Reviewerly/exHarmony huggingface dataset:
Quantum-enhanced passive remote sensing

We investigate theoretically the ultimate resolution that can be achieved with passive remote sensing in the microwave regime used, e.g., on board of satellites observing Earth, such as the soil moisture and ocean salinity (SMOS) mission. We give a fully quantum mechanical analysis of the problem, starting from thermal distributions of microscopic currents on the surface to be imaged that lead to a mixture of coherent states of the electromagnetic field which are then measured with an array of antennas. We derive the optimal detection modes and measurement schemes that allow one to saturate the quantum Cram\'er-Rao bound for the chosen parameters that determine the distribution of the microscopic currents. For parameters comparable to those of SMOS, a quantum enhancement of the spatial resolution by more than a factor of 20 should be possible with a single measurement and a single detector, and a resolution down to the order of 1 m and less than a $\frac{1}{10}$ K for the theoretically possible maximum number of measurements.
Quantum computational quantitative trading: high-frequency statistical arbitrage algorithm

Abstract Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading by utilizing variable time condition number estimation and quantum linear regression. The algorithm complexity has been reduced from the classical benchmark O ( N 2 d ) to <?CDATA $O(\sqrt{dN}{\kappa }_{0}^{2}\,\mathrm{log}{(1/{\epsilon})}^{2})\left.\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mrow> <mml:mi>d</mml:mi> <mml:mi>N</mml:mi> </mml:mrow> </mml:msqrt> <mml:msubsup> <mml:mrow> <mml:mi>κ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>0</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace width="0.17em" /> <mml:mi>log</mml:mi> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mi>ϵ</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mfenced close=")" open="" /> </mml:math> , where N is the length of trading data, and d is the number of stocks, κ 0 is the condition number and ϵ is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.
Lossless Medical Image Compression by Using Difference Transform

This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.
Assessing User Experience of Text Readability with Eye Tracking in Virtual Reality

Virtual Reality (VR) technology is mostly used in gaming, videos, engineering applications, and training simulators. One thing which is shared among all of them is the necessity to display text. Text reading experience is not always in focus for VR systems because of limited hardware capabilities, lack of standardization, user interface (UI) design flaws, and physical design of Head-Mounted Displays (HMDs). With this paper, key variables from the UI design side were researched that can improve text reading user experience in VR. Therefore four important points for reading in VR application were selected to be focused on: 1) Difference in canvas type (flat/curved), 2) Contrast on virtual scene (light/dark), 3) Number of columns in layout (1 column/2 column/3 column) 4) Text distance from the subject (1.5 m/6.5 m). For a user study a VR app for Oculus Quest was developed, enabling the possibility to display text while varying some of the features important for readability in VR. This user experiment has shown parameters that are important for text reading experience in VR. Specifically, subjects performed very well when the text was on a 6.5-meter distance from the subject with font size 22pt, on a flat canvas with one column layout. When it comes to physiological variables, the conditions measurements were behaving similarly, as all of the selected parameters were in line with the design guidelines. Therefore, selection on final settings should be more oriented towards user experience and preferences.
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value At Risk

This letter aims to solve a safe reinforcement learning (RL) problem with risk measure-based constraints. As risk measures, such as conditional value at risk (CVaR), focus on the tail distribution of cost signals, constraining risk measures can effectively prevent a failure in the worst case. An on-policy safe RL method, called TRC, deals with a CVaR-constrained RL problem using a trust region method and can generate policies with almost zero constraint violations with high returns. However, to achieve outstanding performance in complex environments and satisfy safety constraints quickly, RL methods are required to be sample efficient. To this end, we propose an off-policy safe RL method with CVaR constraints, called off-policy TRC. If off-policy data from replay buffers is directly used to train TRC, the estimation error caused by the distributional shift results in performance degradation. To resolve this issue, we propose novel surrogate functions, in which the effect of the distributional shift can be reduced, and introduce an adaptive trust-region constraint to ensure a policy not to deviate far from replay buffers. The proposed method has been evaluated in simulation and real-world environments and satisfied safety constraints within a few steps while achieving high returns even in complex robotic tasks.
Toward Trust-Based Recommender Systems for Open Data: A Literature Review

