Miracle-dz/UniFAR-FTUs huggingface dataset:
Neighborhood-aware Scalable Temporal Network Representation Learning

Temporal networks have been widely used to model real-world complex systems such as financial systems and e-commerce systems. In a temporal network, the joint neighborhood of a set of nodes often provides crucial structural information useful for predicting whether they may interact at a certain time. However, recent representation learning methods for temporal networks often fail to extract such information or depend on online construction of structural features, which is time-consuming. To address the issue, this work proposes Neighborhood-Aware Temporal network model (NAT). For each node in the network, NAT abandons the commonly-used one-single-vector-based representation while adopting a novel dictionary-type neighborhood representation. Such a dictionary representation records a downsampled set of the neighboring nodes as keys, and allows fast construction of structural features for a joint neighborhood of multiple nodes. We also design a dedicated data structure termed N-cache to support parallel access and update of those dictionary representations on GPUs. NAT gets evaluated over seven real-world large-scale temporal networks. NAT not only outperforms all cutting-edge baselines by averaged 1.2% and 4.2% in transductive and inductive link prediction accuracy, respectively, but also keeps scalable by achieving a speed-up of 4.1-76.7x against the baselines that adopt joint structural features and achieves a speed-up of 1.6-4.0x against the baselines that cannot adopt those features. The link to the code: https: //github.com/Graph-COM/Neighborhood-Aware-Temporal-Network.
Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?

Machine learning (ML)-based Android malware detection has been one of the most popular research topics in the mobile security community. An increasing number of research studies have demonstrated that machine learning is an effective and promising approach for malware detection, and some works have even claimed that their proposed models could achieve 99% detection accuracy, leaving little room for further improvement. However, numerous prior studies have suggested that unrealistic experimental designs bring substantial biases, resulting in over-optimistic performance in malware detection. Unlike previous research that examined the detection performance of ML classifiers to locate the causes, this study employs Explainable AI (XAI) approaches to explore what ML-based models learned during the training process, inspecting and interpreting why ML-based malware classifiers perform so well under unrealistic experimental settings. We discover that temporal sample inconsistency in the training dataset brings over-optimistic classification performance (up to 99%F1 score and accuracy). Importantly, our results indicate that ML models classify malware based on temporal differences between malware and benign, rather than the actual malicious behaviors. Our evaluation also confirms the fact that unrealistic experimental designs lead to not only unrealistic detection performance but also poor reliability, posing a significant obstacle to real-world applications. These findings suggest that XAI approaches should be used to help practitioners/researchers better understand how do AI/ML models (i.e., malware detection) work-not just focusing on accuracy improvement.
Multi-Step Prediction in Linearized Latent State Spaces for Representation Learning

In this paper, we derive a novel method as a generalization over LCEs such as E2C. The method develops the idea of learning a locally linear state space by adding a multi-step prediction, thus allowing for more explicit control over the curvature. We show that the method outperforms E2C without drastic model changes which come with other works, such as PCC and P3C. We discuss the relation between E2C and the presented method and derive update equations. We provide empirical evidence, which suggests that by considering the multi-step prediction, our method – ms-E2C – allows learning much better latent state spaces in terms of curvature and next state predictability. Finally, we also discuss certain stability challenges we encounter with multi-step predictions and how to mitigate them.
MaxWeight With Discounted UCB: A Provably Stable Scheduling Policy for Nonstationary Multi-Server Systems With Unknown Statistics

Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, and crowdsourcing. This paper considers a multi-server system with multiple servers and multiple types of jobs. The system maintains a separate queue for each type of jobs. For each time slot, each available server picks a job from a queue and then serves the job until it is complete. The arrival rates of the queues and the mean service times are unknown and even nonstationary. We propose the MaxWeight with discounted upper conﬁdence bound (UCB) algorithm, which simultaneously learns the statistics and schedules jobs to servers. We prove that the proposed algorithm can stabilize the queues when the arrival rates are strictly within the service capacity region. Speciﬁcally, we prove that the queue lengths are bounded in the mean under the assumption that the mean service times change relatively slowly over time and the arrival rates are bounded away from the capacity region by a constant whose value depends on the discount factor used in the discounted UCB. Simulation results conﬁrm that the proposed algorithm can stabilize the queues and that it outperforms MaxWeight with empirical mean and MaxWeight with discounted empirical mean. The proposed algorithm is also better than MaxWeight with UCB in the nonstationary setting.
Triangular lattice Hubbard model physics at intermediate temperatures

