Miracle-dz/UniFAR-FTUs huggingface dataset:
POSTER V2: A simpler and stronger facial expression recognition network

Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extraction. In cross-fusion, we use window-based cross-attention mechanism replacing vanilla cross-attention mechanism. We remove the image-to-landmark branch in the two-stream design. For multi-scale feature extraction, POSTER++ combines images with landmark's multi-scale features to replace POSTER's pyramid design. Extensive experiments on several standard datasets show that our POSTER++ achieves the SOTA FER performance with the minimum computational cost. For example, POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4G floating point operations (FLOPs) and 43.7M parameters (Param). This demonstrates the effectiveness of our improvements.
Cops and robbers on P5-free graphs

We prove that every connected $P_5$-free graph has cop number at most two, solving a conjecture of Sivaraman. In order to do so, we first prove that every connected $P_5$-free graph $G$ with independence number at least three contains a three-vertex induced path with vertices $a \hbox{-} b \hbox{-} c$ in order, such that every neighbour of $c$ is also adjacent to one of $a,b$.
Liquid Rope-Coil Effect

The tendency for a falling stream of very viscous liquid to coil like a rope when it strikes a horizontal surface is studied and the building of a “cone” of liquid of considerable height above the surface is observed. It is shown that, although the liquid stream as a whole appears to rotate about the axis of the “cone,” the particles of liquid do not actually rotate about this axis. Variations of the liquid rope-coil effect are discussed.
Boosting Automated Patch Correctness Prediction via Pre-trained Language Model

—Automated program repair (APR) aims to ﬁx software bugs automatically without human debugging efforts and plays a crucial role in software development and maintenance. Despite the recent signiﬁcant progress in the number of ﬁxed bugs, APR is still challenged by a long-standing overﬁtting problem (i.e., the generated patch is plausible but overﬁtting). Various techniques have thus been proposed to address the overﬁtting problem. Among them, leveraging deep learning approaches to predict patch correctness automatically is emerging along with the available large-scale patch benchmarks recently. However, existing learning-based techniques mainly rely on manually-designed code features, which can be extremely costly and challenging to construct in practice. In this paper, we propose APPT, a pre-trained model-based automated patch correctness assessment technique, which treats the source code as a sequence of tokens without extra overhead to design a mass of features from different perspectives. In particular, APPT adopts a pre-trained model as the encoder stack, followed by an LSTM stack and a deep learning classiﬁer. Although our idea is general and can be built on various existing pre-trained models, we have implemented APPT based on the BERT model. We conduct an extensive experiment on 1,183 Defects4J patches and the experimental results show that APPT achieves prediction accuracy of 79.0% and recall of 81.3%, outperforming the state-of-the-art technique CACHE by 3.6% and 4.8%. Our additional investigation on 49,694 real-world patches shows that APPT achieves the optimum performance (exceeding 99% in ﬁve common metrics for assessing patch classiﬁcation techniques) compared with existing representation learning techniques. We also prove that adopting advanced pre-trained models can further provide substantial advancement (e.g., GraphCodeBERT-based APPT improves BERT-based APPT by 3.0% and 2.6% in precision and recall, respectively), highlighting the generalizability
Supergeo Design: Generalized Matching for Geographic Experiments

We propose a generalization of the standard matched pairs design in which experimental units (often geographic regions or geos) may be combined into larger units/regions called"supergeos"in order to improve the average matching quality. Unlike optimal matched pairs design which can be found in polynomial time (Lu et al. 2011), this generalized matching problem is NP-hard. We formulate it as a mixed-integer program (MIP) and show that experimental design obtained by solving this MIP can often provide a significant improvement over the standard design regardless of whether the treatment effects are homogeneous or heterogeneous. Furthermore, we present the conditions under which trimming techniques that often improve performance in the case of homogeneous effects (Chen and Au, 2022), may lead to biased estimates and show that the proposed design does not introduce such bias. We use empirical studies based on real-world advertising data to illustrate these findings.
Sample Efficient Deep Reinforcement Learning via Local Planning

