Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Action Unit Detection via Adaptive Attention and Relation

Facial action unit (AU) detection is challenging due to the difficulty in capturing correlated information from subtle and dynamic AUs. Existing methods often resort to the localization of correlated regions of AUs, in which predefining local AU attentions by correlated facial landmarks often discards essential parts, or learning global attention maps often contains irrelevant areas. Furthermore, existing relational reasoning methods often employ common patterns for all AUs while ignoring the specific way of each AU. To tackle these limitations, we propose a novel adaptive attention and relation (AAR) framework for facial AU detection. Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions. Moreover, considering the diversity and dynamics of AUs, we propose an adaptive spatio-temporal graph convolutional network to simultaneously reason the independent pattern of each AU, the inter-dependencies among AUs, as well as the temporal dependencies. Extensive experiments show that our approach (i) achieves competitive performance on challenging benchmarks including BP4D, DISFA, and GFT in constrained scenarios and Aff-Wild2 in unconstrained scenarios, and (ii) can precisely learn the regional correlation distribution of each AU.
SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification

The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them quickly and cheaply. This paper introduces SpecInfer, an LLM serving system that accelerates generative LLM inference with speculative inference and token tree veriﬁcation. A key insight behind SpecInfer is to combine various collectively boost-tuned small language models to jointly predict the LLM’s outputs; the predictions are organized as a token tree, whose nodes each represent a candidate token sequence. The correctness of all candidate token sequences represented by a token tree is veriﬁed by the LLM in parallel using a novel tree-based parallel decoding mechanism. SpecInfer uses an LLM as a token tree veriﬁer instead of an incremental decoder, which signiﬁcantly reduces the end-to-end latency and computational requirement for serving generative LLMs while provably preserving model quality.
Frequency perturbation integral for piezoelectric quartz crystal microbalances based on scalar differential equations

We study frequency shifts in a piezoelectric quartz resonator induced by a surface mass layer for sensor applications. The scalar differential equations for thickness-shear modes in a quartz plate are used. A first-order perturbation analysis is performed. The frequency shift is obtained and is expressed by a perturbation integral. It produces the well-known Sauerbrey equation for mass sensitivity in the special case of a uniform mass layer. As an application of the perturbation integral, frequency shifts due to a nonuniform mass layer are calculated.
An efficient solver for ASP(Q)

Answer Set Programming with Quantifiers ASP(Q) extends Answer Set Programming (ASP) to allow for declarative and modular modeling of problems from the entire polynomial hierarchy. The first implementation of ASP(Q), called QASP, was based on a translation to Quantified Boolean Formulae (QBF) with the aim of exploiting the well-developed and mature QBF-solving technology. However, the implementation of the QBF encoding employed in qasp is very general and might produce formulas that are hard to evaluate for existing QBF solvers because of the large number of symbols and subclauses. In this paper, we present a new implementation that builds on the ideas of QASP and features both a more efficient encoding procedure and new optimized encodings of ASP(Q) programs in QBF. The new encodings produce smaller formulas (in terms of the number of quantifiers, variables, and clauses) and result in a more efficient evaluation process. An algorithm selection strategy automatically combines several QBF-solving back-ends to further increase performance. An experimental analysis, conducted on known benchmarks, shows that the new system outperforms QASP.
Direct Numerical Simulation of passive scalar mixing in axisymmetric swirling jets

Abstract Direct Numerical Simulation (DNS) of transport of multiple passive scalars with varying molecular diffusivity in a spatially evolving low-swirl axisymmetric swirling jet is performed at a Reynolds number of 1500. The swirling jets discussed are limited to non-vortex breakdown flow. Multiple passive scalars with differing molecular diffusivity are introduced into the jet and allowed to evolve with the flow field. The DNS result reveals that the scalar decay varies with the jet swirl number as well as the Schmidt number corresponding to the scalar. Furthermore, the scalar decay exhibits non-linearity.
Spectral Clustering via Orthogonalization-Free Methods

