Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimally Resilient Codes for List-Decoding From Insertions and Deletions

We give a complete answer to the following basic question: “What is the maximal fraction of deletions or insertions tolerable by <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary list-decodable codes with non-vanishing information rate? ” This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a <inline-formula> <tex-math notation="LaTeX">$\gamma \leq 0.707$ </tex-math></inline-formula> fraction of insertions is tolerable by some binary code family. For any desired <inline-formula> <tex-math notation="LaTeX">$\varepsilon > 0$ </tex-math></inline-formula>, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> fraction of insertions and <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> fraction of deletions as long as <inline-formula> <tex-math notation="LaTeX">$\gamma + 2\delta \leq 1 - \varepsilon $ </tex-math></inline-formula>. On the other hand, for any <inline-formula> <tex-math notation="LaTeX">$\gamma, \delta $ </tex-math></inline-formula> with <inline-formula> <tex-math notation="LaTeX">$\gamma + 2\delta = 1$ </tex-math></inline-formula> list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. We further generalize our result to codes over any finite alphabet of size <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>. Surprisingly, our work reveals that the feasibility region for <inline-formula> <tex-math notation="LaTeX">$q>2$ </tex-math></inline-formula> is <italic>not</italic> the natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a <inline-formula> <tex-math notation="LaTeX">$(q-1)$ </tex-math></inline-formula>-piece-wise linear boundary whose <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> corner-points lie on a quadratic curve. The main technical work in our results is proving the existence of code families of sufficiently large <italic>size</italic> with good list-decoding properties for any combination of <inline-formula> <tex-math notation="LaTeX">$\delta,\gamma $ </tex-math></inline-formula> within the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh and Ma, 2014]. Finally, we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.
Developing a unified pipeline for large-scale structure data analysis with angular power spectra – II. A case study for magnification bias and radio continuum surveys

Following on our purpose of developing a unified pipeline for large-scale structure data analysis with angular (i.e. harmonic-space) power spectra, we now include the weak lensing effect of magnification bias on galaxy clustering in a publicly available, modular parameter estimation code. We thus forecast constraints on the parameters of the concordance cosmological model, dark energy, and modified gravity theories from galaxy clustering tomographic angular power spectra. We find that a correct modelling of magnification is crucial in order not to bias the estimation of cosmological parameters, especially in the case of deep galaxy surveys. Our case study adopts specifications of the Evolutionary Map of the Universe (EMU), which is a full-sky, deep radio-continuum survey, and is expected to probe the Universe up to redshift $z\sim6$. We assume the Limber approximation, and include magnification bias on top of density fluctuations and redshift-space distortions. By restricting our analysis to the regime where the Limber approximation holds true, we significantly minimise the computational time needed, compared to that of the exact calculation. We also show that there is a trend for more biased parameter estimates from neglecting magnification when the redshift bins are very wide. We conclude that this result implies a strong dependence on the lensing contribution, which is an integrated effect and becomes dominant when wide redshift bins are considered. Finally, we note that instead of being considered a contaminant, magnification bias encodes important cosmological information, and its inclusion leads to an alleviation of the degeneracy between the galaxy bias and the amplitude normalisation of the matter fluctuations.
The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment

The capacity and coverage requirements for 5th generation (5G) and beyond wireless connectivity will be significantly different from the predecessor networks. To meet these requirements, the anticipated deployment cost in the United Kingdom (UK) is predicted to be between £30bn and £50bn, whereas the current annual capital expenditure (CapEX) of the mobile network operators (MNOs) is £2.5bn. This prospect has vastly impacted and has become one of the major delaying factors for building the 5G physical infrastructure, whereas other areas of 5G are progressing at their speed. Due to the expensive and complicated nature of the network infrastructure and spectrum, the second-tier operators, widely known as mobile virtual network operators (MVNO), are entirely dependent on the MNOs. In this paper, an extensive study is conducted to explore the possibilities of reducing the 5G deployment cost and developing viable business models. In this regard, the potential of infrastructure, data, and spectrum sharing is thoroughly investigated. It is established that the use of existing public infrastructure (e.g., streetlights, telephone poles, etc.) has a potential to reduce the anticipated cost by about 40% to 60%. This paper also reviews the recent Ofcom initiatives to release location-based licenses of the 5G-compatible radio spectrum. Our study suggests that simplification of infrastructure and spectrum will encourage the exponential growth of scenario-specific cellular networks (e.g., private networks, community networks, micro-operators) and will potentially disrupt the current business models of telecommunication business stakeholders – specifically MNOs and TowerCos. Furthermore, the anticipated dense device connectivity in 5G will increase the resolution of traditional and non-traditional data availability significantly. This will encourage extensive data harvesting as a business opportunity and function within small and medium-sized enterprises (SMEs) as well as large social networks. Consequently, the rise of new infrastructures and spectrum stakeholders is anticipated. This will fuel the development of a 5G data exchange ecosystem where data transactions are deemed to be high-value business commodities. The privacy and security of such data, as well as definitions of the associated revenue models and ownership, are challenging areas – and these have yet to emerge and mature fully. In this direction, this paper proposes the development of a unified data hub with layered structured privacy and security along with blockchain and encrypted off-chain based ownership/royalty tracking. Also, a data economy-oriented business model is proposed. The study found that with the potential commodification of data and data transactions along with the low-cost physical infrastructure and spectrum, the 5G network will introduce significant disruption in the Telco business ecosystem.
Unveiling the weak radio quasar population at $z\ge 4$

We applied image stacking on empty field Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey maps centred on optically identified high-redshift quasars at $z\ge 4$ to uncover the hidden $\mu$Jy radio emission in these active galactic nuclei (AGN). The median stacking procedure for the full sample of 2229 optically identified AGN uncovered an unresolved point source with an integrated flux density of 52 $\mu$Jy, with a signal-to-noise ratio ${\sim} 10$. We co-added the individual image centre pixels to estimate the characteristic monochromatic radio power at 1.4 GHz considering various values for the radio spectral index, revealing a radio population with $P_\mathrm{1.4\, GHz}\sim 10^{24}$ W Hz$^{-1}$. Assuming that the entire radio emission originates from star-forming (SF) activity in the nuclear region of the host galaxy, we obtained an upper limit on the characteristic star formation rate, ${\sim} 4200$ M$_\odot$ yr$^{-1}$. The angular resolution of FIRST images is insufficient to distinguish between the SF and AGN origin of radio emission at these redshifts. However, a comparison with properties of individual sources from the literature indicates that a mixed nature is likely. Future very long baseline interferometry radio observations and ultradeep Square Kilometre Array surveys are expected to be sensitive enough to detect and resolve the central $1\! -\! 10$ kpc region in the host galaxies, and thus discriminate between SF and AGN related emission.
Training Data Distribution Search with Ensemble Active Learning

Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either do not contribute or negatively impact the DNN's optimization. Modifying the training distribution in a way that excludes such samples could provide an effective solution to both improve performance and reduce training time. In this paper, we propose to scale up ensemble Active Learning methods to perform acquisition at a large scale (10k to 500k samples at a time). We do this with ensembles of hundreds of models, obtained at a minimal computational cost by reusing intermediate training checkpoints. This allows us to automatically and efficiently perform a training data distribution search for large labeled datasets. We observe that our approach obtains favorable subsets of training data, which can be used to train more accurate DNNs than training with the entire dataset. We perform an extensive experimental study of this phenomenon on three image classification benchmarks (CIFAR-10, CIFAR-100 and ImageNet), analyzing the impact of initialization schemes, acquisition functions and ensemble configurations. We demonstrate that data subsets identified with a lightweight ResNet-18 ensemble remain effective when used to train deep models like ResNet-101 and DenseNet-121. Our results provide strong empirical evidence that optimizing the training data distribution can provide significant benefits on large scale vision tasks.
Multicritical points of unitary matrix model with logarithmic potential identified with Argyres–Douglas points

