Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous mixtures of dictionary functions to approximate subspace invariance in Koopman operators

Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . A widely used algorithm, is Extended Dynamic Mode Decomposition , where the dictionary functions are drawn from a ﬁxed, homogeneous class of functions. Recently, deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We discover a novel class of dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we deﬁne as uniform ﬁnite approximate closure. We discover that structured mixing of heterogeneous dictionary functions drawn from diﬀerent classes of nonlinear functions achieve the same accuracy and dimensional scaling as deepDMD. This mixed dictionary does so with an order of magnitude reduction in parameters, while maintaining geometric interpretability. Our results provide a hypothesis to explain the success of deep neural networks in learning numerical approximations to Koopman operators.
Parameter-Efﬁcient Image-to-Video Transfer Learning

Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full ﬁne-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage. This has led to a new research direction in parameter-efﬁcient transfer learning. However, existing attempts typically focus on downstream tasks from the same modality ( e.g. , image understanding) of the pre-trained model. This creates a limit because in some speciﬁc modalities, ( e.g. , video understanding) such a strong pre-trained model with sufﬁcient knowledge is less or not available. In this work, we investigate such a novel cross-modality transfer learning setting, namely parameter-efﬁcient image-to-video transfer learning . To solve this problem, we propose a new Spatio-Temporal Adapter (ST-Adapter) for parameter-efﬁcient ﬁne-tuning per video task. With a built-in spatio-temporal reasoning capability in a compact design, ST-Adapter enables a pre-trained image model without temporal knowledge to reason about dynamic video content at a small ( ∼ 8%) per-task parameter cost, requiring approximately 20 times fewer updated parameters compared to previous work. Extensive experiments on video action recognition tasks show that our ST-Adapter can match or even outperform the strong full ﬁne-tuning strategy and state-of-the-art video models, whilst enjoying the advantage of parameter efﬁciency.
Assessment of U.S. Department of Transportation Lane-Level Map for Connected Vehicle Applications

High-definition (Hi-Def) digital maps are an indispensable automated driving technology that is developing rapidly. There are various commercial or governmental map products in the market. It is notable that the U.S. Department of Transportation (USDOT) map tool allows the user to create MAP and Signal Phase and Timing (SPaT) messages with free access. However, an analysis of the accuracy of this map tool is currently lacking in the literature. This paper provides such an analysis. The analysis manually selects 39 feature points within about 200 meters of the verified point and 55 feature points over longer distances from the verified point. All feature locations are surveyed using GNSS and mapped using the USDOT tool. Different error sources are evaluated to allow assessment of the USDOT map accuracy. In this investigation, The USDOT map tool is demonstrated to achieve 17 centimeters horizontal accuracy, which meets the lane-level map requirement. The maximum horizontal map error is less than 30 centimeters.
Towards a Failure-Aware SDLC for Internet of Things

Internet of Things systems carry substantial engineering risks including catastrophic physical failures. To aid software engineers in developing reliable IoT systems, we conducted an experiment to evaluate the influence of learning treatments on design decisions. Specifically, we compared the influence of a set of design guidelines (current practice) and failure stories (proposed learning treatment) on developers’ design rationale. We conducted an experiment with 21 computer engineering students using a questionnaire. We observed that both treatments helped subjects reason about criticality as a part of their design rationale. However, failure stories had a greater effect at enabling subjects to reason about safety as a part of their design rationale. We share our results illustrating the effects of a failure-aware design process and propose new research directions to enable a Failure-Aware Software Development Life Cycle for IoT development.
Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation

In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. First, we propose an Angle Correlation Point Convolution (ACPConv) module to effectively learn the local shapes of points. Second, based upon ACPConv, we introduce a local multi-scale split (MSS) block that hierarchically connects features within one single block and gradually enlarges the receptive field which is beneficial for exploiting local context. Third, inspired by HRNet that has excellent performance on 2D image vision tasks, we build an HRNet customized for point cloud to learn global multi-scale context. Lastly, we introduce a point-wise attention fusion approach that fuses multi-resolution predictions and further improves point cloud semantic segmentation performance. Our experimental results and ablations on several benchmark datasets show that our proposed method is effective and able to achieve state-of-the-art performances compared to existing methods.
DF-SCA: Dynamic Frequency Side Channel Attacks are Practical

