Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Top-K Non-Overlapping Sequential Patterns

Sequential pattern mining (SPM) has excellent prospects and application spaces and has been widely used in different fields. The non-overlapping SPM, as one of the data mining techniques, has been used to discover patterns that have requirements for gap constraints in some specific mining tasks, such as bio-data mining. And for the non-overlapping sequential patterns with gap constraints, the Nettree structure has been proposed to efficiently compute the support of the patterns. For pattern mining, users usually need to consider the threshold of minimum support (\textit{minsup}). This is especially difficult in the case of large databases. Although some existing algorithms can mine the top-$k$ patterns, they are approximate algorithms with fixed lengths. In this paper, a precise algorithm for mining \underline{T}op-$k$ \underline{N}on-\underline{O}verlapping \underline{S}equential \underline{P}atterns (TNOSP) is proposed. The top-$k$ solution of SPM is an effective way to discover the most frequent non-overlapping sequential patterns without having to set the \textit{minsup}. As a novel pattern mining algorithm, TNOSP can precisely search the top-$k$ patterns of non-overlapping sequences with different gap constraints. We further propose a pruning strategy named \underline{Q}ueue \underline{M}eta \underline{S}et \underline{P}runing (QMSP) to improve TNOSP's performance. TNOSP can reduce redundancy in non-overlapping sequential mining and has better performance in mining precise non-overlapping sequential patterns. The experimental results and comparisons on several datasets have shown that TNOSP outperformed the existing algorithms in terms of precision, efficiency, and scalability.
Addressing distributional shifts in operations management: The case of order fulfillment in customized production

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data—so‐called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data‐driven approach based on adversarial learning, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real‐world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision making under distributional shifts.
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration

Vertical federated learning (VFL) is a cloud-edge collaboration paradigm that enables edge nodes, comprising resource-constrained Internet of Things (IoT) devices, to cooperatively train artificial intelligence (AI) models while retaining their data locally. This paradigm facilitates improved privacy and security for edges and IoT devices, making VFL an essential component of Artificial Intelligence of Things (AIoT) systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing the universal trigger and model parameters of VFL sub-problems. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information not accessible within the split VFL architecture. In contrast, our approach does not necessitate any additional data to execute the attack. On the LendingClub and Zhongyuan datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100\% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance to revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure AIoT.
Intuitionistic S4 is decidable

In this paper we demonstrate decidability for the intuitionistic modal logic S4 first formulated by Fischer Servi. This solves a problem that has been open for almost thirty years since it had been posed in Simpson’s PhD thesis in 1994. We obtain this result by performing proof search in a labelled deductive system that, instead of using only one binary relation on the labels, employs two: one corresponding to the accessibility relation of modal logic and the other corresponding to the order relation of intuitionistic Kripke frames. Our search algorithm outputs either a proof or a finite counter-model, thus, additionally establishing the finite model property for intuitionistic S4, which has been another long-standing open problem in the area.
Open continuum robotics–one actuation module to create them all

Experiments on physical continuum robot are the gold standard for evaluations. Currently, as no commercial continuum robot platform is available, a large variety of early-stage prototypes exists. These prototypes are developed by individual research groups and are often used for a single publication. Thus, a significant amount of time is devoted to creating proprietary hardware and software hindering the development of a common platform, and shifting away scarce time and efforts from the main research challenges. We address this problem by proposing an open-source actuation module, which can be used to build different types of continuum robots. It consists of a high-torque brushless electric motor, a high resolution optical encoder, and a low-gear-ratio transmission. For this article, we create three different types of continuum robots. In addition, we illustrate, for the first time, that continuum robots built with our actuation module can proprioceptively detect external forces. Consequently, our approach opens untapped and under-investigated research directions related to the dynamics and advanced control of continuum robots, where sensing the generalized flow and effort is mandatory. Besides that, we democratize continuum robots research by providing open-source software and hardware with our initiative called the Open Continuum Robotics Project, to increase the accessibility and reproducibility of advanced methods.
Bounds on Cheeger–Gromov invariants and simplicial complexity of triangulated manifolds