Moire systems offer an exciting playground to study many-body effects of strongly correlated electrons in regimes that are not easily accessible in conventional material settings. Motivated by a recent experiment on $\text{WSe}_2/\text{WS}_2$ moire bilayers [Y. Tang et al., Nature 579, 353-358 (2020)], which realizes a triangular superlattice with a small hopping (of approximately 10 Kelvin), with tunable density of holes, we explore the Hubbard model on the triangular lattice for intermediate temperatures $t \lesssim T<U$. Employing finite temperature Lanczos calculations, and closely following the fitting protocols used in the experiment, we recover the observed trends in the reported Curie-Weiss temperature $\Theta$ with filling, using the reported interaction strength $U/t=20$. We focus on the large increase of $|\Theta|$ on decreasing the density below half filling and the sign change of $\Theta$ at higher fillings, which signals the onset of ferromagnetism. The increase in $|\Theta|$ is also seen in the $t$-$J$ model (the low energy limit of the Hubbard model) in the intermediate temperature range, which we clarify is opposite to the trend in its high temperature limit. Differences between the low, intermediate and high temperature regimes are discussed. Our numerical calculations also capture the crossover between short-range antiferromagnetic to ferromagnetic order in the intermediate temperature regime, a result broadly consistent with the experimental findings. We find that this behavior is a finite-temperature remnant of the underlying zero temperature phase transition, which we explore with ground state density matrix renormalization group calculations. We provide evidence of ferromagnetism characterized by weak (but robust) correlations that explain the small $\Theta$ seen in the experiment.
Artiﬁcial Intelligence based Position Detection for Hydraulic Cylinders using Scattering Parameters

Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston position based on the scattering parameters measured by a sensor. The core of this approach is a physical model that mathematically describes the relationship between the measured scattering parameters and the targeted piston position. However, this physical model has shortcomings in accuracy and adaptability, especially in extreme conditions. To overcome this problem, we propose Artiﬁcial Intelligence (AI)-based methods to learn the relationship directly data-driven. As a result, all Artiﬁcial Neural Network (ANN) models in this paper consistently outperform the physical one by a large margin. Given the success of AI-based models for our task, we further deliberate the choice of models based on domain knowledge and provide in-depth analyses combining model performance with the physical characteristics. Speciﬁcally, we use Convolutional Neural Network (CNN)s to discover local interactions of input among adjacent frequencies, apply Complex-Valued Neural Network (CVNN) to exploit the complex-valued nature of electromagnetic scattering parameters, and introduce a novel technique named Frequency Encoding to add weighted frequency information to the model input. By combining these three techniques, our best performing model, a complex-valued CNN with Frequency Encoding, manages to signiﬁcantly reduce the test error to hardly 1/12 of the one given by the traditional physical model.
An Upper Bound on the Reliability Function of the DMC with or without Mismatch

We derive a new upper bound on the reliability function for channel coding over discrete memoryless channels. Our bounding technique relies on two main elements: (i) adding an auxiliary genie-receiver that reveals to the original receiver a list of codewords including the transmitted one, which satisfy a certain type property, and (ii) partitioning (most of) the list into subsets of codewords that satisfy a certain pairwise-symmetry property, which facilitates lower bounding of the average error probability by the pairwise error probability within a subset. We compare the obtained bound to the Shannon-Gallager-Berlekamp straight-line bound, the sphere-packing bound, and an amended version of Blahut’s bound. Our bound is shown to be at least as tight for all rates, with cases of stricter tightness in a certain range of low rates, compared to all three aforementioned bounds. Our derivation is performed in a unified manner which is valid for any rate, as well as for a wide class of additive decoding metrics, whenever the corresponding zero-error capacity is zero. We also present a dual form of the bound, and discuss a looser bound of a simpler form, which is analyzed for the case of the binary symmetric channel with maximum likelihood decoding.
Holographic cold dense matter constrained by neutron stars