The arm race between hardware security engineers and side-channel researchers has become more competitive with more sophisticated attacks and defenses in the last decade. While modern hardware features improve the system performance significantly, they may create new attack surfaces for malicious people to extract sensitive information about users without physical access to the victim device. Although many previously exploited hardware and OS features were patched by OS developers and chip vendors, any feature that is accessible from userspace applications can be exploited to perform software-based side-channel attacks. In this paper, we present DF-SCA, which is a software-based dynamic frequency side-channel attack on Linux and Android OS devices. We exploit unprivileged access to cpufreq interface that exposes real-time CPU core frequency values directly correlated with the system utilization, creating a reliable side-channel for attackers. We show that Dynamic Voltage and Frequency Scaling (DVFS) feature in modern systems can be utilized to perform website fingerprinting attacks for Google Chrome and Tor browsers on modern Intel, AMD, and ARM architectures. We further extend our analysis to a wide selection of scaling governors on Intel and AMD CPUs, verifying that all scaling governors provide enough information on the visited web page. Moreover, we extract properties of keystroke patterns on frequency readings, that leads to 95% accuracy to distinguish the keystrokes from other activities on Android phones. We leverage inter-keystroke timings of a user by training a k-th nearest neighbor model, which achieves 88% password recovery rate in the first guess on Bank of America application. Finally, we propose several countermeasures to mask the user activity to mitigate DF-SCA on Linux-based systems.
DSME-LoRa: Seamless Long-range Communication between Arbitrary Nodes in the Constrained IoT

Long-range radio communication is preferred in many IoT deployments, as it avoids the complexity of multi-hop wireless networks. LoRa is a popular, energy-efficient wireless modulation but its networking substrate LoRaWAN introduces severe limitations to its users. In this article, we present and thoroughly analyze DSME-LoRa, a system design of LoRa with IEEE 802.15. 4 Deterministic Synchronous Multichannel Extension (DSME) as a MAC layer. DSME-LoRa offers the advantage of seamless client-to-client communication beyond the pure gateway-centric transmission of LoRaWAN. We evaluate its feasibility via a full-stack implementation on the popular RIOT operating system, assess its steady-state packet flows in an analytical stochastic Markov model, and quantify its scalability in massive communication scenarios using large-scale network simulations. Our findings indicate that DSME-LoRa is indeed a powerful approach that opens LoRa to standard network layers and outperforms LoRaWAN in many dimensions.
Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA

In the realm of software development, the clarity, completeness, and comprehensiveness of requirements significantly impact the success of software systems. The Software Requirement Specification (SRS) document, a cornerstone of the software development life cycle, delineates both functional and nonfunctional requirements, playing a pivotal role in ensuring the quality and timely delivery of software projects. However, the inherent natural language representation of these requirements poses challenges, leading to potential misinterpretations and conflicts. This study addresses the need for conflict identification within requirements by delving into their semantic compositions and contextual meanings. Our research introduces an automated supervised conflict detection method known as the Supervised Semantic Similarity-based Conflict Detection Algorithm (S3CDA). This algorithm comprises two phases: identifying conflict candidates through textual similarity and employing semantic analysis to filter these conflicts. The similarity-based conflict detection involves leveraging sentence embeddings and cosine similarity measures to identify pertinent candidate requirements. Additionally, we present an unsupervised conflict detection algorithm, UnSupCDA, combining key components of S3CDA, tailored for unlabeled software requirements. Generalizability of our methods is tested across five SRS documents from diverse domains. Our experimental results demonstrate the efficacy of the proposed conflict detection strategy, achieving high accuracy in automated conflict identification.
Disentangling Embedding Spaces with Minimal Distributional Assumptions