We show the existence of linear bounds on Wall 𝜌-invariants of PL manifolds, employing a new combinatorial concept of 𝐺-colored polyhedra. As an application, we show how the number of h-cobordism classes of manifolds simple homotopy equivalent to a lens space with 𝑉 simplices and the fundamental group of Z n \mathbb{Z}_{n} grows in 𝑉. Furthermore, we count the number of homotopy lens spaces with bounded geometry in 𝑉. Similarly, we give new linear bounds on Cheeger–Gromov 𝜌-invariants of PL manifolds endowed with a faithful representation also. A key idea is to construct a cobordism with a linear complexity whose boundary is π 1 \pi_{1} -injectively embedded, using relative hyperbolization. As an application, we study the complexity theory of high-dimensional lens spaces. Lastly, we show the density of 𝜌-invariants over manifolds homotopy equivalent to a given manifold for certain fundamental groups. This implies that the structure set is not finitely generated.
Task-Adaptive Pseudo Labeling for Transductive Meta-Learning

Meta-learning performs adaptation through a limited amount of support set, which may cause a sample bias problem. To solve this problem, transductive meta-learning is getting more and more attention, going beyond the conventional inductive learning perspective. This paper proposes so-called task-adaptive pseudo labeling for transductive meta-learning. Specifically, pseudo labels for unlabeled query sets are generated from labeled support sets through label propagation. Pseudo labels enable to adopt the supervised setting as it is and also use the unlabeled query set in the adaptation process. As a result, the proposed method is able to deal with more examples in the adaptation process than inductive ones, which can result in better classification performance of the model. Note that the proposed method is the first approach of applying task adaptation to pseudo labeling. Experiments show that the proposed method outperforms the state-of-the-art (SOTA) technique in 5-way 1-shot few-shot classification.
Money Over Morals: A Business Analysis of Conti Ransomware

Ransomware operations have evolved from relatively unsophisticated threat actors into highly coordinated cybercrime syndicates that regularly extort millions of dollars in a single attack. Despite dominating headlines and crippling businesses across the globe, there is relatively little in-depth research into the modern structure and economics of ransomware operations.In this paper, we leverage leaked chat messages to provide an in-depth empirical analysis of Conti, one of the largest ransomware groups. By analyzing these chat messages, we construct a picture of Conti’s operations as a highly-profitable business, from profit structures to employee recruitment and roles. We present novel methodologies to trace ransom payments, identifying over $80 million in likely ransom payments to Conti and its predecessor – over five times as much as in previous public datasets. As part of our work, we will publish a dataset of 666 labeled Bitcoin addresses related to Conti and an additional 75 Bitcoin addresses of likely ransom payments. Future work can leverage this case study to more effectively trace – and ultimately counteract – ransomware activity.
Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype

We study the problem of routing plug-in electric and conventional fuel vehicles on a city scale using incentives. In our model, commuters selfishly aim to minimize a local cost that combines travel time and the financial expenses of using city facilities, i.e., parking and service stations. The traffic authority can influence the commuters' routing choice via personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of optimally designing these monetary incentives to induce traffic decongestion as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient scalable solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for bilevel games. Finally, we validate our approach via numerical simulations over the Anaheim's traffic network, showcasing its advantages in terms of traffic decongestion and scalability.
Resonance frequency tracking schemes for micro- and nanomechanical resonators

Nanomechanical resonators can serve as high performance detectors and have potential to be widely used in the industry for a variety of applications. Most nanomechanical sensing applications rely on detecting changes of resonance frequency. In commonly used frequency tracking schemes, the resonator is driven at or close to its resonance frequency. Closed-loop systems can continually check whether the resonator is at resonance and accordingly adjust the frequency of the driving signal. In this work, we study three resonance frequency tracking schemes, a feedback-free (FF), a self-sustaining oscillator (SSO), and a phase-locked loop oscillator (PLLO) scheme. We improve and extend the theoretical models for the FF and the SSO tracking schemes, and test the models experimentally with a nanoelectromechanical system (NEMS) resonator. We employ a SSO architecture with a pulsed positive feedback topology and compare it to the commonly used PLLO and FF schemes. We show that all tracking schemes are theoretically equivalent and that they all are subject to the same speed versus accuracy trade-off characteristics. In order to verify the theoretical models, we present experimental steady-state measurements for all tracking schemes. Frequency stability is characterized by computing the Allan deviation. We obtain almost perfect correspondence between the theoretical models and the experimental measurements. These results show that the choice of the tracking scheme is dictated by cost, robustness and usability in practice as opposed to fundamental theoretical differences in performance.
Developing a cost-effective emulator for groundwater flow modeling using deep neural operators