In [arXiv:1805.05057 [hep-th]],[arXiv:1812.00811 [hep-th]], the partition function of the Gross-Witten-Wadia unitary matrix model with the logarithmic term has been identified with the $\tau$ function of a certain Painlev\'{e} system, and the double scaling limit of the associated discrete Painlev\'{e} equation to the critical point provides us with the Painlev\'{e} II equation. This limit captures the critical behavior of the $su(2)$, $N_f =2$ $\mathcal{N}=2$ supersymmetric gauge theory around its Argyres-Douglas $4D$ superconformal point. Here, we consider further extension of the model that contains the $k$-th multicritical point and that is to be identified with $\hat{A}_{2k, 2k}$ theory. In the $k=2$ case, we derive a system of two ODEs for the scaling functions to the free energy, the time variable being the scaled total mass and make a consistency check on the spectral curve on this matrix model.
Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.
Compiling Stochastic Constraint Programs to And-Or Decision Diagrams

Factored stochastic constraint programming (FSCP) is a formalism to represent multi-stage decision making problems under uncertainty. FSCP models support factorized probabilistic models and involve constraints over decision and random variables. These models have many applications in real-world problems. However, solving these problems requires evaluating the best course of action for each possible outcome of the random variables and hence is computationally challenging. FSCP problems often involve repeated subproblems which ideally should be solved once. In this paper we show how identifying and exploiting these identical subproblems can simplify solving them and leads to a compact representation of the solution. We compile an And-Or search tree to a compact decision diagram. Preliminary experiments show that our proposed method significantly improves the search efficiency by reducing the size of the problem and outperforms the existing methods.
Phase transitions in atypical systems induced by a condensation transition on graphs.

Random graphs undergo structural phase transitions that are crucial for dynamical processes and cooperative behavior of models defined on graphs. In this work we investigate the impact of a first-order structural transition on the thermodynamics of the Ising model defined on Erdos-Renyi random graphs, as well as on the eigenvalue distribution of the adjacency matrix of the same graphical model. The structural transition in question yields graph samples exhibiting condensation, characterized by a large number of nodes having degrees in a narrow interval. We show that this condensation transition induces distinct thermodynamic first-order transitions between the paramagnetic and the ferromagnetic phases of the Ising model. The condensation transition also leads to an abrupt change in the global eigenvalue statistics of the adjacency matrix, which renders the second moment of the eigenvalue distribution discontinuous. As a side result, we derive the critical line determining the percolation transition in Erdos-Renyi graph samples that feature condensation of degrees.
Neural Generative Rhetorical Structure Parsing

Rhetorical structure trees have been shown to be useful for several document-level tasks including summarization and document classification. Previous approaches to RST parsing have used discriminative models; however, these are less sample efficient than generative models, and RST parsing datasets are typically small. In this paper, we present the first generative model for RST parsing. Our model is a document-level RNN grammar (RNNG) with a bottom-up traversal order. We show that, for our parser’s traversal order, previous beam search algorithms for RNNGs have a left-branching bias which is ill-suited for RST parsing.We develop a novel beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias and results in absolute improvements of 6.8 and 2.9 on unlabelled and labelled F1 over previous algorithms. Overall, our generative model outperforms a discriminative model with the same features by 2.6 F1points and achieves performance comparable to the state-of-the-art, outperforming all published parsers from a recent replication study that do not use additional training data
Tangent space spatial filters for interpretable and efficient Riemannian classification

Objective. Methods based on Riemannian geometry have proven themselves to be good models for decoding in brain-computer interfacing (BCI). However, these methods suffer from the curse of dimensionality and are not possible to deploy in high-density online BCI systems. In addition, the lack of interpretability of Riemannian methods leaves open the possibility that artifacts drive classification performance, which is problematic in the areas where artifactual control is crucial, e.g. neurofeedback and BCIs in patient populations. Approach. We rigorously proved the exact equivalence between any linear function on the tangent space and corresponding derived spatial filters. Upon which, we further proposed a set of dimension reduction solutions for Riemannian methods without intensive optimization steps. The proposed pipelines are validated against classic common spatial patterns and tangent space classification using an open-access BCI analysis framework, which contains over seven datasets and 200 subjects in total. At last, the robustness of our framework is verified via visualizing the corresponding spatial patterns. Main results. Proposed spatial filtering methods possess competitive, sometimes even slightly better, performances comparing to classic tangent space classification while reducing the time cost up to 97% in the testing stage. Importantly, the performances of proposed spatial filtering methods converge with using only four to six filter components regardless of the number of channels which is also cross validated by the visualized spatial patterns. These results reveal the possibility of underlying neuronal sources within each recording session. Significance. Our work promotes the theoretical understanding about Riemannian geometry based BCI classification and allows for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods.
rp‐adaptation for compressible flows