In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental concepts and illustrate the potential of using trust-based recommender systems for open data portals. We hope this literature review can benefit practitioners in the field of open data and facilitate the discussion of future work.
Metodologías activas y diseño universal para el aprendizaje

Universal Design for Learning (UDL) is a model that, based on the results of educational practice and research, learning theories, technologies and advances in neuroscience, combines an inclusive view and approach to teaching with proposals for its application in practice1. The universal design for learning (UDL) is not a methodology. This article begins with this statement, in which it will try to address how the change of perspective proposed by UDL can affect the implementation of all kinds of methodologies so that a space for participation is generated for all students. It attempts to focus on how the different guidelines and verification points of the UDL become an effective help guide for teachers in making decisions about the implementation of universal strategies, in the different active methodologies that are desired. use, to generate learning sequences where participation and progress for all is possible.
Generalized Affordance Templates for Mobile Manipulation

This paper presents recent advances to the Affordance Template (AT) task description language. Affordance Templates provide standardized, easy-to-use tools for defining robot manipulation tasks that provide a high level of augmented reality capabilities to facilitate human-in-the-loop operation, but can also be used to support robot autonomy when coupled with various planning tools. While initially defined in terms of end effector waypoint sequences for bimanual robots, such as the NASA Valkyrie and Robonaut 2, this paper extends the original specification to support integrated mobile manipulation, object-centric template definitions, autonomous grasp determination, and integration with custom and off-the-shelf collision free motion planners. ATs have proved highly adaptable to new robots and new domains by both the authors and third-party groups, and have been used in numerous contexts for NASA, DOD, and industry. As such, we believe that the AT framework provides a strong foundation for robot development in multiple real-world contexts that can be increasingly built upon and expanded to meet the challenges of many new applications.
On the probability–quality paradox in language generation

When generating natural language from neural probabilistic models, high probability does not always coincide with high quality: It has often been observed that mode-seeking decoding methods, i.e., those that produce high-probability text under the model, lead to unnatural language.On the other hand, the lower-probability text generated by stochastic methods is perceived as more human-like.In this note, we offer an explanation for this phenomenon by analyzing language generation through an information-theoretic lens.Specifically, we posit that human-like language should contain an amount of information (quantified as negative log-probability) that is close to the entropy of the distribution over natural strings.Further, we posit that language with substantially more (or less) information is undesirable.We provide preliminary empirical evidence in favor of this hypothesis; quality ratings of both human and machine-generated text-covering multiple tasks and common decoding strategies-suggest high-quality text has an information content significantly closer to the entropy than we would expect by chance.
Eye Gaze and Self-attention: How Humans and Transformers Attend Words in Sentences

Attention describes cognitive processes that are important to many human phenomena including reading.The term is also used to describe the way in which transformer neural networks perform natural language processing.While attention appears to be very different under these two contexts, this paper presents an analysis of the correlations between transformer attention and overt human attention during reading tasks.An extensive analysis of human eye tracking datasets showed that the dwell times of human eye movements were strongly correlated with the attention patterns occurring in the early layers of pre-trained transformers such as BERT.Additionally, the strength of a correlation was not related to the number of parameters within a transformer.This suggests that something about the transformers' architecture determined how closely the two measures were correlated.
Interpreting Character Embeddings With Perceptual Representations: The Case of Shape, Sound, and Color