Current groundwater models face a significant challenge in their implementation due to heavy computational burdens. To overcome this, our work proposes a cost-effective emulator that efficiently and accurately forecasts the impact of abstraction in an aquifer. Our approach uses a deep neural operator (DeepONet) to learn operators that map between infinite-dimensional function spaces via deep neural networks. The goal is to infer the distribution of hydraulic head in a confined aquifer in the presence of a pumping well. We successfully tested the DeepONet on four problems, including two forward problems, an inverse analysis, and a nonlinear system. Additionally, we propose a novel extension of the DeepONet-based architecture to generate accurate predictions for varied hydraulic conductivity fields and pumping well locations that are unseen during training. Our emulator's predictions match the target data with excellent performance, demonstrating that the proposed model can act as an efficient and fast tool to support a range of tasks that require repetitive forward numerical simulations or inverse simulations of groundwater flow problems. Overall, our work provides a promising avenue for developing cost-effective and accurate groundwater models.
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning

This paper presents FedPIDAvg, the winning submission to the Federated Tumor Segmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our winning contribution to FETS21, we contribute an improved aggregation strategy for federated and collaborative learning. FedCostWAvg is a weighted averaging method that not only considers the number of training samples of each cluster but also the size of the drop of the respective cost function in the last federated round. This can be interpreted as the derivative part of a PID controller (proportional-integral-derivative controller). In FedPIDAvg, we further add the missing integral term. Another key challenge was the vastly varying size of data samples per center. We addressed this by modeling the data center sizes as following a Poisson distribution and choosing the training iterations per center accordingly. Our method outperformed all other submissions.
Co-GRU Enhanced End-to-End Design for Long-haul Coherent Transmission Systems

In recent years, the end-to-end (E2E) scheme based on deep learning (DL) has been proposed as a potential scheme to jointly optimize the encoder and the decoder parameters of the optical communication system. Compared with conventional deep neural network (DNN) adopted in E2E design, center-oriented Gated Recurrent Unit (Co-GRU) network has the ability to learn and compensate for inter-symbol interference (ISI) with low computation cost while satisfying the gradient backpropagation (BP) condition. In this paper, the Co-GRU structure is adopted for both channel modeling and decoder implementation in E2E design for long-haul coherent wavelength division multiplexing (WDM) transmission systems, which can enhance the performance of general mutual information (GMI) and Q2-factor. For the E2E system with Co-GRU based decoder, the gain of GMI and Q2-factor are respectively improved 0.2 bits/sym and 0.48dB, compared to that of the conventional QAM system, for a 5-channel dual-polarization coherent system transmitting over 960km standard single mode fiber (SSMF). This work paves the way for further study of the application of the Co-GRU structure for both the data-driven channel modeling and the decoder performance improvement in E2E design.
Interplay of viscosity and wettability controls fluid displacement in porous media

Direct numerical simulations are used to elucidate the interplay of wettability and fluid viscosities on immiscible fluid displacements in a heterogeneous porous medium.We classify the flow regimes based using qualitative and quantitative analysis into viscous fingering (low $M$), compact displacement (high $M$), and an intermediate transition regime ($M \approx 1$). We use stability analysis to obtain theoretical phase boundaries between these regimes, which agree well with our analyses. At the macroscopic (sample) scale, we find that wettability strongly controls the threshold $M$ (at which the regimes change). At the pore scale, wettability alters the dominant pore-filling mechanism. At very small $M$ (viscous fingering regime), smaller pore spaces are preferentially invaded during imbibition, with flow of films of invading fluid along the pore walls. In contrast, during drainage, bursts result in filling of pores irrespective of their size. As $M$ increases, the effect of wettability decreases as cooperative filling becomes the dominant mechanism regardless of wettability. This suggest that for imbibition at a given contact angle, decreasing $M$ is associated with change in effective wetting from neutral-wet (cooperative filling) to strong-wet (film flow).
MaNGA DynPop – I. Quality-assessed stellar dynamical modelling from integral-field spectroscopy of 10K nearby galaxies: a catalogue of masses, mass-to-light ratios, density profiles and dark matter

