Reviewerly/exHarmony huggingface dataset:
Commitments to Quantum States

What does it mean to commit to a quantum state? In this work, we propose a simple answer: a commitment to quantum messages is binding if, after the commit phase, the committed state is hidden from the sender's view. We accompany this new definition with several instantiations. We build the first non-interactive succinct quantum state commitments, which can be seen as an analogue of collision-resistant hashing for quantum messages. We also show that hiding quantum state commitments (QSCs) are implied by any commitment scheme for classical messages. All of our constructions can be based on quantum-cryptographic assumptions that are implied by but are potentially weaker than one-way functions.
External Memory Fully Persistent Search Trees

We present the first fully-persistent external-memory search tree achieving amortized I/O bounds matching those of the classic (ephemeral) B-tree by Bayer and McCreight. The insertion and deletion of a value in any version requires amortized O(logB Nv) I/Os and a range reporting query in any version requires worst-case O(logB Nv + K/B) I/Os, where K is the number of values reported, Nv is the number of values in the version v of the tree queried or updated, and B is the external-memory block size. The data structure requires space linear in the total number of updates. Compared to the previous best bounds for fully persistent B-trees [Brodal, Sioutas, Tsakalidis, and Tsichlas, SODA 2012], this paper eliminates from the update bound an additive term of O(log2 B) I/Os. This result matches the previous best bounds for the restricted case of partial persistent B-trees [Arge, Danner and Teh, JEA 2003]. Central to our approach is to consider the problem as a dynamic set of two-dimensional rectangles that can be merged and split.
The Round Complexity of Statistical MPC with Optimal Resiliency

In STOC 1989, Rabin and Ben-Or (RB) established an important milestone in the fields of cryptography and distributed computing by showing that every functionality can be computed with statistical (information-theoretic) security in the presence of an active (aka Byzantine) rushing adversary that controls up to half of the parties. We study the round complexity of general secure multiparty computation and several related tasks in the RB model.
A Characterization of List Learnability

A classical result in learning theory shows the equivalence of PAC learnability of binary hypothesis classes and the finiteness of VC dimension. Extending this to the multiclass setting was an open problem, which was settled in a recent breakthrough result characterizing multiclass PAC learnability via the DS dimension introduced earlier by Daniely and Shalev-Shwartz.
Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank

We give a simple proof of the matrix Spencer conjecture up to poly-logarithmic rank: given symmetric d × d matrices A1,…,An each with ||Ai||op ≤ 1 and rank at most n/log3 n, one can efficiently find ± 1 signs x1,…,xn such that their signed sum has spectral norm ||∑i=1n xi Ai||op = O(√n). This result also implies a logn − Ω( loglogn) qubit lower bound for quantum random access codes encoding n classical bits with advantage ≫ 1/√n.
Approximating Binary Longest Common Subsequence in Almost-Linear Time

The Longest Common Subsequence (LCS) is a fundamental string similarity measure, and computing the LCS of two strings is a classic algorithms question. A textbook dynamic programming algorithm gives an exact algorithm in quadratic time, and this is essentially best possible under plausible fine-grained complexity assumptions, so a natural problem is to find faster approximation algorithms. When the inputs are two binary strings, there is a simple 1/2-approximation in linear time: compute the longest common all-0s or all-1s subsequence. It has been open whether a better approximation is possible even in truly subquadratic time. Rubinstein and Song showed that the answer is yes under the assumption that the two input strings have equal lengths. We settle the question, generalizing their result to unequal length strings, proving that, for any ε>0, there exists δ>0 and a (1/2+δ)-approximation algorithm for binary LCS that runs in n1+ε time. As a consequence of our result and a result of Akmal and Vassilevska-Williams, for any ε>0, there exists a (1/q+δ)-approximation for LCS over q-ary strings in n1+ε time.
A Strongly Polynomial Algorithm for Approximate Forster Transforms and Its Application to Halfspace Learning

The Forster transform is a method of regularizing a dataset by placing it in radial isotropic position while maintaining some of its essential properties. Forster transforms have played a key role in a diverse range of settings spanning computer science and functional analysis. Prior work had given weakly polynomial time algorithms for computing Forster transforms, when they exist. Our main result is the first strongly polynomial time algorithm to compute an approximate Forster transform of a given dataset or certify that no such transformation exists. By leveraging our strongly polynomial Forster algorithm, we obtain the first strongly polynomial time algorithm for distribution-free PAC learning of halfspaces. This learning result is surprising because proper PAC learning of halfspaces is equivalent to linear programming. Our learning approach extends to give a strongly polynomial halfspace learner in the presence of random classification noise and, more generally, Massart noise.
Art, technology and the Internet of Living Things

