Miracle-dz/UniFAR-FTUs huggingface dataset:
Nambu-Goldstone Modes for Superconducting Lattice Fermions

: We present a lattice model for superconducting fermions whose nearest-neighbour two-body interactions are a Bardeen-Cooper-Schrieﬀer-type pairing on the hypercubic lattice Z d with the dimension d ≥ 3. Although these eﬀective interactions between two electrons are believed to be caused by electron-phonon interactions, we assume that the interactions between two electrons are of short range without phonons. For the model, we prove the existence of the long-range order of the superconductivity at low temperatures, and also prove the existence of a gapless excitation above the inﬁnite-volume ground state. Namely, there appears a Nambu-Goldstone mode which is associated with the U(1) symmetry breaking. The corresponding Nambu-Goldstone boson is exactly a Cooper pair, which consists of spin up and down electrons.
Bicycling As A Mode Of Transport In Dhaka City Status And Prospects

This study aims to find out the current status and prospects of using a bicycle as a mode for commuting within Dhaka city. Bicycling is a very sustainable mode of transport but unfortunately is used very less by the commuters of Dhaka. There has been a lot of factors affecting the choice of bicycle to commute. This study was aimed to find out what factors could motivate the commuters of Dhaka to use a bicycle as a mode of transport. For determining the motivators, a survey was administered among the commuters of Dhaka city in which the respondents were asked to answer how certain factors would affect their choice to use a bicycle to commute. A Likert scale was used in the survey and the responses were analysed, from which the top motivators were found. The Motivators were then grouped together using exploratory factor analysis to support possible policymaking. Four factors were extracted using the method. The factors were named Additional Perks, General benefits, Personal Benefits, and Infrastructural benefits
Solving Routing Problems via Important Cuts

We introduce a novel approach of using important cuts which allowed us to design significantly faster fixed-parameter tractable (FPT) algorithms for the following routing problems: the Mixed Chinese Postman Problem parameterized by the number of directed edges (Gutin et al., JCSS 2017), the Minimum Shared Edges problem (MSE) parameterized by the number p of paths between two specified vertices (Fluschnik et al., JCSS 2019), and the Weighted Min Cut Prevention problem (Gruttemeier et al., WG 2021). The Minimum Vulnerability problem (MV) is a generalization of MSE (Assadi et al., Algorithmica 2014). The only known FPT algorithm for MV parameterized by p (the same parameter as for MSE) was for chordal graphs (Aoki et al., JCO 2018). We design an FPT algorithm for MV on all undirected graphs.
Race Driver Evaluation at a Driving Simulator using a physical Model and a Machine Learning Approach

Professional race drivers are still superior to automated systems at controlling a vehicle at its dynamic limit. Gaining insight into race drivers' vehicle handling process might lead to further development in the areas of automated driving systems. We present a method to study and evaluate race drivers on a driver-in-the-loop simulator by analysing tire grip potential exploitation. Given initial data from a simulator run, two optimiser based on physical models maximise the horizontal vehicle acceleration or the tire forces, respectively. An overall performance score, a vehicle-trajectory score and a handling score are introduced to evaluate drivers. Our method is thereby completely track independent and can be used from one single corner up to a large data set. We apply the proposed method to a motorsport data set containing over 1200 laps from seven professional race drivers and two amateur drivers whose lap times are 10-20% slower. The difference to the professional drivers comes mainly from their inferior handling skills and not their choice of driving line. A downside of the presented method for certain applications is an extensive computation time. Therefore, we propose a Long-short-term memory (LSTM) neural network to estimate the driver evaluation scores. We show that the neural network is accurate and robust with a root-mean-square error between 2-5% and can replace the optimisation based method. The time for processing the data set considered in this work is reduced from 68 hours to 12 seconds, making the neural network suitable for real-time application.
Convolutional Filtering in Simplicial Complexes

This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC). SCs are mathematical tools that not only capture pairwise relationships as graphs but account also for higher-order network structures. These filters are built by following the shift-and-sum principle of the convolution operation and rely on the Hodge-Laplacians to shift the signal within the simplex. But since in SCs we have also inter-simplex coupling, we use the incidence matrices to transfer the signal in adjacent simplices and build a filter bank to jointly filter signals from different levels. We prove some interesting properties for the proposed filter bank, including permutation and orientation equivariance, a computational complexity that is linear in the SC dimension, and a spectral interpretation using the simplicial Fourier transform. We illustrate the proposed approach with numerical experiments.
Beyond synchronization: Body gestures and gaze direction in duo performance