The equation of state (EoS) for cold dense matter inside neutron stars is investigated by using holographic QCD models in the framework of the Einstein-Maxwell-dilaton (EMD) system and the improved Karch-Katz-Son-Stephanov (KKSS) action for matter part. This method of describing holographic nuclear matter in the EMD + KKSS framework is diﬀerent from that by using the Dirac-Born-Infeld (DBI) action and the Chern-Simons (CS) terms. Combining with the Hebeler-Lattimer-Pethick-Schwenk (HLPS) intermediate equation of state (EoS), the hybrid EoS inside the neutron stars is constructed. The obtained hybrid EoS is located in the range that is deﬁned by the low-density chiral eﬀective theory, the high-density perturbative QCD, and the polytropic interpolations between them, and is constrained by the astrophysics observations. The square of the sound velocity reaches a maximum value larger than 0 . 8 in the region of 2 − 5 times the saturation baryon number density and approaches the conformal limit at the high baryon density range. The mass-radius relation and the tidal deformability of the neutron stars are in agreement with astrophysical measurements. The possible maximum mass for the neutron star is about 2 . 5 M (cid:12) and the radius is about 12km then. It is noticed that the holographic quark matter branch in the mass-radius relation is always unstable and the holographic nuclear matter can produce a stable branch. These results indicate that even in the core of the NS, the matter is still in the conﬁnement phase and the quark matter is not favored.
Optimizing Quantum Circuit Parameters via SDP

In recent years, parameterized quantum circuits have become a major tool to design quantum algorithms for optimization problems. The challenge in fully taking advantage of a given family of parameterized circuits lies in finding a good set of parameters in a non-convex landscape that can grow exponentially to the number of parameters. We introduce a new framework for optimizing parameterized quantum circuits: round SDP solutions to circuit parameters. Within this framework, we propose an algorithm that produces approximate solutions for a quantum optimization problem called Quantum Max Cut. The rounding algorithm runs in polynomial time to the number of parameters regardless of the underlying interaction graph. The resulting 0.562-approximation algorithm for generic instances of Quantum Max Cut improves on the previously known best algorithms, which give approximation ratios of less than 0.54.
Feynman Checkers: External Electromagnetic Field and Asymptotic Properties

We study Feynman chekers - one of the most elementary models of electron motion. It is also known as one-dimensional quantum walk or an Ising model at imaginary temperature. We add the simpliest nontrivial electromagnetic field to the model and find the limits of the resulting model for a small lattice step and a large time, similar to the results by J. Narlikar 1972 and G. Grimmet - S. Jason - P. Scudo from the 2000s. It turns out that the limits in the model with the field are obtained from the known ones without a field by a mass renormalization. We also find an exact solution for the resulting model.
Emission of gravitational waves by superconducting cosmic strings

We study the gravitational radiation emission efficiency Γ of superconducting cosmic strings. We demonstrate, by using a solvable model of transonic strings, that the presence of a current leads to a suppression of the gravitational emission of cusps, kinks and different types of loops. We also show that, when a current is present, the spectrum of emission of loops with cusps is exponentially suppressed as the harmonic mode increases, thus being significantly different from the power law spectrum of currentless loops. Furthermore, we establish a phenomenological relationship between Γ and the value of the current on cosmic strings. We conjecture that this relation should be valid for an arbitrary type of current-carrying string. We use this result to study the potential impact of current on the stochastic gravitational wave background generated by cosmic strings with additional degrees of freedom and show that both the amplitude and shape of the spectrum may be significantly affected.
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions ∗Corresponding authors: Nghia Duong-Trung and Ernesto Martinez Preprint submitted to Biochemical Engineering Journal November 2, 2022 ar X iv :2 20 9. 01 08 3v 2 [ cs . L G ] 1 N ov 2 02 2 for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community. There is no one-fits-all procedure; however, this review should help identify the potential automation combining biotechnology and ML domains.
KMTNet Nearby Galaxy Survey: Overview and Survey Description

Recently, there has been increasing demand for deep imaging surveys to investigate the history of the mass assembly of galaxies in detail by examining the remnants of mergers and accretions, both of which have very low surface brightness (LSB). In addition, the nature of star formation in LSB regions, such as galaxy outer disks, is also an intriguing topic in terms of understanding the physical mechanisms of disk evolution. To address these issues, this study conducted a survey project, called the Korea Microlensing Telescope Network Nearby Galaxy Survey, to construct a deep imaging data set of nearby galaxies in the southern hemisphere. It provides deep and wide-field images with a field of view of ∼12 deg2 for 13 nearby galaxies drawn from the Carnegie–Irvine Galaxy Survey catalog in optical broad bands (BRI) and an Hα narrow band. Through a dedicated data reduction, the surface brightness limit in 10″ × 10″ boxes was found to reach as deep as μ 1σ ∼ 29–31 mag arcsec−2 in the optical broad bands and f 1σ ∼ 1–2 × 10−18 erg s−1 cm−2 arcsec−2 in the Hα narrow band. To conclude the paper, several possible scientific applications for this data set are described.
Argument for the radiation-dominated behavior of matter fields in the preinflationary era