Intelligence augmentation was one of the original goals of computing. Artificial Intelligence (AI) inherits this project and is at the leading edge of computing today. Computing can be considered an extension of brain and body, with mathematical prowess and logic fundamental to the infrastructure of computing. Multimedia computing-sensing, analyzing, and translating data to and from visual images, animation, sound and music, touch and haptics, as well as smell-is based on our human senses and is now commonplace. We use data visualization and sonification, as well as data mining and analysis, to sort through the complexity and vast volume of data coming from the world inside and around us. It helps us 'see' in new ways. We can think of this capacity as a new kind of "digital glasses". The Internet of Living Things (IOLT) is potentially an even more profound extension of ourselves to the world: a network of electronic devices embedded into objects, but now with subcutaneous, ingestible devices, and embedded sensors that include people and other living things. Like the Internet of Things (IOT), living things are connected; we call those connections "ecology". As the IOT becomes increasingly synonymous with the IOLT, the question of ethics that is at the centre of aesthetics and the arts will move to the forefront of our experience of and regard for the world in and around us.
Planning and Learning in Partially Observable Systems via Filter Stability

Partially Observable Markov Decision Processes (POMDPs) are an important model in reinforcement learning that take into account the agent’s uncertainty about its current state. In the literature on POMDPs, it is customary to assume access to a planning oracle that computes an optimal policy when the parameters are known, even though this problem is known to be computationally hard. The major obstruction is the Curse of History, which arises because optimal policies for POMDPs may depend on the entire observation history thus far. In this work, we revisit the planning problem and ask: Are there natural and well-motivated assumptions that avoid the Curse of History in POMDP planning (and beyond)?
Binary Error-Correcting Codes with Minimal Noiseless Feedback

In the setting of error-correcting codes with feedback, Alice wishes to communicate a k-bit message x to Bob by sending a sequence of bits over a channel while noiselessly receiving feedback from Bob. It has been long known (Berlekamp, 1964) that in this model, Bob can still correctly determine x even if ≈ 1/3 of Alice’s bits are flipped adversarially. This improves upon the classical setting without feedback, where recovery is not possible for error fractions exceeding 1/4.
Application of MEMS Data to Fast Inversion of Rupture Process: Tests with Recordings from the IRREEW Network

Abstract The China Earthquake Administration established a network for intensity rapid report and earthquake early warning (IRREEW) in 2016–2020, which consists of approximately 5000 conventional strong-motion and approximately 10,000 low-cost micro-electro-mechanical system (MEMS) seismometers. These seismographs, particularly MEMS stations, can provide a large amount of near-field waveform data suitable for rapid source inversion. Compared with conventional strong-motion data, MEMS recordings have rarely been used in the previous source inversions, because the MEMS technology is newly applied in earthquake monitoring, and the seismograph has a relatively lower signal-to-noise ratio and more severe baseline shifts. However, from waveform comparisons at collocated MEMS and strong-motion stations, we find that they are highly consistent with each other, particularly at frequencies above 0.04 Hz. To explore the application prospect of MEMS data to source inversion, we inverted both MEMS and strong-motion data for three strong earthquakes recorded by the IRREEW network during 2021–2022 to determine their rupture processes. In applications to the 2021 Mw 6.1 Yangbi earthquake, the 2022 Mw 6.6 Menyuan earthquake, and the 2022 Mw 6.6 Luding earthquake, the MEMS data equally well constrain the rupture model. The resulting source information, including the moment magnitude, rupture direction, and rupture dimension, are consistent with those obtained from the strong-motion inversions. Because the low-cost MEMS instruments can be deployed densely around seismically active regions, they can provide urgent waveform data for rapid determination of rupture process, which is crucial for simulation of strong ground motions, and assessments of earthquake and related disasters.
Analytic plenoptic camera diffraction model and radial distortion analysis due to vignetting