Graph Signal Filter used as dimensionality reduction in spectral clustering usually requires expensive eigenvalue estimation. We analyze the filter in an optimization setting and propose to use four orthogonalization-free methods by optimizing objective functions as dimensionality reduction in spectral clustering. The proposed methods do not utilize any orthogonalization, which is known as not well scalable in a parallel computing environment. Our methods theoretically construct adequate feature space, which is, at most, a weighted alteration to the eigenspace of a normalized Laplacian matrix. We numerically hypothesize that the proposed methods are equivalent in clustering quality to the ideal Graph Signal Filter, which exploits the exact eigenvalue needed without expensive eigenvalue estimation. Numerical results show that the proposed methods outperform Power Iteration-based methods and Graph Signal Filter in clustering quality and computation cost. Unlike Power Iteration-based methods and Graph Signal Filter which require random signal input, our methods are able to utilize available initialization in the streaming graph scenarios. Additionally, numerical results show that our methods outperform ARPACK and are faster than LOBPCG in the streaming graph scenarios. We also present numerical results showing the scalability of our methods in multithreading and multiprocessing implementations to facilitate parallel spectral clustering.
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation

Distributed training of GNNs enables learning on massive graphs (e.g., social and e-commerce networks) that exceed the storage and computational capacity of a single machine. To reach performance comparable to centralized training, distributed frameworks focus on maximally recovering cross-instance node dependencies with either communication across instances or periodic fallback to centralized training, which create overhead and limit the framework scalability. In this work, we present a simplified framework for distributed GNN training that does not rely on the aforementioned costly operations, and has improved scalability, convergence speed and performance over the state-of-the-art approaches. Specifically, our framework (1) assembles independent trainers, each of which asynchronously learns a local model on locally-available parts of the training graph, and (2) only conducts periodic (time-based) model aggregation to synchronize the local models. Backed by our theoretical analysis, instead of maximizing the recovery of cross-instance node dependencies -- which has been considered the key behind closing the performance gap between model aggregation and centralized training -- , our framework leverages randomized assignment of nodes or super-nodes (i.e., collections of original nodes) to partition the training graph such that it improves data uniformity and minimizes the discrepancy of gradient and loss function across instances. In our experiments on social and e-commerce networks with up to 1.3 billion edges, our proposed RandomTMA and SuperTMA approaches -- despite using less training data -- achieve state-of-the-art performance and 2.31x speedup compared to the fastest baseline, and show better robustness to trainer failures.
Sharp interface limit for inhomogeneous incompressible Navier-Stokes/Allen-Cahn system in a bounded domain via a relative energy method

This paper concerns the sharp interface limit of solutions to the inhomogeneous incompressible Navier-Stokes/Allen-Cahn coupled system in a bounded domain $\Omega \subset \mathbb{R}^n,\ n =2,3$. Based on a relative energy method, we prove that the solutions to the Navier-Stokes/Allen-Cahn system converge to the corresponding solutions to a sharp interface model provided that the thickness of the diffuse interfacial zone goes to zero. It is noted that the relative energy method can avoid both the spectral estimates of the linearized Allen-Cahn operator and the construction of approximate solutions by the matched asymptotic expansion method in the study of the sharp interface limit process. And some suitable functionals are designed and estimated by elaborated energy methods accordingly.
Effect of Spherical Polarization on the Magnetic Spectrum of the Solar Wind

Magnetic fluctuations in the solar wind are often observed to maintain constant magnitude of the magnetic field in a manner consistent with spherically polarized large-amplitude Alfvén waves. We investigate the effect of spherical polarization on the magnetic spectral index through a statistical survey of magnetic fluctuations observed by Parker Solar Probe between 20 R ⊙ and 200 R ⊙. We find that deviations from spherical polarization, i.e., changes in ∣ B ∣ (compressive fluctuations) and one-dimensional discontinuities, have a dramatic effect on the scaling behavior of the turbulent fluctuations. We show that shallow k −3/2 spectra are only observed for three-dimensional structures of constant magnetic field strength, which we identify as large-amplitude Alfvén waves. The presence of compressive fluctuations coincides with a steepening of the spectrum up to k −5/3. Steeper power-law scalings approaching k −2 are observed when the fluctuations are dominated by discontinuities. Near-Sun fluctuations are found to be the most spherically polarized, suggesting that this spherical state is fundamental to the generation of the solar wind. With increasing distance from the Sun, fluctuations are found to become less three-dimensional and more compressive, which may indicate the breakdown of the Alfvénic equilibrium state.
Anomaly Detection Dataset for Industrial Control Systems