Character-level information is included in many NLP models, but evaluating the information encoded in character representations is an open issue.We leverage perceptual representations in the form of shape, sound, and color embeddings and perform a representational similarity analysis to evaluate their correlation with textual representations in five languages.This cross-lingual analysis shows that textual character representations correlate strongly with sound representations for languages using an alphabetic script, while shape correlates with featural scripts.We further develop a set of probing classifiers to intrinsically evaluate what phonological information is encoded in character embeddings.Our results suggest that information on features such as voicing are embedded in both LSTM and transformer-based representations.
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning

Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to novel tasks.Three main strategies have emerged for making use of multiple supervised datasets during fine-tuning: training on an intermediate task before training on the target task (STILTs), using multi-task learning (MTL) to train jointly on a supplementary task and the target task (pairwise MTL), or simply using MTL to train jointly on all available datasets (MTL All ).In this work, we compare all three TL methods in a comprehensive analysis on the GLUE dataset suite.We find that there is a simple heuristic for when to use one of these techniques over the other: pairwise MTL is better than STILTs when the target task has fewer instances than the supporting task and vice versa.We show that this holds true in more than 92% of applicable cases on the GLUE dataset and validate this hypothesis with experiments varying dataset size.The simplicity and effectiveness of this heuristic is surprising and warrants additional exploration by the TL community.Furthermore, we find that MTL All is worse than the pairwise methods in almost every case.We hope this study will aid others as they choose between TL methods for NLP tasks. 1
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings

Contrastive learning has shown great potential in unsupervised sentence embedding tasks, e.g., SimCSE (Gao et al., 2021).However, We find that these existing solutions are heavily affected by superficial features like the length of sentences or syntactic structures.In this paper, we propose a semantics-aware contrastive learning framework for sentence embeddings, termed Pseudo-Token BERT (PT-BERT), which is able to exploit the pseudotoken space (i.e., latent semantic space) representation of a sentence while eliminating the impact of superficial features such as sentence length and syntax.Specifically, we introduce an additional pseudo token embedding layer independent of the BERT encoder to map each sentence into a sequence of pseudo tokens in a fixed length.Leveraging these pseudo sequences, we are able to construct same-length positive and negative pairs based on the attention mechanism to perform contrastive learning.In addition, we utilize both the gradientupdating and momentum-updating encoders to encode instances while dynamically maintaining an additional queue to store the representation of sentence embeddings, enhancing the encoder's learning performance for negative examples.Experiments show that our model outperforms the state-of-the-art baselines on six standard semantic textual similarity (STS) tasks.Furthermore, experiments on alignments and uniformity losses, as well as hard examples with different sentence lengths and syntax, consistently verify the effectiveness of our method.
Eye Movements in Sports Research and Practice: Immersive Technologies as Optimal Environments for the Study of Gaze Behavior

Head-mounted eye tracking has been fundamental for developing an understanding of sporting expertise, as the way in which performers sample visual information from the environment is a major determinant of successful performance. There is, however, a long running tension between the desire to study realistic, in situ gaze behavior and the difficulties of acquiring accurate ocular measurements in dynamic and fast-moving sporting tasks. Here, we describe how immersive technologies, such as virtual reality, offer an increasingly compelling approach for conducting eye movement research in sport. The possibility of studying gaze behavior in representative and realistic environments, but with high levels of experimental control, could enable significant strides forward for eye tracking in sport and improve understanding of how eye movements underpin sporting skills. By providing a rationale for virtual reality as an optimal environment for eye tracking research, as well as outlining practical considerations related to hardware, software, and data analysis, we hope to guide researchers and practitioners in the use of this approach.
Machine Learning for Exhibition Recommendation in a Museum’s Virtual Tour Application