Using a mathematical approach, this paper presents a generalization of semi-analytical expressions for the point spread function (PSF) of plenoptic cameras. The model is applicable in the standard regime of the scalar diffraction theory while the extension to arbitrary main lens transmission functions generalizes a priori formalism. The accuracy and applicability of the model is well verified against the exact Rayleigh-Sommerfeld diffraction integral and a rigorous proof of convergence for the PSF series expression is made. Since vignetting can never be fully eliminated, it is critical to inspect the image degradation it poses through distortions. For what we believe is the first time, diffractive distortions in the diffraction-limited plenoptic camera are closely examined and demonstrated to exceed those that would otherwise be estimated by a geometrical optics formalism, further justifying the necessity of an approach based on wave optics. Microlenses subject to the edge diffraction effects of the main lens vignetting are shown to translate into radial distortions of increasing severity and instability with defocus. The distortions due to vignetting are found to be typically bound by the radius of the geometrical defocus in the image plane, while objects confined to the depth of field give rise to merely subpixel distortions.
Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising

Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent approaches implicitly learn to exploit auxiliary features for denoising, which could lead to insufficient utilization of each type of auxiliary features. To overcome such an issue, we propose a denoising framework that relies on an explicit pixel-wise guidance for utilizing auxiliary features. First, we train two denoisers, each trained by a different auxiliary feature (i.e., G-buffers or P-buffers). Then we design our ensembling network to obtain per-pixel ensembling weight maps, which represent pixel-wise guidance for which auxiliary feature should be dominant at reconstructing each individual pixel and use them to ensemble the two denoised results of our denosiers. We also propagate our pixel-wise guidance to the denoisers by jointly training the denoisers and the ensembling network, further guiding the denoisers to focus on regions where G-buffers or P-buffers are relatively important for denoising. Our result and show considerable improvement in denoising performance compared to the baseline denoising model using both G-buffers and P-buffers. The source code is available at https://github.com/qbhan/GuidanceMCDenoising.
Eye-Based Point Rendering for Dynamic Multiview Effects

Eye-based point rendering (EPR) can make multiview effects much more practical by adding eye (camera) buffer resolution efficiencies to improved view-independent rendering (iVIR). We demonstrate this very successfully by applying EPR to dynamic cube-mapped reflections, sometimes achieving nearly 7× speedups over iVIR and traditional multiview rendering (MVR), with nearly equivalent quality. Our application to omnidirectional soft shadows is less successful, demonstrating that EPR is most effective with larger shader loads and tight eye buffer to off-screen (render target) buffer mappings. This is due to EPR's eye buffer resolution constraints limiting points and shading calculations to the sampling rate of the eye's viewport. In a 2.48 million triangle scene with 50 reflective objects (using 300 off-screen views), EPR renders environment maps with a 49.40ms average frame time on an NVIDIA 1080 Ti GPU. In doing so, EPR generates up to 5x fewer points than iVIR, and regularly performs 50× fewer shading calculations than MVR.
Photonic interface between subcarrier wave and dual-rail encodings

Quantum key distribution with multimode subcarrier wave encoding is propitious for being robust against environmental disturbance. For application in long-distance quantum communications, this encoding has to be compatible with entanglement-assisted quantum repeaters that are commonly designed to work with dual-rail encodings. We propose and demonstrate an interface between subcarrier wave and dual-rail encodings with a fidelity of $0.95\ifmmode\pm\else\textpm\fi{}0.03$ using a linear optical circuit. The developed scheme opens the way for future heterogeneous quantum networks that combine subcarrier wave quantum key distribution with trusted and nontrusted nodes.
Self-organizing maps on “what-where” codes towards fully unsupervised classification

Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on proposing unsupervised techniques that can produce adequate hidden representations which can then be fed to a simpler supervised classifier. Despite great success with this approach, an ultimate dependence on a supervised model remains, which forces the number of classes to be known beforehand, and makes the system depend on labels to extract concepts. To overcome this limitation, recent work has been proposed that shows how a self-organizing map (SOM) can be used as a completely unsupervised classifier. However, to achieve success it required deep learning techniques to generate high quality embeddings. The purpose of this work is to show that we can use our previously proposed What-Where encoder in tandem with the SOM to get an end-to-end unsupervised system that is Hebbian. Such system, requires no labels to train nor does it require knowledge of which classes exist beforehand. It can be trained online and adapt to new classes that may emerge. As in the original work, we use the MNIST data set to run an experimental analysis and verify that the system achieves similar accuracies to the best ones reported thus far. Furthermore, we extend the analysis to the more difficult Fashion-MNIST problem and conclude that the system still performs.
Evaluation of Pollution of Soils with Potentially Toxic Elements in the Zone of Influence of Potassium Salt Mining

