Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximal diameter of integral circulant graphs

Integral circulant graphs are proposed as models for quantum spin networks that permit a quantum phenomenon called perfect state transfer. Specifically, it is important to know how far information can potentially be transferred between nodes of the quantum networks modelled by integral circulant graphs and this task is related to calculating the maximal diameter of a graph. The integral circulant graph $ICG_n (D)$ has the vertex set $Z_n = \{0, 1, 2, \ldots, n - 1\}$ and vertices $a$ and $b$ are adjacent if $\gcd(a-b,n)\in D$, where $D \subseteq \{d : d \mid n,\ 1\leq d<n\}$. Motivated by the result on the upper bound of the diameter of $ICG_n(D)$ given in [N. Saxena, S. Severini, I. Shparlinski, \textit{Parameters of integral circulant graphs and periodic quantum dynamics}, International Journal of Quantum Information 5 (2007), 417--430], according to which $2|D|+1$ represents one such bound, in this paper we prove that the maximal value of the diameter of the integral circulant graph $ICG_n(D)$ of a given order $n$ with its prime factorization $p_1^{\alpha_1}\cdots p_k^{\alpha_k}$, is equal to $r(n)$ or $r(n)+1$, where $r(n)=k + |\{ i \ | \alpha_i>1,\ 1\leq i\leq k \}|$, depending on whether $n\not\in 4N+2$ or not, respectively. Furthermore, we show that, for a given order $n$, a divisor set $D$ with $|D|\leq k$ can always be found such that this bound is attained. Finally, we calculate the maximal diameter in the class of integral circulant graphs of a given order $n$ and cardinality of the divisor set $t\leq k$ and characterize all extremal graphs. We actually show that the maximal diameter can have the values $2t$, $2t+1$, $r(n)$ and $r(n)+1$ depending on the values of $t$ and $n$. This way we further improve the upper bound of Saxena, Severini and Shparlinski and we also characterize all graphs whose diameters are equal to $2|D|+1$, thus generalizing a result in that paper.
SCOTCH -- Search for Clandestine Optically Thick Compact HIIs

This study uses archival high frequency continuum data to expand the search for Hypercompact HII regions and determine the conditions at which they appear, as this stage high mass star formation is short-lived and rare. We use 23 GHz continuum data taken towards methanol masers, which are an excellent signpost for very young embedded high-mass protostars. We have searched for high-frequency, optically thick radio sources to identify HC HII region candidates. The data cover 128 fields that include 141 methanol masers identified by the Methanol Multibeam (MMB) survey. We have detected 68 high-frequency radio sources and conducted a multi-wavelength analysis to determine their nature. This has identified 49 HII regions, 47 of which are embedded in dense clumps fourteen of which do not have a 5 GHz radio counterpart. We have identified 13 methanol maser sites that are coincident with radio sources that have a steep positive spectral index. The majority of these are not detected in the mid-infrared and have been classified as protostellar or young stellar objects in the literature and we therefore consider to be good HC HII region candidates, however, further work and higher resolution data are needed to confirm these candidates.
Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks

Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location.
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning

Forgetting refers to the loss or deterioration of previously acquired information or knowledge. While the existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new tasks, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we aim to present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, in future work, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.
Adapting an ASR Foundation Model for Spoken Language Assessment

A crucial part of an accurate and reliable spoken language assessment system is the underlying ASR model. Recently, large-scale pre-trained ASR foundation models such as Whisper have been made available. As the output of these models is designed to be human readable, punctuation is added, numbers are presented in Arabic numeric form and abbreviations are included. Additionally, these models have a tendency to skip disfluencies and hesitations in the output. Though useful for readability, these attributes are not helpful for assessing the ability of a candidate and providing feedback. Here a precise transcription of what a candidate said is needed. In this paper, we give a detailed analysis of Whisper outputs and propose two solutions: fine-tuning and soft prompt tuning. Experiments are conducted on both public speech corpora and an English learner dataset. Results show that we can effectively alter the decoding behaviour of Whisper to generate the exact words spoken in the response.
Building PEBI Grids Conforming To 3D Geological Features Using Centroidal Voronoi Tessellations

