling of the Ryukyu Trench Topography, Pacific Ocean

Abstract This research focuses on the 2D and 3D geospatial analysis of the Ryukyu Trench, a deep-sea trench located in the western Pacific Ocean between Japan and Taiwan. The aim of the research is to visualize regional differences in the topography of the southern (S) and northern (N) parts of the trench. Technically, the methodology is based on using the Generic Mapping Tools (GMT) scripting toolset, for modelling the General Bathymetric Chart of the Oceans (GEBCO), and Earth Topography and Bathymetry dataset (ETOPO1) raster grids. The results demonstrated topographic differences in the two segments. The most frequent depths lie between −5,000 and −6,000 m. The N part has steeper gradient slopes and deeper bathymetry. Of the depth differences &gt;−6,000 m, S has nine values with depths &gt;−6,800 m while N shows 123 records (max −7,460 m). The submarine terraces of S have gentler slopes compared with the N segment. The technical approach presents GMT-based 2D and 3D cartographic modelling aimed at visualizing regional variations of the seafloor topography.
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after user clicks. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.
Model-size reduction for reservoir computing by concatenating internal states through time

Abstract Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called “reservoirs.” To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (Sci Rep 2:514, 2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error.
Requirements for Blockchain Technology in Supply Chain Management: An Exploratory Case Study

The aim of this research study is to look for possible research opportunities to applying blockchain technology in supply chain management and logistics.In addition, accompanying challenges to utilizing blockchain in supply chain management along with possible solutions are also provided.To fulfil the study objective, both theoretical and empirical approaches are adopted for this study.With respect to theoretical approach, relevant literature on blockchain was reviewed considering both technical and economic aspects, its architecture and implementation challenges.The empirical part of the research was conducted by studying three case companies operating in the domains of wood construction, consulting and regional development, and technology.Three case companies were analysed with respect to the application of blockchain in their supply chain operations.From the study outcomes, it was noticed that blockchain technology can be utilized successfully in supply chain management in various business domains in order to provide, for instance, better services and transparency.
Detection of Head Raising Rate of Students in Classroom Based on Head Posture Recognition

The proliferation of smart mobile terminals has weakened the attention and reduced the learning efficiency of students, making them more likely to lower their heads. To quantify the classroom participation, it is helpful to detect the head raising rate (HRR) of students in classroom. To this end, this paper puts forward a novel method to recognize the HRR of students in classroom. Based on the map of predicted facial features, an extraction method was developed for the salient facial features of students, and used to realize model matching between facial contour and facial organ. Next, the face orientation of each student was determined by soft label coding. After that, a multi-task convolutional neural network (CNN) was constructed to detect the HRR of students. The authors also explained the regularization of the loss function, and the steps of target detection. The proposed method was proved effective through experiments. The research results provide a reference for the application of head posture recognition in other fields.
A Fine-Grained and Lightweight Data Access Control Model for Mobile Cloud Computing

With rapidly increasing adoption of cloud computing and the advancement of today mobile computing, it is inevitable that mobile devices are used to receive and send the data through the mobile cloud platform. This increases the convenience and flexibility of data access over the cloud computing since data users are able to access the shared data anytime, anywhere via mobile devices. However, using mobile devices in accessing shared data in a cloud where the sensitive data is encrypted is not practical because mobile devices have limited computing resources in dealing with heavy cryptographic operations. In this article, we propose a lightweight collaborative ciphertext policy attribute role-based encryption (LW-C-CP-ARBE) scheme to support a fine-grained and lightweight access control for mobile cloud environment. We apply CP-ABE approach as a core cryptographic access control and introduce a new proxy re-encryption (PRE) protocol to reduce data re-encryption and decryption cost for the mobile users. To this end, the overhead in running the cryptographic operation at the end-user device is small. In addition, we develop secure access policy sharing and re-encryption protocol to enable users having write privilege to update the data and request the proxy to perform data re-encryption. Finally, we present the evaluation and experiments to demonstrate the efficiency and practicality of our system.
Supervised Visual Attention for Multimodal Neural Machine Translation

This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image.The proposed visual attention mechanism captures the relationship between a word and an image region more precisely than a conventional visual attention mechanism trained through MNMT in an unsupervised manner.Our experiments on English-German and German-English translation tasks using the Multi30k dataset and on English-Japanese and Japanese-English translation tasks using the Flickr30k Entities JP dataset show that a Transformer-based MNMT model can be improved by incorporating our proposed supervised visual attention mechanism and that further improvements can be achieved by combining it with a supervised cross-lingual attention mechanism (up to +1.61 BLEU, +1.7 METEOR).
IITK at SemEval-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of Internet Memes

Social media is abundant in visual and textual information presented together or in isolation.Memes are the most popular form, belonging to the former class.In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8.The goal of this task is to classify memes based on their emotional content and sentiment.We leverage techniques from Natural Language Processing (NLP) and Computer Vision (CV) towards the sentiment classification of internet memes (Subtask A).We consider Bimodal (text and image) as well as Unimodal (text-only) techniques in our study ranging from the Naïve Bayes classifier to Transformer-based approaches.Our results show that a text-only approach, a simple Feed Forward Neural Network (FFNN) with Word2vec embeddings as input, performs superior to all the others.We stand first in the Sentiment analysis task with a relative improvement of 63% over the baseline macro-F1 score.Our work is relevant to any task concerned with the combination of different modalities.
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus

Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context.LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1,366 languages.We train LangID models on up to 1,629 languages with comparable quality on held-out test sets, but find that human-judged LangID accuracy for web-crawl text corpora created using these models is only around 5% for many lower-resource languages, suggesting a need for more robust evaluation.Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models.We propose two classes of techniques to mitigate these errors: wordlist-based tunable-precision filters (for which we release curated lists in about 500 languages) and transformer-based semi-supervised LangID models, which increase median dataset precision from 5.5% to 71.2%.These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000-language web text corpus.
SemEval-2020 Task 5: Counterfactual Recognition

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020.Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent).Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequents with causal relations.Our task provides a benchmark for counterfactual recognition in natural language with two subtasks.Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not.Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement.During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2.The data, baseline code, and leaderboard can be found at https://competitions.codalab.org/competitions/21691.