Reviewerly/exHarmony huggingface dataset:
Scalable Photonic Platform for Real-Time Quantum Reservoir Computing

Quantum reservoir computing (QRC) exploits the information-processing capabilities of quantum systems to solve nontrivial temporal tasks, improving over their classical counterparts. Recent progress has shown the potential of QRC exploiting the enlarged Hilbert space, but real-time processing and the achievement of a quantum advantage with efficient use of resources are prominent challenges towards viable experimental realizations. In this work, we propose a photonic platform suitable for real-time QRC based on a physical ensemble of reservoirs in the form of identical optical pulses recirculating through a closed loop. While ideal operation achieves maximum capacities, statistical noise is shown to undermine any quantum improvement. We propose a strategy to overcome this limitation and sustain the QRC performance when the size of the system is scaled up. The protocol is conceived for experimental implementations to be viable with current technology.
A survey of techniques for optimizing transformer inference

Recent years have seen a phenomenal rise in the performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and Computer Vision (CV) domains. Transformer-based networks such as ChatGPT have impacted the lives of common men. However, the quest for high predictive performance has led to an exponential increase in transformers’ memory and compute footprint. Researchers have proposed techniques to optimize transformer inference at all levels of abstraction. This paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level. We further review hardware-level optimization techniques and the design of novel hardware accelerators for transformers. We summarize the quantitative results on the number of parameters/FLOPs and the accuracy of several models/techniques to showcase the tradeoff exercised by them. We also outline future directions in this rapidly evolving field of research. We believe that this survey will educate both novice and seasoned researchers and also spark a plethora of research efforts in this field.
LightGlue: Local Feature Matching at Light Speed

We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improvements. Cumulatively, they make LightGlue more efficient – in terms of both memory and computation, more accurate, and much easier to train. One key property is that LightGlue is adaptive to the difficulty of the problem: the inference is much faster on image pairs that are intuitively easy to match, for example because of a larger visual overlap or limited appearance change. This opens up exciting prospects for deploying deep matchers in latency-sensitive applications like 3D reconstruction. The code and trained models are publicly available at github.com/cvg/LightGlue.
The deterministic interpretation of the Kalman filter

It is known that the Kalman filter has both stochastic and deterministic interpretations, whereby the deterministic interpretation relates the prediction of the filter to the response of the plant driven by the minimising least squares disturbances acting thereon. Whilst the deterministic interpretation is known, the contribution of this note is to provide an alternative, simple and self-contained proof of these properties in the discrete case. The presentation allows an efficient derivation of the key deterministic properties, i.e. that the residuals computed by the Kalman filter (both forwards and backwards) are identical to the least squares disturbances. Results with variations are given for both zero and non-zero initial conditions. Finally, a numerical example is given to illustrate the deterministic properties.
Red-Black Relaxed Plan Heuristics Reloaded

Despite its success, the delete relaxation has significant pitfalls. In an attempt to overcome these pitfalls, recent work has devised so-called red-black relaxed plan heuristics, where red variables take the relaxed semantics (accumulating their values), while black variables take the regular semantics. These heuristics were shown to significantly improve over standard delete relaxation heuristics. However, the experiments also brought to light a major weakness: Being based on repairing fully delete-relaxed plans, the returned estimates depend on arbitrary choices made in such plans. This can lead to huge over-estimation in arbitrary subsets of states. Here we devise a new red-black planning method not based on repairing relaxed plans, getting rid of much of this variance. Our experiments show a significant improvement over previous red-black relaxed plan heuristics, and other related methods.
Adsorption to Heterogeneous Surfaces

Adsorption of metals in soils, sediments and aquifers is a complex phenomenon. Metal ions are charged species which may form various soluble complexes both with inorganic and organic ligands. The presence of soluble metal complexes often reduces the metal adsorption compared with the absence of these dissolved complexes. A change in the surface potential affects metal adsorption through a change in electrostatic repulsion or attraction. The resulting cadmium adsorption is only a function of the various reactive surfaces that are present and the resulting pH, but also depends on the degree of competition with zinc ions. The adsorption of CO2 as such hardly influences the surface charge. Calculations show that the electric potential in the surface layer and the second adsorption layer is hardly influenced by the adsorption. The knowledge with respect to adsorption phenomena on metal (hydr)oxides is far more advanced than the same for humic materials.
A Data-Driven Approach to Question Subjectivity Identification in Community Question Answering