The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environment to a previously observed state. We propose an algorithmic framework, named uncertainty-first local planning (UFLP), that takes advantage of this property. Concretely, in each data collection iteration, with some probability, our meta-algorithm resets the environment to an observed state which has high uncertainty, instead of sampling according to the initial-state distribution. The agent-environment interaction then proceeds as in the standard online RL setting. We demonstrate that this simple procedure can dramatically improve the sample cost of several baseline RL algorithms on difficult exploration tasks. Notably, with our framework, we can achieve super-human performance on the notoriously hard Atari game, Montezuma's Revenge, with a simple (distributional) double DQN. Our work can be seen as an efficient approximate implementation of an existing algorithm with theoretical guarantees, which offers an interpretation of the positive empirical results.
On the Efficacy of Metrics to Describe Adversarial Attacks

Adversarial defenses are naturally evaluated on their ability to tolerate adversarial attacks. To test defenses, diverse ad- versarial attacks are crafted, that are usually described in terms of their evading capability and the L 0 , L 1 , L 2, and L ∞ norms. We question if the evading capability and L-norms are the most effective information to claim that defenses have been tested against a representative attack set. To this extent, we select image quality metrics from the state of the art and search correlations between image perturbation and detectability. We observe that computing L-norms alone is rarely the preferable solution. We observe a strong correlation between the identified metrics computed on an adversarial image and the output of a detector on such an image, to the extent that they can predict the response of a detector with approxi- mately 0.94 accuracy. Further, we observe that metrics can classify attacks based on similar perturbations and similar detectability. This suggests a possible review of the approach to evaluate detectors, where additional metrics are included to assure that a representative attack dataset is selected.
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Models

We introduce a new test set for visual question answering (VQA) called BinaryVQA to push the limits of VQA models. Our dataset includes 7,800 questions across 1,024 images and covers a wide variety of objects, topics, and concepts. For easy model evaluation, we only consider binary questions. Questions and answers are formulated and veriﬁed carefully and manually. Around 63% of the questions have positive answers. The median number of questions per image and question length are 7 and 5, respectively. The state of the art OFA model achieves 75% accuracy on BinaryVQA dataset, which is signiﬁcantly lower than its performance on the VQA v2 test-dev dataset (94.7%). We also analyze the model behavior along several dimensions including: a) performance over different categories such as text, counting and gaze direction, b) model interpretability, c) the effect of question length on accuracy, d) bias of models towards positive answers and introduction of a new score called the “ShufﬂeAcc”, and e) sensitivity to spelling and grammar errors. Our investigation demonstrates the difﬁculty of our dataset and shows that it can challenge VQA models for next few years. Data and code are publicly available at: DATA and CODE.
Composer's Assistant: An Interactive Transformer for Multi-Track MIDI Infilling

We introduce Composer's Assistant, a system for interactive human-computer composition in the REAPER digital audio workstation. We consider the task of multi-track MIDI infilling when arbitrary track-measures have been deleted from a contiguous slice of measures from a MIDI file, and we train a T5-like model to accomplish this task. Composer's Assistant consists of this model together with scripts that enable interaction with the model in REAPER. We conduct objective and subjective tests of our model. We release our complete system, consisting of source code, pretrained models, and REAPER scripts. Our models were trained only on permissively-licensed MIDI files.
YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment

In this paper we extensively explore the suitability of YOLO architectures to monitor the process ﬂow across a Fischertechnik industry 4.0 application. Speciﬁcally, diﬀerent YOLO architectures in terms of size and complexity design along with diﬀerent prior-shapes assign-ment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with diﬀerent distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the eﬀectiveness of the presented approach evaluated using diﬀerent measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.
Mock-Lie bialgebras and mock-Lie analogue of the classical Yang-Baxter equation

The aim of this paper is to introduce the notion of a mock-Lie bialgebra which is equivalent to a Manin triple of mock-Lie algebras. The study of a special case called coboundary mock-Lie bialgebra leads to the introduction the mock-Lie Yang-Baxter equation on a mock-Lie algebra which is an analogue of the classical Yang-Baxter equation on a Lie algebra. Note that a skew-symmetric solution of mock-Lie Yang-Baxter equation gives a mock-Lie bialgebra. Finally, the notation of $\mathcal O$-operators are studied to construct skew-symmetric solution of mock-Lie Yang-Baxter equation.
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks

Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications. To build trustworthy GNNs, two aspects - fairness and privacy - have emerged as critical considerations. Previous studies have separately examined the fairness and privacy aspects of GNNs, revealing their trade-off with GNN performance. Yet, the interplay between these two aspects remains unexplored. In this paper, we pioneer the exploration of the interaction between the privacy risks of edge leakage and the individual fairness of a GNN. Our theoretical analysis unravels that edge privacy risks unfortunately escalate when the nodes' individual fairness improves. Such an issue hinders the accomplishment of privacy and fairness of GNNs at the same time. To balance fairness and privacy, we carefully introduce fairness-aware loss reweighting based on influence function and privacy-aware graph structure perturbation modules within a fine-tuning mechanism. Experimental results underscore the effectiveness of our approach in achieving GNN fairness with limited performance compromise and controlled privacy risks. This work contributes to the comprehensively developing trustworthy GNNs by simultaneously addressing both fairness and privacy aspects.
Spatiotemporal patterns enhanced by intra- and inter-molecular fluctuations in arrays of allosterically regulated enzymes

Enzymatic reactions often involve slow conformational changes, with reaction cycles that sometimes require milliseconds or seconds to complete. The dynamics are strongly affected by fluctuations at the nanoscale. However, such enzymes often occur in small numbers in a cell; hence, the fluctuations caused by finite numbers of molecules should also be substantial. Because of these factors, the behavior of the system is likely to deviate from that of classical reaction–diffusion equations, in which immediate reaction events are assumed to occur without memory and between a huge number of molecules. In this work, we model each enzyme as a unit represented by a phase variable to investigate the effects of fluctuations in arrays of enzymes. Using an analysis based on partial differential equations and stochastic simulations, we show that fluctuations arising from internal states of enzymes (intramolecular fluctuations) and those arising from the stochastic nature of interactions between molecules (intermolecular fluctuations) have distinctive effects on spatiotemporal patterns; the former generally disturb synchronization at high frequencies, whereas the latter can enhance synchronization. The balance of the two types of fluctuations may determine the spatiotemporal behavior of biochemical processes.
Jiamusi pulsar observations: IV. The core-weak pattern of PSR B0329+54

The bright pulsar PSR B0329+54 was previously known for many years to have two emission modes. Sensitive observations of individual pulses reveal that the central component of pulse profile, which is called core component, is found to be very weakened occasionally for some periods and then recovered. This is the newly identified core-weak mode. Based on our long observations of PSR B0329+54 by the Jiamusi 66-m telescope at 2250 MHz, we report here that the profile components of individual pulses, including these for the core and the leading and trailing peaks, are relatedly varying over some periods even before and after the core-weak mode, forming a regular pattern in the phase-vs-time plot for a train of period-folded pulses. The pattern has a similar structure for the core-weak mode with a time scale of 3 to 14 periods. It starts with an intensity brightening at the trailing phase of the core component, and then the core intensity declines to a very low level, as if the core component is drifting out from the normal radiation window within one or two periods. Then the intensity for the trailing components is enhanced, and then the leading component appears at an advanced phase. Such a core-weak mode lasts for several periods. Finally, the core-weak mode ends up with an enhanced intensity at the leading phase for the core component, as if the core gradually comes back and finally stays at the phase of the profile center as it used to.
Semantic Tagging with LSTM-CRF

In the present paper, two models are presented namely LSTM-CRF and BERT-LSTM-CRF for semantic tagging of universal semantic tag dataset. The experiments show that the first model is much easier to converge while the second model that leverages BERT embedding, takes a long time to converge and needs a big dataset for semtagging to be effective.
Recovering Dantzig–Wolfe Bounds by Cutting Planes

Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been extensively applied to solve large-scale mixed-integer programs with decomposable structures, leading to exact solution approaches, such as branch and price. However, these approaches would require solving the problem in an extended variable space and are not readily present in off-the-shelf solvers. In “Recovering Dantzig–Wolfe Bounds by Cutting Planes,” Chen, Günlük, and Lodi propose a computational effective approach for generating cutting planes from Dantzig–Wolfe decomposition to enhance branch and cut in the space of original variables. The proposed approach requires a relatively small number of cutting planes to recover the strength of the Dantzig–Wolfe dual bound and should be easy to implement in general-purpose mixed-integer programming solvers. The authors show that these cutting planes typically lead to a formulation with lower dual degeneracy and hence, a better computational performance than naïve approaches, such as the objective function cut.
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?

Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems. However, the success of these techniques depends on finding proper decompositions that accurately represent the black-box. While previous works learn those decompositions based on data, we investigate data-independent decomposition sampling rules in this paper. We find that data-driven learners of decompositions can be easily misled towards local decompositions that do not hold globally across the search space. Then, we formally show that a random tree-based decomposition sampler exhibits favourable theoretical guarantees that effectively trade off maximal information gain and functional mismatch between the actual black-box and its surrogate as provided by the decomposition. Those results motivate the development of the random decomposition upper-confidence bound algorithm (RDUCB) that is straightforward to implement - (almost) plug-and-play - and, surprisingly, yields significant empirical gains compared to the previous state-of-the-art on a comprehensive set of benchmarks. We also confirm the plug-and-play nature of our modelling component by integrating our method with HEBO, showing improved practical gains in the highest dimensional tasks from Bayesmark.
Stochastic Dimension-reduced Second-order Methods for Policy Optimization

In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods. Specifically, we propose a dimension-reduced second-order method (DR-SOPO) which repeatedly solves a projected two-dimensional trust region subproblem. We show that DR-SOPO obtains an $\mathcal{O}(\epsilon^{-3.5})$ complexity for reaching approximate first-order stationary condition and certain subspace second-order stationary condition. In addition, we present an enhanced algorithm (DVR-SOPO) which further improves the complexity to $\mathcal{O}(\epsilon^{-3})$ based on the variance reduction technique. Preliminary experiments show that our proposed algorithms perform favorably compared with stochastic and variance-reduced policy gradient methods.
Subcloud Layer Energetics of Precipitating Convection

Abstract The thermodynamic modification of the subcloud layer in the GATE area is shown to be a function of precipitating convection. A critical rate of 2 mm h−1, based on the Z – R relationship, in conjunction with 4 km × 4 km scale 15 min mean radar maps, distinguishes between evaporation of precipitation in the subcloud layer (no change in moist static energy h) and vertical mass transport associated with penetrative downdrafts (decreases in h) into this layer from near and above cloud base. The spatial extent of the outflow of the active downdrafts is limited to a convective-mesoscale area directly under and as much as 15 km downwind of the precipitation causing the change. A more extensive wake region occurs on the upwind side of the precipitating region. The initial thermodynamic environment directly affects energy transport per unit mass by moist convection. Precipitating cells which operate upon an initially undisturbed atmosphere cause a net transfer of 60% more energy per unit mass than those co.
MRAC with Memory for Switched Linear Systems

This work proposes a switched model reference adaptive control (S-MRAC) architecture for a multi-input multi-output (MIMO) switched linear system with memory for enhanced learning. A salient feature of the proposed method that separates it from most previous results is the use of memory that store the estimator states at switching and facilitate parameter learning during both active and inactive phases of a subsystem, thereby improving the tracking performance of the overall switched system. Specifically, the learning experience from the previous active duration of a subsystem is retained in the memory and reused when the subsystem is inactive and when the subsystem becomes active again. Parameter convergence is shown based on an intermittent initial excitation (IIE), which is significantly relaxed than the classical persistence of excitation (PE) condition. A common Lyapunov function is considered to ensure closed-loop stability with S-MRAC. Further under IIE, the exponential stability of tracking and parameter estimation error dynamics are guaranteed.
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm

We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown characteristics, real-time optimization becomes challenging, particularly because of the satisfaction of safety constraints. We propose the ARTEO algorithm, where we cast multi-armed bandits as a mathematical programming problem subject to safety constraints and learn the unknown characteristics through exploration while optimizing the targets. We quantify the uncertainty in unknown characteristics by using Gaussian processes and incorporate it into the cost function as a contribution which drives exploration. We adaptively control the size of this contribution in accordance with the requirements of the environment. We guarantee the safety of our algorithm with a high probability through conﬁdence bounds constructed under the regularity assumptions of Gaussian processes. We demonstrate the safety and efﬁciency of our approach with two case studies: optimization of electric motor current and real-time bidding problems. We further evaluate the performance of ARTEO compared to a safe variant of upper conﬁdence bound based algorithms. ARTEO achieves less cumulative regret with accurate and safe decisions.