We present a novel rp‐adaptation strategy for high‐fidelity simulations of compressible inviscid flows with shocks. The mesh resolution in regions of flow discontinuities is increased by using a variational optimizer to r‐adapt the mesh and cluster degrees of freedom there. In regions of smooth flow, we locally increase or decrease the local resolution through increasing or decreasing the polynomial order of the elements, respectively. This dual approach allows us to take advantage of the strengths of both methods for best computational performance, thereby reducing the overall cost of the simulation. The adaptation workflow uses a sensor for both discontinuities and smooth regions that is cheap to calculate, but the framework is general and could be used in conjunction with other feature‐based sensors or error estimators. We demonstrate this proof‐of‐concept using two geometries in transonic and supersonic flow regimes. The method has been implemented in the open‐source spectral/hp element framework Nektar++, and adaptivity is performed by its dedicated high‐order mesh generation tool NekMesh. The results show that the proposed rp‐adaptation methodology is a reasonably cost‐effective way of improving simulation accuracy.
On the Design and Analysis of a Biometric Authentication System using Keystroke Dynamics

This paper proposes a portable hardware token for user’s authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%.
Learning Asr-Robust Contextualized Embeddings for Spoken Language Understanding

Employing pre-trained language models (LM) to extract contextualized word representations has achieved state-of-the-art performance on various NLP tasks. However, applying this technique to noisy transcripts generated by automatic speech recognizer (ASR) is concerned. Therefore, this paper focuses on making contextualized representations more ASR-robust. We propose a novel confusion-aware fine-tuning method to mitigate the impact of ASR errors on pre-trained LMs. Specifically, we fine-tune LMs to produce similar representations for acoustically confusable words that are obtained from word confusion networks (WCNs) produced by ASR. Experiments on multiple benchmark datasets show that the proposed method significantly improves the performance of spoken language understanding when performing on ASR transcripts1.
A Taxonomic Review of Adaptive Random Testing for Numeric Programs

Random testing (RT) is a black-box software testing technique that tests programs by generating random test inputs. It is a widely used technique for software quality assurance, but there has been much debate by practitioners concerning its failure-detection effectiveness. However, RT is argued to be possibly less effective by some researchers as it does not utilize any information about the program under test. Efforts to mainly improve the failure-detection capability of RT, have led to the proposition of Adaptive Random Testing (ART). ART takes advantage of the location information of previous non-fault-detecting test cases to enhance effectiveness as compared to RT. The approach has gained popularity and has a large number of theoretical studies and methods that employ different notions. In this review, our goal is to provide an overview of existing ART methods for numeric programs. We classify and assess existing ART methods with a focus on their motivation, strategy, and findings. The study also discusses several worthy avenues related to ART. The review uses 54 ART papers in several journals, workshops, and conference proceedings. The results of the review show that significant research efforts have been made towards the field of ART, however further empirical studies are still required to make the technique applicable in different test scenarios in order to impact on the industry.
Oldie is Goodie: Effective User Retention by In-game Promotion Event Analysis

For sustainable growth and profitability, online game companies are constantly carrying out various events to attract new game users, to maximize return users, and to minimize churn users in online games. Because minimizing churn users is the most cost-effective method, many pieces of research are being conducted on ways to predict and to prevent churns in advance. However, there is still little research on the validity of event effects. In this study, we investigate whether game events influence the user churn rate and confirm the difference in how game users respond to events by character level, item purchasing frequency and game-playing time band.