Automatic Subjective Question Answering (ASQA), which aims at answering users'subjective questions using summaries of multiple opinions, becomes increasingly important. One challenge of ASQA is that expected answers for subjective questions may not readily exist in the Web. The rising and popularity of Community Question Answering (CQA) sites, which provide platforms for people to post and answer questions, provides an alternative to ASQA. One important task of ASQA is question subjectivity identification, which identifies whether a user is asking a subjective question. Unfortunately, there has been little labeled training data available for this task. In this paper, we propose an approach to collect training data automatically by utilizing social signals in CQA sites without involving any manual labeling. Experimental results show that our data-driven approach achieves 9.37% relative improvement over the supervised approach using manually labeled data, and achieves 5.15% relative gain over a state-of-the-art semi-supervised approach. In addition, we propose several heuristic features for question subjectivity identification. By adding these features, we achieve 11.23% relative improvement over word n-gram feature under the same experimental setting.
CDBR: A semi-automated collaborative execute-before-after dependency-based requirement prioritization approach

The success of requirement prioritization process largely depends upon how well different constraints and influential factors are handled by stakeholders and developers while prioritization. The main goal of this research is to present a semi-automated dependency based collaborative requirement prioritization approach (CDBR), which uses linguistic values, execute-before-after (EBA) relation among requirements and machine learning algorithm to minimize the difference of opinion between stakeholder and developers for effective collaboration and for better approximation of final prioritization results, acceptable to both. The presented approach targets three major constraints rarely addressed in existing work, namely dependencies among requirements, communication among stakeholder and developers and the issue of scalability. Results of performance assessment conducted on several different requirement sets and on a case study by comparing CDBR with other state of the art approaches namely, AHP and IGA. The results are accurate and comparable in terms of effectiveness, efficiency, scalability and disagreement concerns among stakeholder and developers which in turn provides robustness to decision making process of awarding more importance to some requirements over others. CDBR overpowers AHP and IGA in terms of efficiency and processing time respectively.
Harmonizable mixture kernels with variational Fourier features

The expressive power of Gaussian processes depends heavily on the choice of kernel. In this work we propose the novel harmonizable mixture kernel (HMK), a family of expressive, interpretable, non-stationary kernels derived from mixture models on the generalized spectral representation. As a theoretically sound treatment of non-stationary kernels, HMK supports harmonizable covariances, a wide subset of kernels including all stationary and many non-stationary covariances. We also propose variational Fourier features, an inter-domain sparse GP inference framework that offers a representative set of ‘inducing frequencies’. We show that harmonizable mixture kernels interpolate between local patterns, and that variational Fourier features offers a robust kernel learning framework for the new kernel family.
Propositional Modal Logic with Implicit Modal Quantification

Propositional term modal logic is interpreted over Kripke structures with unboundedly many accessibility relations and hence the syntax admits variables indexing modalities and quantification over them. This logic is undecidable, and we consider a variable-free propositional bi-modal logic with implicit quantification. Thus $$[\forall ]\alpha $$ asserts necessity over all accessibility relations and $$[\exists ]\alpha $$ is classical necessity over some accessibility relation. The logic is associated with a natural bisimulation relation over models and we show that the logic is exactly the bisimulation invariant fragment of a two sorted first order logic. The logic is easily seen to be decidable and admits a complete axiomatization of valid formulas. Moreover the decision procedure extends naturally to the ‘bundled fragment’ of full term modal logic.
A Cloud Service Framework for Virtual Try-On of Footwear in Augmented Reality

This paper presents an experimental cloud service framework for design evaluation of personalized footwear in augmented reality (AR) via networks. The service allows users to ubiquitously perceive themselves trying on three-dimensional (3D) shoe models in a video stream. They upload a clip of feet motion recorded by a commercial depth camera to the cloud. A new clip is generated to display the try-on process and made available to specified receivers via video streaming on a mobile device. The framework design emphasizes making most use of open-source software and off-the-shelf technologies commercially available. A prototyping cloud system implementing the framework demonstrates the practical value of virtual footwear try-on as AR as a service (ARaaS). This experimental study realizes the idea of human-centric design evaluation in modern e-commerce. The cloud framework may provide a feasible example to improve the usability for real-time applications of AR.
Toward Device-Free Micro-Gesture Tracking via Accurate Acoustic Doppler-Shift Detection