For numerical reservoir flow simulation, grids that are conformal to the geological features are needed in order to reduce the homogenization error (in particular between horizons) and to re- trieve the major flow features (such as faults). In this paper, Voronoi Tessellations are obtained by an optimization method where the minimized function is modified from the classical Cen- troidal Voronoi function. The geological features are considered as inner surfaces, dividing the reservoir into closed subdomains.These methodologies are applied successfully to 3D synthetic reservoirs with internal features such as horizons, faults, partly cutting faults and pinch-outs.
Polynomial-Delay Enumeration of Large Maximal Common Independent Sets in Two Matroids

Finding a maximum cardinality common independent set in two matroids (also known as \textsc{Matroid Intersection}) is a classical combinatorial optimization problem, which generalizes several well-known problems, such as finding a maximum bipartite matching, a maximum colorful forest, and an arborescence in directed graphs. Enumerating all maximal common independent sets in two (or more) matroids is a classical enumeration problem. In this paper, we address an ``intersection'' of these problems: Given two matroids and a threshold $\tau$, the goal is to enumerate all maximal common independent sets in the matroids with cardinality at least $\tau$. We show that this problem can be solved in polynomial delay and polynomial space. Moreover, our technique can be extended to a more general problem, which is relevant to Matroid Matching. We give a polynomial-delay and polynomial-space algorithm for enumerating all maximal ``matchings'' with cardinality at least $\tau$, assuming that the optimization counterpart is ``tractable'' in a certain sense. This extension allows us to enumerate small minimal connected vertex covers in subcubic graphs. We also discuss a framework to convert enumeration with cardinality constraints into ranked enumeration.
Local Minima Drive Communications in Cooperative Interaction

An important open question in human-robot interaction (HRI) is precisely when an agent should decide to communicate, particularly in a cooperative task. Perceptual Control Theory (PCT) tells us that agents are able to cooperate on a joint task simply by sharing the same 'intention', thereby distributing the effort required to complete the task among the agents. This is even true for agents that do not possess the same abilities, so long as the goal is observable, the combined actions are sufficient to complete the task, and there is no local minimum in the search space. If these conditions hold, then a cooperative task can be accomplished without any communication between the contributing agents. However, for tasks that do contain local minima, the global solution can only be reached if at least one of the agents adapts its intention at the appropriate moments, and this can only be achieved by appropriately timed communication. In other words, it is hypothesised that in cooperative tasks, the function of communication is to coordinate actions in a complex search space that contains local minima. These principles have been verified in a computer-based simulation environment in which two independent one-dimensional agents are obliged to cooperate in order to solve a two-dimensional path-finding task.
Nested stochastic block model for simultaneously clustering networks and nodes

We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with potentially different node sets, the flexibility to model heterogeneous communities, and the means to automatically select the number of classes for the networks and the number of communities within each network. This is accomplished via a Bayesian model, with a novel application of the nested Dirichlet process (NDP) as a prior to jointly model the between-network and within-network clusters. The dependency introduced by the network data creates nontrivial challenges for the NDP, especially in the development of efficient samplers. For posterior inference, we propose several Markov chain Monte Carlo algorithms including a standard Gibbs sampler, a collapsed Gibbs sampler, and two blocked Gibbs samplers that ultimately return two levels of clustering labels from both within and across the networks. Extensive simulation studies are carried out which demonstrate that the model provides very accurate estimates of both levels of the clustering structure. We also apply our model to two social network datasets that cannot be analyzed using any previous method in the literature due to the anonymity of the nodes and the varying number of nodes in each network.
Newtonian Program Analysis of Probabilistic Programs