The museum visit is having a crisis during the COVID-19 pandemic.SMBII Museum in Palembang has a remarkable decrease of visitors up to 90%.A strategy is needed to increase museum visits and enable educational and tourism roles in a pandemic situation.This paper evaluates the machine learning model for exhibition recommendations given to visitors through virtual tour applications.Exploring unfamiliar museum exhibitions to visitors through virtual museum applications will be tedious.If virtual collections are ancient and do not display any interest, they will quickly lead to boredom and reluctance to explore virtual museums.For this reason, an effective method is needed to provide suggestions or recommendations that meet the interests of visitors based on the profiles of museum visitors, making it easier for visitors to find exciting exhibition rooms for learning and tourism.Machine learning has proven its effectiveness for predictions and recommendations.This study evaluates several machine learning classifiers for exhibition recommendations and development of virtual tour applications that applied machine learning classifiers with the best performance based on the model evaluation.The experimental results show that the KNN model performs best for exhibition recommendations with cross-validation accuracy = 89.09% and F-Measure = 90.91%.The SUS usability evaluation on the exhibition recommender feature in the virtual tour application of SMBII museum shows average score of 85.83.The machine learning-based recommender feature usability is acceptable, making it easy and attractive for visitors to find an exhibition that might match their interests.
Cluster &amp; Tune: Boost Cold Start Performance in Text Classification

In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is prone to produce poor performance.We suggest a method to boost the performance of such models by adding an intermediate unsupervised classification task, between the pretraining and fine-tuning phases.As such an intermediate task, we perform clustering and train the pre-trained model on predicting the cluster labels.We test this hypothesis on various data sets, and show that this additional classification phase can significantly improve performance, mainly for topical classification tasks, when the number of labeled instances available for fine-tuning is only a couple of dozen to a few hundred.
Effective combination of pretrained models - KIT@IWSLT2022

Pretrained models in acoustic and textual modalities can potentially improve speech translation for both Cascade and End-to-end approaches.In this evaluation, we aim at empirically looking for the answer by using the wav2vec, mBART50 and DeltaLM models to improve text and speech translation models.The experiments showed that the presence of these models together with an advanced audio segmentation method results in an improvement over the previous End-to-end system by up to 7 BLEU points.More importantly, the experiments showed that given enough data and modeling capacity to overcome the training difficulty, we can outperform even very competitive Cascade systems.In our experiments, this gap can be as large as 2.0 BLEU points, the same gap that the Cascade often led over the years.
Unified Line and Paragraph Detection by Graph Convolutional Networks

We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem. Given a set of text detection boxes that roughly correspond to words, a text line is a cluster of boxes and a paragraph is a cluster of lines. These clusters form a two-level tree that represents a major part of the layout of a document. We use a graph convolutional network to predict the relations between text detection boxes and then build both levels of clusters from these predictions. Experimentally, we demonstrate that the unified approach can be highly efficient while still achieving state-of-the-art quality for detecting paragraphs in public benchmarks and real-world images.
A reliable calibration method for accurate determination of major and trace elements in fluorapatite by LA-ICP-MS without an internal standard

During in situ determination of major and trace element concentrations of minerals by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), an internal standard is needed.
NACA: A Joint Distortion-Based Non-Additive Cost Assignment Method for Video Steganography

Lots of non-additive cost assignment methods designed for image steganography have improved the security of stego images, but surprisingly there are only a few such non-additive cost assignment methods for video steganography. In this paper, we first analyze the distortion propagation by decomposing it into inner-block, inter-block, and inter-frame distortion drifts. Then, we determine the inner-block distortion drift (caused by the embedding modifications) that induces the inter-block and the inter-frame distortion drifts, using prediction. Based on the findings, we compose a joint distortion for all transform coefficients in each transform block. Finally, we propose a joint distortion-based non-additive cost assignment (NACA) method to reduce the inner-block distortion drift by distortion compensation. This allows us to further reduce both intra-frame (inter-block) and inter-frame distortion drifts, and achieve enhanced security. We conduct extensive experiments to evaluate the performance of NACA, in terms of security and coding performance. The evaluation results demonstrate that NACA achieves improved security and visual stego video quality, and maintains a very marginal increase in bit-rate, in comparison to four other competing additive cost assignment approaches.