As a universal interaction method, hand and finger gestures can express people's intention directly and clearly in daily life, and has been one of the hotspots in human-computer interaction community.In this paper, we present DMT, a device-free finger gesture tracking system that can track and recognize the finger motion accurately.To achieve this, we transform the mobile device, such as a smart phone, into an active sonar system by establishing inaudible audio links between the built-in speakers and microphone.The finger motion will have an effect (e.g., Doppler-shift) on the audio signals, which makes it possible to track the finger motion according to the received signal characteristics at the microphone side.Due to the small reflection energy and slow moving speed of the finger, the existing methods cannot detect the Dopplershift accurately.To this end, a Fourier fitting-based method is proposed in the DMT to accurately detect the Doppler-shift.With the detected Doppler-shift, the DMT can track the finger motion with high accuracy.The DMT supports all kinds of finger gestures interaction, including characters and shapes.Extensive experiments demonstrate the high accuracy and robustness of the DMT in dynamic environments.
Face Alignment via Multi-Regressors Collaborative Optimization

Face alignment is a fundamental step in facial image analysis. To solve the non-convex optimization problem, most cascade-based regression approaches conventionally utilize a single regressor to cover the entire optimization space. These measures are prone to average conflicting gradient directions, especially when applied to faces in the unconstrained condition with various poses and expressions. In this paper, we present an effective face alignment approach based on the multi-regressors collaborative optimization. The foundation of our method is the cascaded regression (CR) that has recently established itself as one of the most practical and effective frameworks for localizing the facial landmarks. CR is interpreted as a learning-based approach to iteratively optimize an objective function. On this basis, in all iterations, the proposed algorithm further divides the sample space into several clusters, in each of which, samples with similar gradient directions and one separate local regressor are learned. During the prediction stage, the unseen landmarks of a face image are evaluated by a linear combination of estimations from all cluster regressors with different weights. The experimental results demonstrate the advantages of our method on the general unconstrained images.
Interaction Concept for Mixed-Initiative Mission Planning on Multiple Delegation Levels in Multi-UCAV Fighter Missions

The management of several UCAVs from board a manned combat aircraft is a challenging problem. While fully automatic planners lead to complacency and low workload, manual planning leads to a much too heavy workload for the pilot. In this article, we present a mixed-initiative planning concept, consisting of a delegation and an assistance part. The delegation part of the concept enables the pilot to command tasks to the UCAVs on a team-, task- and parameter-based level, which are taken as planning constraints. The assistance part supports the pilot in the plan creation process and can proactively counteract possible failures. An initial validation of the implemented concept is presented.
Faster quantum computation with permutations and resonant couplings

Recently, there has been increasing interest in designing schemes for quantum computations that are robust against errors. Although considerable research has been devoted to quantum error correction schemes, much less attention has been paid to optimizing the speed it takes to perform a quantum computation and developing computation models that act on decoherence-free subspaces. Speeding up a quantum computation is important, because fewer errors are likely to result. Encoding quantum information in a decoherence-free subspace is also important, because errors would be inherently suppressed. In this paper, we consider quantum computation in a decoherence-free subspace and also optimize its speed. To achieve this, we perform certain single-qubit quantum computations by simply permuting the underlying qubits. In this paper, we make progress in understanding the extent in which quantum computation can be performed by permuting qubits. Namely, we provide two different subgroups of the single qubit Clifford group that can be computed solely by permutations. To make the quantum computation universal for one of the schemes, we rely on resonant couplings motivated from physics. Our first scheme potentially improves the speed in which a quantum computation can be done. We also reduce the problem of finding the existence of permutational Clifford gates to that finding an upper bound for the rank of certain matrices.
Teachers’ Strategies to Design Media to Implement Communicative Leaning in Public Schools