Due to their quantitative nature, probabilistic programs pose non-trivial challenges for designing compositional and efficient program analyses. Many analyses for probabilistic programs rely on iterative approximation. This article presents an interprocedural dataflow-analysis framework, called NPA-PMA, for designing and implementing (partially) non-iterative program analyses of probabilistic programs with unstructured control-flow, nondeterminism, and general recursion. NPA-PMA is based on Newtonian Program Analysis (NPA), a generalization of Newton's method to solve equation systems over semirings. The key challenge for developing NPA-PMA is to handle multiple kinds of confluences in both the algebraic structures that specify analyses and the equation systems that encode control flow: semirings support a single confluence operation, whereas NPA-PMA involves three confluence operations (conditional, probabilistic, and nondeterministic). Our work introduces $\omega$-continuous pre-Markov algebras ($\omega$PMAs) to factor out common parts of different analyses; adopts regular infinite-tree expressions to encode program-execution paths in control-flow hyper-graphs; and presents a linearization method that makes Newton's method applicable to the setting of regular-infinite-tree equations over $\omega$PMAs. NPA-PMA allows analyses to supply a non-iterative strategy to solve linearized equations. Our experimental evaluation demonstrates that (i) NPA-PMA holds considerable promise for outperforming Kleene iteration, and (ii) provides great generality for designing program analyses.
Teach model to answer questions after comprehending the document

Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text. The MRC task has traditionally been viewed as a process of answering questions based on the given text. This single-stage approach has often led the network to concentrate on generating the correct answer, potentially neglecting the comprehension of the text itself. As a result, many prevalent models have faced challenges in performing well on this task when dealing with longer texts. In this paper, we propose a two-stage knowledge distillation method that teaches the model to better comprehend the document by dividing the MRC task into two separate stages. Our experimental results show that the student model, when equipped with our method, achieves significant improvements, demonstrating the effectiveness of our method.
Potential energy curves of molecular nitrogen up to N 24+

The potential energy curves for molecular ions up to N 24+ are calculated in an ab initio manner using the multi configurational self-consistent field method. Specifically, we implement in an automatic way a previously used double loop optimisation scheme within the multi configurational self-consisted field method. We obtain the potential energy curves up to N 24+ ions with any combination of core, inner valence, and outer valence holes. Finally, we provide the code used to generate these potential energy curves.
Efficient Approximation for Subgraph-Hitting Problems in Sparse Graphs and Geometric Intersection Graphs

We investigate a fundamental vertex-deletion problem called (Induced) Subgraph Hitting: given a graph $G$ and a set $\mathcal{F}$ of forbidden graphs, the goal is to compute a minimum-sized set $S$ of vertices of $G$ such that $G-S$ does not contain any graph in $\mathcal{F}$ as an (induced) subgraph. This is a generic problem that encompasses many well-known problems that were extensively studied on their own, particularly (but not only) from the perspectives of both approximation and parameterization. In this paper, we study the approximability of the problem on a large variety of graph classes. Our first result is a linear-time $(1+\varepsilon)$-approximation reduction from (Induced) Subgraph Hitting on any graph class $\mathcal{G}$ of bounded expansion to the same problem on bounded degree graphs within $\mathcal{G}$. This directly yields linear-size $(1+\varepsilon)$-approximation lossy kernels for the problems on any bounded-expansion graph classes. Our second result is a linear-time approximation scheme for (Induced) Subgraph Hitting on any graph class $\mathcal{G}$ of polynomial expansion, based on the local-search framework of Har-Peled and Quanrud [SICOMP 2017]. This approximation scheme can be applied to a more general family of problems that aim to hit all subgraphs satisfying a certain property $\pi$ that is efficiently testable and has bounded diameter. Both of our results have applications to Subgraph Hitting (not induced) on wide classes of geometric intersection graphs, resulting in linear-size lossy kernels and (near-)linear time approximation schemes for the problem.
Novel Formulation of Parzen Data Analysis

The Parzen window density is a well-known technique, associating Gaussian kernels with data points. It is a very useful tool in data exploration, with particular importance for clustering schemes and image analysis. This method is presented here within a formalism containing scalar fields, such as the density function and its potential, and their corresponding gradients. The potential is derived from the density through the dependence of the latter on the common scale parameter of all Gaussian kernels. The loci of extrema of the density and potential scalar fields are points of interest which obey a variation condition on a novel indicator function. They serve as focal points of clustering methods depending on maximization of the density, or minimization of the potential, accordingly. The mixed inter-dependencies of the different fields in d-dim data-space and 1-d scale-space, are discussed. They lead to a Schr\H{o}dinger equation in d-dim, and to a diffusion equation in (d+1)-dim
Mitigating Metastable Failures in Distributed Systems with Offline Reinforcement Learning