We investigate the leading-order behavior of matter ﬁelds in the preinﬂationary era using the semiclassical approximation. Many inﬂationary models assume without supporting arguments that the Universe was radiation dominated prior to inﬂation, leading to modiﬁcations of cosmological observables, such as the Cosmic Microwave Background power spectrum. In previous work, we demonstrated that conformally coupled scalar ﬁelds do have a radiation-like contribution to the stress-energy tensor at suﬃciently early times. In this work, we extend these arguments to apply to massless spin-1 ﬁelds and massive or massless spin- 12 ﬁelds. We ﬁnd massless spin-1 ﬁelds always have a radiation-like contribution. For spin- 12 ﬁelds, we ﬁnd the contribution at early times is radiation-like assuming this is the dominant contribution to the stress-energy tensor.
Index Tracking via Learning to Predict Market Sensitivities

A significant number of equity funds are preferred by index funds nowadays, and market sensitivities are instrumental in managing them. Index funds might replicate the index identically, which is, however, cost-ineffective and impractical. Moreover, to utilize market sensitivities to replicate the index partially, they must be predicted or estimated accurately. Accordingly, first, we examine deep-learning models to predict market sensitivities. Also, we present pragmatic applications of data processing methods to aid training and generate target data for the prediction. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes’ efficacy is corroborated by the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations, and competitive tracking errors of replicating the index using fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study that addresses market sensitivities focused on deep learning.
Gill's problem in a sandwiched porous slab

Abstract The classical Gill's stability problem for stationary and parallel buoyant flow in a vertical porous slab with impermeable and isothermal boundaries kept at different temperatures is reconsidered from a different perspective. A three-layer slab is studied instead of a homogeneous slab as in Gill's problem. The three layers have a symmetric configuration where the two external layers have a high thermal conductivity, while the core layer has a much lower conductivity. A simplified model is set up where the thermal conductivity ratio between the external layers and the internal core is assumed as infinite. It is shown that a flow instability in the sandwiched porous slab may arise with a sufficiently large Rayleigh number. It is also demonstrated that this instability coincides with that predicted in a previous analysis for a homogeneous porous layer with permeable boundaries, by considering the limiting case where the permeability of the external layers is much larger than that of the core layer.
Impact of rheology on the acousto-elastic eﬀect revealed by guided waves in a highly-stretched plate

We study the propagation of guided elastic waves in a plate made of Ecoﬂex © , a nearly-incompressible elastomer. The in-plane displacement ﬁeld in a soft plate is extracted. We measure the phase velocities of two in-plane modes SH 0 and S 0 (ﬁrst symmetrical Lamb mode) coexisting in the low frequency limit. While SH 0 propagates at the transverse velocity, S 0 propagates at the plate velocity. The plate is then subjected to a nearly-uniaxial stress with an elongation reaching 120%. An induced anisotropy is ﬁrst observed and then characterized by following the phase velocities of both modes in two principal directions. Although these measurements provide an estimate of the initial stress, they are not correctly predicted by the acousto-elastic theory. We thus show that the acousto-elastic theory alone is not suﬃcient to explain the evolution of velocities in a prestressed elastomer. The origin of this discrepancy is actually explained by the rheological properties of the elastomer, namely the frequency-dependent shear modulus. The implementation a fractional rheological model in the acousto-elastic theory enables a proper prediction for these velocities up to 80% elongation.
Data-driven stabilizer design and closed-loop analysis of general nonlinear systems via Taylor's expansion

For data-driven control of nonlinear systems, the basis functions characterizing the dynamics are usually essential. In existing works, the basis functions are often carefully chosen based on pre-knowledge of the dynamics so that the system can be expressed or well-approximated by the basis functions and the experimental data. For a more general setting where explicit information on the basis functions is not available, this paper presents a data-driven approach for stabilizer design and closed-loop analysis via the Lyapunov method. First, based on Taylor's expansion and using input-state data, a stabilizer and a Lyapunov function are designed to render the known equilibrium locally asymptotically stable. Then, data-driven conditions are derived to check whether a given sublevel set of the found Lyapunov function is an invariant subset of the region of attraction. One of the main challenges is how to handle Taylor's remainder in the design of the local stabilizers and the analysis of the closed-loop performance.