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the ‘ICS-Flow’ dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, “ICSFlowGenerator,” for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.
FaaSKeeper: Learning from Building Serverless Services with ZooKeeper as an Example

FaaS (Function-as-a-Service) brought a fundamental shift into cloud computing: (persistent) virtual machines have been replaced with dynamically allocated resources, trading locality and statefulness for a pay-as-you-go model more suitable for varying and infrequent workloads. However, adapting services to functions in the serverless paradigm while still fulfilling functional requirements is challenging. In this work, we demonstrate how ZooKeeper, a centralized coordination service that offers a safe and wait-free consensus mechanism, can be redesigned to benefit from serverless computing. We define synchronization primitives to extend the capabilities of scalable cloud storage and contribute a set of requirements for efficient and scalable FaaS computing. We introduce FaaSKeeper, the first coordination service built on serverless functions and cloud-native services, and share serverless design lessons based on our experiences of implementing a ZooKeeper model deployable to clouds today. FaaSKeeper provides the same consistency guarantees and interface as ZooKeeper, with a serverless price model that lowers costs up to 450 times on infrequent workloads.
Coagent Networks: Generalized and Scaled

Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpropagation-based deep learning (BDL) that overcomes some of backpropagation's main limitations. For example, coagent networks can compute different parts of the network \emph{asynchronously} (at different rates or at different times), can incorporate non-differentiable components that cannot be used with backpropagation, and can explore at levels higher than their action spaces (that is, they can be designed as hierarchical networks for exploration and/or temporal abstraction). However, the coagent framework is not just an alternative to BDL; the two approaches can be blended: BDL can be combined with coagent learning rules to create architectures with the advantages of both approaches. This work generalizes the coagent theory and learning rules provided by previous works; this generalization provides more flexibility for network architecture design within the coagent framework. This work also studies one of the chief disadvantages of coagent networks: high variance updates for networks that have many coagents and do not use backpropagation. We show that a coagent algorithm with a policy network that does not use backpropagation can scale to a challenging RL domain with a high-dimensional state and action space (the MuJoCo Ant environment), learning reasonable (although not state-of-the-art) policies. These contributions motivate and provide a more general theoretical foundation for future work that studies coagent networks.
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective

We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction targets into joint span detection, classification and association problems with a unified extractive framework, namely UniEX. UniEX can synchronously encode schema-based prompt and textual information, and collaboratively learn the generalized knowledge from pre-defined information using the auto-encoder language models. We develop a traffine attention mechanism to integrate heterogeneous factors including tasks, labels and inside tokens, and obtain the extraction target via a scoring matrix. Experiment results show that UniEX can outperform generative universal IE models in terms of performance and inference-speed on 14 benchmarks IE datasets with the supervised setting. The state-of-the-art performance in low-resource scenarios also verifies the transferability and effectiveness of UniEX.
Sustainability in Computing Education: A Systematic Literature Review