This paper introduces a load shedding mechanism that mitigates metastable failures through offline reinforcement learning (RL). Previous studies have heavily focused on heuristics that are reactive and limited in generalization, while online RL algorithms face challenges in accurately simulating system dynamics and acquiring data with sufficient coverage. In contrast, our algorithm leverages offline RL to learn directly from existing log data. Through extensive empirical experiments, we demonstrate that our algorithm outperforms rule-based methods and supervised learning algorithms in a proactive, adaptive, generalizable, and safe manner. Deployed in a Java compute service with diverse execution times and configurations, our algorithm exhibits faster reaction time and attains the Pareto frontier between throughput and tail latency.
Sliding mode synchronization of a delayed complex dynamical network in the presence of uncertainties and external disturbances

This paper is concerned with the problem of robust sliding mode H ∞ control of a delayed complex dynamical network subject to parametric uncertainties and external disturbances. In this regard, synchronization error dynamics between the nodes of the complex dynamical network and an individual node are derived. Based on Lyapunov–Krasovskii stability theory, sliding mode H ∞ control laws are designed to force the state trajectories of the error dynamics to sliding manifolds and are maintained thereafter. Moreover, an H ∞ control law is designed to compare its results with the proposed method. By simulating a numerical Chua’s circuit, it is shown that the proposed method is effective at attenuating the effect of the external disturbances and parametric uncertainties.
Big Data Analytics with Machine Learning Models

Data that is too big, moving too quickly, or complex to process using conventional techniques is referred to as "Big Data. " Formerly, the 3Vs were employed in Big Data, but today the 5Vs — volume, velocity, variety, veracity, and value — are used. While there is no doubt that these huge data have great potential. The artificial intelligence technique of information discovery for thoughtful decision-making is called machine learning. The most popular technologies for study in various analytics and computations today are Big Data analytics and machine learning. Although data preparation for Big Data is a topic in and of itself, large data may benefit from machine learning. Bigdata may facilitate the creation and fine-tuning of incremental/online/stream-oriented ML algorithms; in particular, it may be worthwhile to consider models that have already been created for drifting data. In most cases, learning is created by performing in-depth computations on pre-existing datasets to produce a learning model. Since data sizes are growing daily and a typical system cannot manage very large dataset calculations, the discovered model needs to be adjusted accordingly. Machine learning makes utilization of Big Data approaches. We are all aware that large data sets are ideal for machine learning, and here is where Big Data comes into play. Big Data is used to glean hidden knowledge or important insights from massive data sets. In a nutshell, we may assert that machine learning would be useless without large data. Big Data analytics and machine learning are crucial for classification and prediction in many businesses, including those in the health, education, agriculture, manufacturing, banking, and other industries. Data must be provided to machine learning models as input, and sometimes the more comprehensive the data, the better the model's output. To create the required result in such a scenario, huge data is provided as an input to a machine learning model. One of the input sources for the machine learning model could be Big Data. Machine learning is a technique used in artificial intelligence to find information that may be used to make wise decisions. This article discusses machine learning methodologies, key Big Data technologies, and a few machine learning applications in Big Data. It also explains machine learning algorithms in Big Data analytics, and machine learning challenges us to make decisions where there is no known "right path" for the specific problem based on previous lessons. It also enumerates some of the most widely used tools for analyzing and modeling Big Data.
On Globalization and Technology an Evaluation

The so-called globalization process can be described as the unification of different nations at a common denominator on a world scale. Globalization has supporting dimensions such as political, social, economic and technology. In the focus of our subject, more technological aspects of this process are discussed and examined in the context of technological globalization and globalized technology. As a result of these investigations, the advantages and disadvantages of a science of application such as technology in the process of globalization have been identified. The information obtained has shown that globalization and technology support each other in almost every field and have a share in its development. Because globalization can be likened to a vehicle without wheels without technology.