In this chapter, we focus on two main categories of visual interaction: body gestures and gaze direction. Our focus on body gestures is motivated by research showing that gesture patterns often change during joint action tasks to become more predictable (van der Wel et al., 2016). Moreover, coordination sometimes emerges between musicians at the level of body sway (Chang et al., 2017). Our focus on gaze direction was motivated by the fact that gaze can serve simultaneously as a means of obtaining information about the world and as a means of communicating one's own attention and intent.
POLİETİLEN TERAFTALAT (PET) POLİMERİNDE EKSTRUDER İÇERİSİNDE ÖNEMLİ OLAN BOZUNMALAR

Bu makalede, Turkiye’de ve dunyada en fazla uretilen sentetik lif olan polietilen teraftalat (PET) lifindeekstruder icerisinde meydana gelen bozunmalar anlatilmistir. Once, PET polimerinde gorulen bozunmalardansoz edilmis, daha sonra ekstruder icerisinde gerceklesen bozunmalar uzerinde durulmustur. Sonuc bolumunde deekstruder icerisinde meydana gelen bozunmalarin, urun ozellikleri uzerindeki etkilerinden soz edilmistir.
Online Service Caching and Routing at the Edge with Unknown Arrivals

This paper studies a problem of jointly optimizing two important operations in mobile edge computing without knowing future requests, namely service caching, which deter-mines which services to be hosted at the edge, and service routing, which determines which requests to be processed locally at the edge. We aim to address several practical challenges, including limited storage and computation capacities of edge servers and unknown future request arrival patterns. To this end, we formulate the problem as an online optimization problem, in which the objective function includes costs of forwarding requests, processing requests, and reconfiguring edge servers. By leveraging a natural timescale separation between service routing and service caching, namely, the former happens faster than the latter, we propose an online two-stage algorithm and its randomized variant. Both algorithms have low complexity, and our fractional solution achieves sublinear regret. Simulation results show that our algorithms significantly outperform other state-of-the-art online policies.
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates

Abstract Numerical generalized randomized Hamiltonian Monte Carlo is introduced, as a robust, easy to use and computationally fast alternative to conventional Markov chain Monte Carlo methods for continuous target distributions. A wide class of piecewise deterministic Markov processes generalizing Randomized HMC (Bou-Rabee and Sanz-Serna) by allowing for state-dependent event rates is defined. Under very mild restrictions, such processes will have the desired target distribution as an invariant distribution. Second, the numerical implementation of such processes, based on adaptive numerical integration of second order ordinary differential equations (ODEs) is considered. The numerical implementation yields an approximate, yet highly robust algorithm that, unlike conventional Hamiltonian Monte Carlo, enables the exploitation of the complete Hamiltonian trajectories (hence, the title). The proposed algorithm may yield large speedups and improvements in stability relative to relevant benchmarks, while incurring numerical biases that are negligible relative to the overall Monte Carlo errors. Granted access to a high-quality ODE code, the proposed methodology is both easy to implement and use, even for highly challenging and high-dimensional target distributions. Supplementary materials for this article are available online.
An Alternative Issue Tracking Dataset of Public Jira Repositories

Organisations use issue tracking systems (ITSs) to track and document their projects' work in units called issues. This style of documentation encourages evolutionary refinement, as each issue can be independently improved, commented on, linked to other issues, and progressed through the organisational workflow. Commonly studied ITSs so far include GitHub, GitLab, and Bugzilla, while Jira, one of the most popular ITS in practice with a wealth of additional information, has yet to receive similar attention. Unfortunately, diverse public Jira datasets are rare, likely due to the difficulty in finding and accessing these repositories. With this paper, we release a dataset of 16 public Jiras with 1822 projects, spanning 2.7 million issues with a combined total of 32 million changes, 9 million comments, and 1 million issue links. We believe this Jira dataset will lead to many fruitful research projects investigating issue evolution, issue linking, cross-project analysis, as well as cross-tool analysis when combined with existing well-studied ITS datasets.
Topology-Preserving Dimensionality Reduction via Interleaving Optimization

Dimensionality reduction techniques are powerful tools for data preprocessing and visualization which typically come with few guarantees concerning the topological correctness of an embedding. The interleaving distance between the persistent homology of Vietoris-Rips filtrations can be used to identify a scale at which topological features such as clusters or holes in an embedding and original data set are in correspondence. We show how optimization seeking to minimize the interleaving distance can be incorporated into dimensionality reduction algorithms, and explicitly demonstrate its use in finding an optimal linear projection. We demonstrate the utility of this framework to data visualization.
A Probabilistic Graph Coupling View of Dimension Reduction

Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on minimizing a cost between input and latent pairwise similarities. Though widely used, these approaches lack clear probabilistic foundations to enable a full understanding of their properties and limitations. To that extent, we introduce a unifying statistical framework based on the coupling of hidden graphs using cross entropy. These graphs induce a Markov random field dependency structure among the observations in both input and latent spaces. We show that existing pairwise similarity DR methods can be retrieved from our framework with particular choices of priors for the graphs. Moreover this reveals that these methods suffer from a statistical deficiency that explains poor performances in conserving coarse-grain dependencies. Our model is leveraged and extended to address this issue while new links are drawn with Laplacian eigenmaps and PCA.
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics

This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE). Both the KL-divergence and the IPMs are widely used in various fields in applications such as generative modeling. However, a unified understanding of these concepts has still been unexplored. In this paper, we show that the KL-divergence and the IPMs can be represented as maximal likelihoods differing only by sampling schemes, and use this result to derive a unified form of the IPMs and a relaxed estimation method. To develop the estimation problem, we construct an unconstrained maximum likelihood estimator to perform DRE with a stratified sampling scheme. We further propose a novel class of probability divergences, called the Density Ratio Metrics (DRMs), that interpolates the KL-divergence and the IPMs. In addition to these findings, we also introduce some applications of the DRMs, such as DRE and generative adversarial networks. In experiments, we validate the effectiveness of our proposed methods.
AIRFOIL PERFORMANCE ENHANCEMENT USING PULSED JET SEPARATION CONTROL

The effects of active separation control on the aerodynamic performance of an advanced fighter aircraft configuration have been determined. Lowspeed wind tunnel tests were performed using a 7% scale lambda-wing model with jet actuators on the wing leading edge flaps. The jets were operated in a pulsed mode and their frequency and amplitude were varied during the experiments. The effects of the jet actuators on the model aerodynamic performance were measured using a six component force balance for several flap configurations and for angles-of-attack (a) up to 40 deg. The results indicate that the pulsed jets are effective in delaying separation and augmenting lift. Maximum increases in lift were approximately 7% and occurred near C,max. Experiments performed to determine the influence of pulse frequency, ff, on control effectiveness showed that control was optimized for nondimensional frequencies, based on freestream velocity, UM, and wing chord, c, near Strc =/p c/Ura= 1. In addition, it was found that control effectiveness increased monotonically with increases in pulse amplitude. When operating the jet actuators on one wing alone, roll moment coefficients of CRoll- 0.011 could be produced. The results of these experiments indicate that leading edge pulsed jet actuators are an effective means of enhancing aerodynamic performance and maneuverability of advanced fighters.
A Context-Integrated Transformer-Based Neural Network for Auction Design

One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a fixed set of bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring \emph{public} contextual information of bidders and items into the auction learning framework. We propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids and contexts while being able to find asymmetric solutions. We show by extensive experiments that $\mathtt{CITransNet}$ can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training.
EDGE: the puzzling ellipticity of Eridanus II's star cluster and its implications for dark matter at the heart of an ultra-faint dwarf

The Eridanus II (EriII) ‘ultra-faint’ dwarf has a large (15 pc) and low mass (4 . 3 × 10 3 M (cid:12) ) star cluster (SC) oﬀset from its centre by 23 ± 3 pc in projection. Its size and oﬀset are naturally explained if EriII has a central dark matter core, but such a core may be challenging to explain in a Λ CDM cosmology. In this paper, we revisit the survival and evolution of EriII’s SC, focussing for the ﬁrst time on its puzzlingly large ellipticity (0 . 31 + 0 . 05 − 0 . 06 ). We perform a suite of 960 direct 𝑁 -body simulations of SCs, orbiting within a range of spherical background potentials ﬁt to ultra-faint dwarf (UFD) galaxy simulations. We ﬁnd only two scenarios that come close to explaining EriII’s SC. In the ﬁrst, EriII has a low density dark matter core (of size ∼ 70 pc and density (cid:46) 2 × 10 8 M (cid:12) kpc − 3 ). In this model, the high ellipticity of EriII’s SC is set at birth, with the lack of tidal forces in the core allowing its ellipticity to remain frozen in for long times. In the second, EriII’s SC orbits in a partial core, with its high ellipticity owing to its imminent tidal destruction. However, this latter model struggles to reproduce the large size of EriII’s SC, and it predicts substantial tidal tails around EriII’s SC that should have already been seen in the data. This leads us to favour the cored model. We discuss potential caveats to these ﬁndings, and the implications of the cored model for galaxy formation and the nature of dark matter.
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent

Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This paper proposes a novel algorithm that encodes the partial derivatives themselves and furthermore optimizes the codes by performing lossy compression on the derivative codewords by maximizing the information contained in the codewords while minimizing the information between the codewords. The utility of this application of coding theory is a geometrical consequence of the observed fact in optimization research that noise is tolerable, sometimes even helpful, in gradient descent based learning algorithms since it helps avoid overfitting and local minima. This stands in contrast with much current conventional work on distributed coded computation which focuses on recovering all of the data from the workers. A second further contribution is that the low-weight nature of the coding scheme allows for asynchronous gradient updates since the code can be iteratively decoded; i.e., a worker's task can immediately be updated into the larger gradient. The directional derivative is always a linear function of the direction vectors; thus, our framework is robust since it can apply linear coding techniques to general machine learning frameworks such as deep neural networks.
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time. While effective, a major bottleneck of using these models in practice is the computationally costly datastore search, which can be performed as frequently as every time step. In this paper, we present RetoMaton - retrieval automaton - which approximates the datastore search, based on (1) saving pointers between consecutive datastore entries, and (2) clustering of entries into"states". This effectively results in a weighted finite automaton built on top of the datastore, instead of representing the datastore as a flat list. The creation of the automaton is unsupervised, and a RetoMaton can be constructed from any text collection: either the original training corpus or from another domain. Traversing this automaton at inference time, in parallel to the LM inference, reduces its perplexity by up to 1.85, or alternatively saves up to 83% of the nearest neighbor searches over $k$NN-LM (Khandelwal et al., 2020) without hurting perplexity. Our code and trained models are available at https://github.com/neulab/retomaton .
Why the Rich Get Richer? On the Balancedness of Random Partition Models

Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems. While the number of clusters induced by random partition models has been studied extensively, another important model property regarding the balancedness of partition has been largely neglected. We formulate a framework to define and theoretically study the balancedness of exchangeable random partition models, by analyzing how a model assigns probabilities to partitions with different levels of balancedness. We demonstrate that the"rich-get-richer"characteristic of many existing popular random partition models is an inevitable consequence of two common assumptions: product-form exchangeability and projectivity. We propose a principled way to compare the balancedness of random partition models, which gives a better understanding of what model works better and what doesn't for different applications. We also introduce the"rich-get-poorer"random partition models and illustrate their application to entity resolution tasks.
Hybrid high-voltage direct current topology with line commutated converter and modular multilevel converter in series connection suitable for bulk power overhead line transmission

In this study, a hybrid high-voltage direct current (HVDC) topology with line commutated converter (LCC) and modular multilevel converter (MMC) in series-connection is proposed, which is suitable for bulk power overhead line transmission. This topology is of operational flexibility in terms of active and reactive power controls, and is able to withstand ac and dc faults by the cooperative control of LCC and MMC. First, the operation principle and mathematical model are presented. Then, the control strategies for ac faults at rectifier and inverter side are discussed, which can prevent current cut-off under ac fault at rectifier side as well as maintain part of active power if commutation failure of LCC occurs under ac fault at inverter side. In addition, the feasibility on dealing with dc fault is theoretically demonstrated by analysing the characteristic of MMC under blocking state. A detailed control strategy for dc fault is further proposed combined with a test system. Finally, the effectiveness of the control strategy for ac and dc faults is verified and further compared with LCC-based HVDC topology through time-domain simulation.
Gibbs measures of the Ising model with mixed spin-1 and spin-1/2 on a Cayley tree

In the present paper, the Ising model with mixed spin-(1,1/2) is considered on the second order Cayley tree. A construction of splitting Gibbs measures corresponding the model is given which allows to establish the existence of the phase transition (non-uniqueness of Gibbs measures). We point out that, in the phase transition region, the considered model has three translation-invariant Gibbs measures in the ferromagnetic and anti-ferromagnetic regimes, while the classical Ising model does not possesses such Gibbs measures in the anti-ferromagnetic regime. It turns out that the considered model, like the Ising model, exhibits a disordered Gibbs measure. Therefore, non-extremity and extremity of such disordered Gibbs measures is investigated by means of tree-indexed Markov chains.