Research shows that the global society as organized today, with our current technological and economic system, is impossible to sustain. We are living in an era in which human activities in highly industrialized countries are responsible for overshooting several planetary boundaries, with poorer communities contributing the least to the problems but being impacted the most. At the same time, technical and economic gains fail to provide society at large with equal opportunities and improved quality of life. This paper describes approaches taken in computing education to address the issue of sustainability. It presents results of a systematic review of the literature on sustainability in computing education. From a set of 572 publications extracted from six large digital libraries plus snowballing, we distilled and analyzed the 89 relevant primary studies. Using an inductive and deductive thematic analysis, we study (1) conceptions of sustainability, computing, and education, (2) implementations of sustainability in computing education, and (3) research on sustainability in computing education. We present a framework capturing learning objectives and outcomes as well as pedagogical methods for sustainability in computing education. These results can be mapped to existing standards and curricula in future work. We find that only a few of the articles engage with the challenges as calling for drastic systemic change, along with radically new understandings of computing and education. We suggest that future work should connect to the substantial body of critical theory such as feminist theories of science and technology. Existing research on sustainability in computing education may be considered rather immature as the majority of articles are experience reports with limited empirical research.
General Continuity Bounds for Quantum Relative Entropies

In this article, we generalize a proof technique by Alicki, Fannes and Winter and introduce a method to prove continuity bounds for entropic quantities derived from different quantum relative entropies. For the Umegaki relative entropy, we mostly recover known almost optimal bounds, whereas, for the Belavkin-Staszewski relative entropy, our bounds are new. Finally, we use these continuity bounds to derive a new entropic uncertainty relation. This is a short version of [1].
Scratch Copilot Evaluation: Assessing AI-Assisted Creative Coding for Families

How can AI enhance creative coding experiences for families? This study explores the potential of large language models (LLMs) in helping families with creative coding using Scratch. Based on our previous user study involving a prototype AI assistant, we devised three evaluation scenarios to determine if LLMs could help families comprehend game code, debug programs, and generate new ideas for future projects. We utilized 22 Scratch projects for each scenario and generated responses from LLMs with and without practice tasks, resulting in 120 creative coding support scenario datasets. In addition, the authors independently evaluated their precision, pedagogical value, and age-appropriate language. Our findings show that LLMs achieved an overall success rate of more than 80\% on the different tasks and evaluation criteria. This research offers valuable information on using LLMs for creative family coding and presents design guidelines for future AI-supported coding applications. Our evaluation framework, together with our labeled evaluation data, is publicly available.
Dislocation correlations and the continuum dynamics of the weak line bundle ensemble

Progress toward a first-principles theory of plasticity and work-hardening is currently impeded by an insufficient picture of dislocation kinetics (the dynamic effect of driving forces in a given dislocation theory). This is because present methods ignore the short-range interaction of dislocations. This work presents a kinetic theory of continuum dislocation dynamics in a vector density framework which takes into account the short-range interactions by means of suitably defined correlation functions. The weak line bundle ensemble of dislocations is defined, whereby the treatment of dislocations by a vector density is justified. It is then found by direct averaging of the dislocation transport equation that additional driving forces arise which are dependent on the dislocation correlation. A combination of spatial coarse-graining and statistical averaging of discrete dislocation systems are used to evaluate the various classes of tensorial dislocation correlations which arise in the line bundle kinetic theory. A novel, chiral classification of slip system interactions in FCC crystals is used to define proper and improper rotations by which correlation functions corresponding to six interaction classifications can be evaluated. The full set of these six dislocation correlations are evaluated from discrete data. Only the self-correlations (for densities of like slip system) are found to be highly anisotropic. All six classes of correlation functions are found to decay within 2-4 times the coarse-graining distance. The correlations corresponding to the coplanar interactions are found to be negligible. Implications of the evaluated correlations for the implementation of vector density continuum dislocation dynamics are discussed, especially in terms of an additional correlation component of the driving force and a gesture toward a coarse-grained dislocation mobility.
SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Learning from human feedback has been shown to be effective at aligning language models with human preferences. Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data. In this work we show how the recently introduced Sequence Likelihood Calibration (SLiC), can also be used to effectively learn from human preferences (SLiC-HF). Furthermore, we demonstrate this can be done with human feedback data collected for a different model, similar to off-policy, offline RL data. Automatic and human evaluation experiments on the TL;DR summarization task show that SLiC-HF significantly improves supervised fine-tuning baselines. Furthermore, SLiC-HF presents a competitive alternative to the PPO RLHF implementation used in past work while being much simpler to implement, easier to tune and more computationally efficient in practice.