This is the first paper in our series on the combined analysis of the Dynamics and stellar Population (DynPop) for the MaNGA survey in the final SDSS Data Release 17 (DR17). Here we present a catalogue of dynamically-determined quantities for over 10000 nearby galaxies based on integral-field stellar kinematics from the MaNGA survey. The dynamical properties are extracted using the axisymmetric Jeans Anisotropic Modelling (JAM) method, which was previously shown to be the most accurate for this kind of study. We assess systematic uncertainties using eight dynamical models with different assumptions. We use two orientations of the velocity ellipsoid: either cylindrically-aligned JAMcyl or spherically-aligned JAMsph. We also make four assumptions for the models’ dark vs. luminous matter distributions: (1) mass-follows-light, (2) free NFW dark halo, (3) cosmologically-constrained NFW halo, (4) generalized NFW dark halo, i.e. with free inner slope. In this catalogue, we provide the quantities related to the mass distributions (e.g. the density slopes and enclosed mass within a sphere of a given radius for total mass, stellar mass, and dark matter mass components). We also provide the complete models which can be used to compute the full luminous and mass distribution of each galaxy. Additionally, we visually assess the qualities of the models to help with model selections. We estimate the observed scatter in the measured quantities which decreases as expected with improvements in quality. For the best data quality, we find a remarkable consistency of measured quantities between different models, highlighting the robustness of the results.
A Multi-frequency Magnetic Particle Spectroscopy System for the Characterization of Magnetic Nanoparticles

Magnetic particle spectroscopy (MPS) is one of the most versatile methods to characterize the magnetic properties of magnetic nanoparticles (MNPs). The excitation magnetic field is one of the most crucial factors that affects the MPS signal of the MNPs. In this study, a multi-frequency MPS system is developed to investigate the MPS signal of MNPs in different ac magnetic fields. The MPS system consists of a multi-channel excitation module for the generation of different-frequency ac magnetic fields and a detection module for the measurement of the magnetic response of the MNPs. The MPS system allows to generate ac magnetic fields with a frequency up to 32.6 kHz and amplitude up to 25 mT. The MPS signals of the MNPs in different ac magnetic fields are measured to systematically evaluate the performance of the multi-frequency MPS system, including the MNP spectra and its dynamic magnetization curve. In addition, the signal-to-noise ratio (SNR) of the MPS system is quantitively assessed with measured MPS signals of a given MNP sample and DI water. Furthermore, a series of MNP samples with different iron concentrations are prepared and measured to evaluate the limit-of-detection (LOD) in terms of iron concentration. The influence of the excitation magnetic field, including frequency and amplitude, is discussed based on the SNRs of the measured harmonics. Experimental results show that the LOD is 2.3 ng in terms of iron.
The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior

Deep Image Prior (DIP) shows that some network architectures inherently tend towards generating smooth images while resisting noise, a phenomenon known as spectral bias. Image denoising is a natural application of this property. Although denoising with DIP mitigates the need for large training sets, two often intertwined practical challenges need to be overcome: architectural design and noise fitting. Existing methods either handcraft or search for suitable architectures from a vast design space, due to the limited understanding of how architectural choices affect the denoising outcome. In this study, we demonstrate from a frequency perspective that unlearnt upsampling is the main driving force behind the denoising phenomenon with DIP. This finding leads to straightforward strategies for identifying a suitable architecture for every image without laborious search. Extensive experiments show that the estimated architectures achieve superior denoising results than existing methods with up to 95% fewer parameters. Thanks to this under-parameterization, the resulting architectures are less prone to noise-fitting 1.
A Review of Deep Learning for Video Captioning

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction. In essence, VC involves understanding a video and describing it with language. Captioning is used in a host of applications from creating more accessible interfaces (e.g., low-vision navigation) to video question answering (V-QA), video retrieval and content generation. This survey covers deep learning-based VC, including but, not limited to, attention-based architectures, graph networks, reinforcement learning, adversarial networks, dense video captioning (DVC), and more. We discuss the datasets and evaluation metrics used in the field, and limitations, applications, challenges, and future directions for VC.