The results of the assessment of soil contamination with potentially toxic elements (Sr, Pb, As, Zn, Ni, Co, Mn, Cr, V, Ti, Fe) in the zone of influence of potassium salt mining at the Verkhnekamskoye deposit (Perm region) are presented. A comparison is made with the clark, the regional background of the Perm region and the background of the natural region of the Southern Taiga. Data on the accumulation and dispersion of elements under the influence of the extraction of potassium salts in the humus and podzolic soil horizons were obtained, and geochemical series were compiled.
Design of a Reminder and Recall System in a Contact Tracing Application to Support Coronavirus Booster Vaccination

The rate of coronavirus disease 2019 (COVID-19) booster vaccination in Indonesia remains relatively low, representing 15.33% of the overall vaccination target as of April 2022. The implementation of a reminder and recall system has been shown to be effective in increasing vaccination rates. In prior research, reminders and recalls were sent through traditional media, such as mail, and had not yet been integrated into modern media, such as smartphone applications and (in particular) contact tracing applications. Therefore, the present study was conducted to design a reminder and recall system for the PeduliLindungi contact tracing application.We used the design science research (DSR) methodology with three iterations. The first iteration produced a low-fidelity prototype (or wireframe), and the next yielded a high-fidelity (clickable) prototype.The final prototype included three main features: a reminder and recall mechanism, online registration for COVID-19 booster vaccination, and educational articles. The evaluation consisted of interviews in the first iteration, interviews and the System Usability Scale (SUS) questionnaire in the second, and the Post-Study System Usability Questionnaire (PSSUQ) in the third. The SUS value obtained in the second iteration was 71.6, indicating good (acceptable) results, while in the third iteration, the system usefulness, information quality, interface quality, and overall PSSUQ values were 2.456, 2.473, 2.230, and 2.397, respectively, indicating good quality of the resulting design.This research contributes to two areas: implementation of a reminder and recall system in the PeduliLindungi contact tracing application and enhancement of contact tracing applications using DSR methodology.
Prediction method of building inclination around foundation pit based on Grey Model

In order to improve the prediction accuracy and effect of building inclination around foundation pit, a prediction method of building inclination around foundation pit based on grey model is designed in this paper. When calculating the parameters of the prediction model, the number of the bottom layer of the foundation pit excavation, the stress change value and the excavation load value are taken as the basis. At the same time, the grey derivative defined by the smooth discrete function is combined with the grey differential equation to build the grey model. The determined parameters are input into the grey prediction model to output the slope value of the buildings affected by the foundation pit. Complete the design of building inclination prediction method around the foundation pit. The experimental results show that the proposed method can improve the prediction of building inclination around the foundation pit, and clarify the direct relationship between foundation pit and building inclination, which verifies the effectiveness of the proposed method.
Touchstones of Equivalence and the Houdini Transformation

Inspired by Peter Molenaar's Houdini transformation, we consider the idea of touchstones between different models. Touchstones represent instances where models that appear different on the surface can have equivalent characteristics. Touchstones can appear as identical tests of model parameters. They can exist in the mean structure, in the covariance structure, or in both. In the latter case, the models will generate identical mean and covariance structures and will fit the data equally well. After showing some examples of touchstones and how they result from constraints on a general model, we show how that idea can suggest Molenaar's Houdini transformation. This transformation allows one to take a latent variable model and derive an equivalent model comprised solely of manifest (observed) variables. As equivalent models, the parameters of one can be transformed into the parameters of the other.
Swapping data: A pragmatic approach for enabling academic-industrial partnerships

Academic institutions have access to comprehensive sets of real-world data. However, their potential for secondary use-for example, in medical outcomes research or health care quality management-is often limited due to data privacy concerns. External partners could help achieve this potential, yet documented frameworks for such cooperation are lacking. Therefore, this work presents a pragmatic approach for enabling academic-industrial data partnerships in a health care environment.We employ a value-swapping strategy to facilitate data sharing. Using tumor documentation and molecular pathology data, we define a data-altering process as well as rules for an organizational pipeline that includes the technical anonymization process.The resulting dataset was fully anonymized while still retaining the critical properties of the original data to allow for external development and the training of analytical algorithms.Value swapping is a pragmatic, yet powerful method to balance data privacy and requirements for algorithm development; therefore, it is well suited to enable academic-industrial data partnerships.
The model practice of reconstructing education management based on Web 3.0 technology blended learning under the people‐oriented concept

Abstract This paper deeply analyzes the mode of blended learning based on network 3.0 technology under the people‐oriented concept, and introduces the characteristics and essence of blended learning based on network 3.0 technology. On this basis, this paper discusses the dilemma of the traditional educational management mode, puts forward the educational practice design based on Web 3.0 technology blended learning under the people‐oriented concept, and finally summarizes the practice.