In order to implement communicative learning activities, a teacher must be able to design various means or media so that learning process keeps running well. A teacher is expected not to stutter in designing media for various learning needs. The main purpose of this study is to examine the strategies done by teachers in designing media to implement communicative learning. This research is a descriptive research in which the data were collected from teachers of Madrasah Aliyah Negeri (public Islamic senior high schools) in Pekanbaru city. The research sample was 36 teachers. The instrument used to collect the data was a scale questionnaire. The data were descriptively analyzed. Each item of the questionnaire becomes the basis and the principal of the research analysis. The research findings reveal that to implement communicative learning, the teachers designed learning media. The strategies they used are making use of e-learning and making use of various teaching and learning facilities, creating student worksheets and compiling teaching materials, and presenting slides or other media in accordance with the teaching materials. Each of the strategy is set at a very high standard. The teachers’ strategy to design media at the public schools runs well. It can be seen from the average of the strategy that is 4.48 and the average belongs to very high category. The data also clearly reveal that the teachers are creative and innovative in designing media to implement communicative learning.
Politics of Designing Visions of the Future

Scenarios for policy and the public are increasingly given form by designers. For design, this means ideas about the future – futurity – is at stake, particularly in genres of ‘concept’, ‘critical’ and ‘persuasive’ design. While critical approaches are present in futures studies and political philosophy, design assumptions and preferences are typically not explicit, including gender norms, socio-ecological practices and power structures. Calling for further studies of the politics of design visions, I outline possible approaches and elaborate through the example ‘Switch! Energy Futures’. I reflect upon how competing visions and politics of sustainability become explicit through our process, aesthetics and stakeholders.
Support vector machine with quantile hyper-spheres for pattern classification

This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification. The idea of QHSVM is to build two quantile hyper-spheres with the same center for positive or negative training samples. Every quantile hyper-sphere is constructed by using pinball loss instead of hinge loss, which makes the new classification model be insensitive to noise, especially the feature noise around the decision boundary. Moreover, the robustness and generalization of QHSVM are strengthened through maximizing the margin between two quantile hyper-spheres, maximizing the inner-class clustering of samples and optimizing the independent quadratic programming for a target class. Besides that, this paper proposes a novel local center-based density estimation method. Based on it, ρ-QHSVM with surrounding and clustering samples is given. Under the premise of high accuracy, the execution speed of ρ-QHSVM can be adjusted. The experimental results in artificial, benchmark and strip steel surface defects datasets show that the QHSVM model has distinct advantages in accuracy and the ρ-QHSVM model is fit for large-scale datasets.
Metric learning by simultaneously learning linear transformation matrix and weight matrix for person re‐identification

Mahalanobis metric learning is one of the most popular methods for person re‐identification. Most existing metric learning methods regularly formulate the person re‐identification as an unconstrained optimisation problem and the constraints on the Mahalanobis matrix are seldom imposed. In addition, weights are often used to model the relationships between different variables but they often suffer from boundedness caused by their hand‐designed feature. Taking the above two disadvantages into consideration, the authors propose a new metric learning method for person re‐identification, which formulates the metric learning problem as a constrained optimisation problem by imposing a constraint on the linear transformation matrix. Furthermore, they treat the weights as unknown variables and introduce a weight learning method instead of designing weight intuitively. Finally, they evaluate the proposed method on two challenging person re‐identification databases and show that it performs favourably against the state‐of‐the‐art approaches.
You always look again: Learning to detect the unseen objects

Object detection has always attracted a lot of attention in computer vision due to its practical applications, i.e., robotics engineering, autonomous vehicles, and surveillance systems. Recently deep learning approaches have successfully improved the performance of object detection by a significant amount. However, there exist many challenging objects in the images that state-of-the-art approaches still fail to detect. In this paper, we propose an efficient approach that intentionally learns to detect the unseen (missing) objects. In particular, we utilize a dual-level of deep networks to efficiently detect difficult objects in images. The extensive experiments on three benchmarking datasets, PASCAL VOC, KITTI, and MS-COCO, show the superiority of our approach over the state-of-the-art methods.
ViziQuer: A Visual Notation for RDF Data Analysis Queries

Visual SPARQL query notations aim at easing the RDF data querying task. At the current state of the art there is still no generally accepted visual graph-based notation suitable to describe RDF data analysis queries that involve aggregation and subqueries. In this paper we present a visual diagram-centered notation for SPARQL select query formulation, capable to handle aggregate/statistics queries and hierarchic queries with subquery structure. The notation is supported by a web-based prototype tool. We present the notation examples, describe its syntax and semantics and describe studies with possible end users, involving both IT and medicine students.