Interest in understanding and factorizing learned embedding spaces is growing. For instance, recent concept-based explanation techniques analyze a machine learning model in terms of interpretable latent components. Such components have to be discovered in the model’s embedding space, e.g., through independent component analysis (ICA) or modern disentanglement learning techniques. While these unsupervised approaches offer a sound formal framework, they either require access to a data generating function or impose rigid assumptions on the data distribution, such as independence of components, that are often violated in practice. In this work, we link conceptual explainability for vision models with disentanglement learning and ICA. This enables us to provide first theoretical results on how components can be identified without requiring any distributional assumptions. From these insights, we derive the disjoint attributions (DA) concept discovery method that is applicable to a broader class of problems than current approaches, but yet possesses a formal identifiability guarantee. In an extensive comparison against component analysis and over 300 state-of-the-art disentanglement models, DA stably maintains superior performance, even under varying distributions and correlation strengths.
Engineering a pure Dirac regime in ZrTe$_5$

Real-world topological semimetals typically exhibit Dirac and Weyl nodes that coexist with trivial Fermi pockets. This tends to mask the physics of the relativistic quasiparticles. Using the example of ZrTe_55, we show that strain provides a powerful tool for in-situ tuning of the band structure such that all trivial pockets are pushed far away from the Fermi energy, but only for a certain range of Van der Waals gaps. Our results naturally reconcile contradicting reports on the presence or absence of additional pockets in ZrTe_55, and provide a clear map of where to find a pure three-dimensional Dirac semimetallic phase in the structural parameter space of the material.
Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

Data-driven, learning-based control methods offer the potential to improve operations in complex systems, and model-free deep reinforcement learning represents a popular approach to data-driven control. However, existing classes of algorithms present a trade-off between two important deployment requirements for real-world control: (i) practical performance guarantees and (ii) data efficiency. Off-policy algorithms make efficient use of data through sample reuse but lack theoretical guarantees, while on-policy algorithms guarantee approximate policy improvement throughout training but suffer from high sample complexity. In order to balance these competing goals, we develop a class of Generalized Policy Improvement algorithms that combines the policy improvement guarantees of on-policy methods with the efficiency of sample reuse. We demonstrate the benefits of this new class of algorithms through extensive experimental analysis on a variety of continuous control tasks from the DeepMind Control Suite.
Palatini R 2 quintessential inflation

We construct a model of quintessential inflation in Palatini R 2 gravity employing a scalar field with a simple exponential potential and coupled to gravity with a running non-minimal coupling. At early times, the field acts as the inflaton, while later on it becomes the current dark energy. Combining the scalar sector with an ideal fluid, we study the cosmological evolution of the model from inflation all the way to dark energy domination. We interpret the results in the Einstein frame, where a coupling emerges between the fluid and the field, feeding energy from the former to the latter during the matter-dominated era. We perform a numerical scan over the parameter space and find points that align with observations for both the inflationary CMB data and the late-time behaviour. The final dark energy density emerges from an interplay between the model parameters, without requiring the extreme fine-tuning of the cosmological constant in ΛCDM.
Efficient Deep Learning Using Non-Volatile Memory Technology

Embedded machine learning (ML) systems have now become the dominant platform for deploying ML serving tasks and are projected to become of equal importance for training ML models. With this comes the challenge of overall ef-ﬁcient deployment, in particular low power and high throughput implementations, under stringent memory constraints. In this context, non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have signiﬁcant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While prior work has investigated sev-eral architectural implications of NVM for generic applications, in this work we present DeepNVM++ , a comprehensive framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-speciﬁc circuit-level models and the actual memory behavior of various DL workloads. DeepNVM++ relies on iso-capacity and iso-area performance and energy models for last-level caches implemented using conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3 . 8 × and 4 . 7 × energy-delay product (EDP) reduction and 2 . 4 × and 2 . 8 × area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide up to 2 . 2 × and 2 . 4 × EDP reduction and accommodate 2 . 3 × and 3 . 3 × cache capacity when compared to SRAM, respectively. We also perform a scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of magnitude EDP reduction when compared to SRAM for large cache capacities. DeepNVM++ is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPUs for DL applications.
Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency

The class imbalance problem is important and challenging. Ensemble approaches are widely used to tackle this problem because of their effectiveness. However, existing ensemble methods are always applied into original samples, while not considering the structure information among original samples. The limitation will prevent the imbalanced learning from being better. Besides, research shows that the structure information among samples includes local and global structure information. Based on the analysis above, an imbalanced ensemble algorithm with the deep sample pre-envelope network (DSEN) and local-global structure consistency mechanism (LGSCM) is proposed here to solve the problem.This algorithm can guarantee high-quality deep envelope samples for considering the local manifold and global structures information, which is helpful for imbalance learning. First, the deep sample envelope pre-network (DSEN) is designed to mine structure information among samples.Then, the local manifold structure metric (LMSM) and global structure distribution metric (GSDM) are designed to construct LGSCM to enhance distribution consistency of interlayer samples. Next, the DSEN and LGSCM are put together to form the final deep sample envelope network (DSEN-LG). After that, base classifiers are applied on the layers of deep samples respectively.Finally, the predictive results from base classifiers are fused through bagging ensemble learning mechanism. To demonstrate the effectiveness of the proposed method, forty-four public datasets and more than ten representative relevant algorithms are chosen for verification. The experimental results show that the algorithm is significantly better than other imbalanced ensemble algorithms.
Improving Tese Case Generation for Python Native Libraries Through Constraints on Input Data Structures

—Modern Python projects execute computational functions using native libraries and give Python interfaces to boost execution speed; hence, testing these libraries becomes critical to the project’s robustness. One challenge is that existing approaches use coverage to guide generation, but native libraries run as black boxes to Python code with no execution information. Another is that dynamic binary instrumentation reduces testing performance as it needs to monitor both native libraries and the Python virtual machine. To address these challenges, in this paper, we propose an automated test case generation approach that works at the Python code layer. Our insight is that many path conditions in native libraries are for processing input data structures through interacting with the VM. In our approach, we instrument the Python Interpreter to monitor the interactions between native libraries and VM, derive constraints on the structures, and then use the constraints to guide test case generation. We implement our approach in a tool named PyCing and apply it to six widely-used Python projects. The experimental results reveal that with the structure constraint guidance, PyCing can cover more execution paths than existing test cases and state-of-the-art tools. Also, with the checkers in the testing framework Pytest , PyCing can identify segmentation faults in 10 Python interfaces and memory leaks in 9. Our instrumentation strategy also has an acceptable inﬂuence on testing efﬁciency.
Image of a Kerr-Melvin black hole with a thin accretion disk

In this present work, we study the observational appearance of Kerr-Melvin black hole (KMBH) illuminated by an accretion disk. The accretion disk is assumed to be located on the equatorial plane and be thin both geometrically and optically. Considering the fact that outside the innermost stable circular orbit (ISCO) the accretion ﬂow moves in prograde or retrograde circular orbit and falls towards the horizon along plunging orbit inside the ISCO, we develop the numerical backward ray-tracing method and obtain the images of KMBH accompanying with the accretion disk for various black hole spins, strengths of magnetic ﬁelds and inclination angles of observers. We present the intensity distribution horizontally and longitudinally and show the proﬁles of the red-shift for the direct and lensed images. Our study suggests that the inner shadow and critical curves can be used to estimate the magnetic ﬁeld around a black hole without degeneration.
InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments

Due to the vast number of students enrolled in Massive Open Online Courses (MOOCs), there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Such state-of-the-art techniques use program clustering to take advantage of previous correct student implementations to repair a given new incorrect submission. Usually, these repair techniques use clustering methods since analyzing all available correct student submissions to repair a program is not feasible. The clustering methods use program representations based on several features such as abstract syntax tree (AST), syntax, control flow, and data flow. However, these features are sometimes brittle when representing semantically similar programs. This paper proposes InvAASTCluster, a novel approach for program clustering that takes advantage of dynamically generated program invariants observed over several program executions to cluster semantically equivalent IPAs. Our main objective is to find a more suitable representation of programs using a combination of the program's semantics, through its invariants, and its structure, through its anonymized abstract syntax tree. The evaluation of InvAASTCluster shows that the proposed program representation outperforms syntax-based representations when clustering a set of different correct IPAs. Furthermore, we integrate InvAASTCluster into a state-of-the-art clustering-based program repair tool and evaluate it on a set of IPAs. Our results show that InvAASTCluster advances the current state-of-the-art when used by clustering-based program repair tools by repairing a larger number of students' programs in a shorter amount of time.