Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adversarial Training: A Game Perspective

The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a game theoretical perspective . Deﬁning optimal solutions in domain-adversarial training as local Nash equilibria, we show that gradient descent in domain-adversarial training can violate the asymptotic convergence guarantees of the optimizer, oftentimes hindering the transfer performance. Our analysis leads us to replace gradient descent with high-order ODE solvers (i.e., Runge–Kutta), for which we derive asymptotic convergence guarantees. This family of optimizers is signiﬁcantly more stable and allows more aggressive learning rates, leading to high performance gains when used as a drop-in replacement over standard optimizers. Our experiments show that in conjunction with state-of-the-art domain-adversarial methods, we achieve up to 3.5% improvement with less than half of training iterations. Our optimizers are easy to implement, free of additional parameters, and can be plugged into any domain-adversarial framework.
I-Tuning: Tuning Language Models with Image for Caption Generation

Recently, tuning the pre-trained language model (PLM) in a parameter-efﬁcient manner becomes a popular topic in the natural language processing area. However, most of them focus on tuning the PLM with the text-only information. In this work, we propose a new perspective to tune the frozen PLM with images for caption generation. We denote our method as I-Tuning , which can automatically ﬁlter the vision information from images to adjust the output hidden states of PLM. Evaluating on the image captioning tasks (MSCOCO and Flickr30k Captioning), our method achieves comparable or even better performance than the previous models which have 2-4 times more trainable parameters and/or consume a large amount of cross-modal pre-training data.
BViT: Broad Attention based Vision Transformer

Recent works have demonstrated that transformer can achieve promising performance in computer vision, by exploiting the relationship among image patches with self-attention. They only consider the attention in a single feature layer, but ignore the complementarity of attention in different layers. In this article, we propose broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer (ViT), which is called BViT. The broad attention is implemented by broad connection and parameter-free attention. Broad connection of each transformer layer promotes the transmission and integration of information for BViT. Without introducing additional trainable parameters, parameter-free attention jointly focuses on the already available attention information in different layers for extracting useful information and building their relationship. Experiments on image classification tasks demonstrate that BViT delivers superior accuracy of 75.0%/81.6% top-1 accuracy on ImageNet with 5M/22M parameters. Moreover, we transfer BViT to downstream object recognition benchmarks to achieve 98.9% and 89.9% on CIFAR10 and CIFAR100, respectively, that exceed ViT with fewer parameters. For the generalization test, the broad attention in Swin Transformer, T2T-ViT and LVT also brings an improvement of more than 1%. To sum up, broad attention is promising to promote the performance of attention-based models. Code and pretrained models are available at https://github.com/DRL/BViT.
Entroformer: A Transformer-based Entropy Model for Learned Image Compression

One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules. Previous works build entropy models upon convolutional neural networks which are inefficient in capturing global dependencies. In this work, we propose a novel transformer-based entropy model, termed Entroformer, to capture long-range dependencies in probability distribution estimation effectively and efficiently. Different from vision transformers in image classification, the Entroformer is highly optimized for image compression, including a top-k self-attention and a diamond relative position encoding. Meanwhile, we further expand this architecture with a parallel bidirectional context model to speed up the decoding process. The experiments show that the Entroformer achieves state-of-the-art performance on image compression while being time-efficient.
Compute Trends Across Three Eras of Machine Learning

Compute, data, and algorithmic advances are the three fundamental factors that drive progress in modern Machine Learning (ML). In this paper we study trends in the most readily quantified factor - compute. We make three novel contributions: (1) we curate a dataset with the training compute of 123 milestone ML systems, 3× larger than previous such datasets. (2) We frame the trends in compute in in three eras - the Pre Deep Learning Era, the Deep Learning Era, and the Large-Scale Era, based on our identification of a novel trend emerging around 2015. (3) We find a Deep Learning Era compute doubling time of around 6 months, significantly longer than previous findings. Overall, our work highlights the fast-growing compute requirements for training advanced ML systems.
EREBA: Black-box Energy Testing of Adaptive Neural Networks

Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determining the ro-bustness of a DNN with respect to its energy consumption (energy robustness) is relatively unexplored compared to accuracy-based ro-bustness. This work investigates the energy robustness of Adaptive Neural Networks (AdNNs), a type of energy-saving DNNs proposed for many energy-sensitive domains and have recently gained traction. We propose EREBA, the first black-box testing method for determining the energy robustness of an AdNN. EREBA explores and infers the relationship between inputs and the energy con-sumption of AdNN s to generate energy surging samples. Extensive implementation and evaluation using three state-of-the-art AdNNs demonstrate that test inputs generated by EREBA could degrade the performance of the system substantially. The test inputs gener-ated by EREBA can increase the energy consumption of AdNN s by 2,000% compared to the original inputs. Our results also show that test inputs generated via EREBA are valuable in detecting energy surging inputs.
Probabilistic drift capacity models for reinforced concrete columns

The need to ascertain the drift capacity of reinforced concrete columns frequently arises in the displacement-based seismic design and assessment method. Drift capacity at column lateral strength degradation, as well as the column capacity to sustain axial load after the loss of lateral strength, is sought for each column. Such predictions can only be made in a probabilistic sense, due to lack of experimental data, model imperfections and variability in the intervening parameters. The probabilistic models developed in this study incorporate these sources of uncertainty and are capable of estimating the probability of column failure for a given drift demand. The probabilistic models not only provide engineers the prediction of column drift capacities but also help engineers to determine the critical columns and the priority of column retrofit. The prediction of column response is also one of the objectives in this study. Two approaches are proposed to classify columns. First, a probabilistic failure mode index model is developed to identify column failure modes: flexure failure, flexure-shear failure and shear failure. Second, a two-zone column classification method is proposed to approximately distinguish the shear response dominated columns (Zone S) and the flexural response dominated columns (Zone F) based on three column parameters. Two probabilistic drift capacity models at 20% reduction in lateral strength are developed; one for columns classified in Zone S, and another for columns in Zone F. The key parameters affecting the drift capacity at 80% lateral strength for reinforced concrete columns are identified through the assessment of probabilistic drift capacity models. Based on the shear-friction mechanism, a probabilistic drift capacity model at axial failure is developed for shear-damaged columns. Several examples are presented to demonstrate the applications of the probabilistic models. The probabilistic failure mode index model can be employed to assess the probability of each failure mode for a given column. The probabilistic drift capacity models can be used to construct the fragility estimate of reinforced concrete columns with respect to the drift demand. The variability in the fragility estimate due to epistemic
Modelling, measurement and optimization of passive and active custom-made hearing protectors

In industry, hearing protectors are often implemented to prevent noise-induced hearing loss, but the use of protectors at the workfloor might substantially vary from laboratory conditions. Therefore it seems advisable to also verify the attenuation in situ, for instance with the MIRE (microphone in real ear) approach. The first aim of this research project is to investigate the implementation of the MIRE technique for custom-made hearing protectors designed with a test bore. In this channel, a MIRE measurement microphone can be slided to measure the sound pressure in the ear canal. This setup allows quick and stable measurements, but the issue is the difference in sound levels between the microphone and the eardrum. This relationship, henceforth called transfer function, is studied in different ways. First, the transfer functions are registered with a head-and-torso-simulator (HATS) and simulated with numerical FDTD (finite-difference time-domain) models. They appear to be stable, reproducible and depend on clearly distinguishable characteristics of earplug and ear canal. In the second stage, transfer functions are assessed for human subjects in a similar way. They vary substantially with respect to the exact configuration in function of the frequency, but this can be predicted with individualized FDTD simulations. Based on these findings, a filter approach is developed to allow implementation in practice. The second aim is addressing verbal communication with hearing protection. In this regard, new technologies are implemented in so-called augmented protectors so that the applied attenuation is automatically adapted to the background noise in contrast to the classical protectors with constant attenuation over a considerable range of input levels. This project compares speech intelligibility between those augmented and classical earplugs for normal-hearing subjects. The analyses show that certain innovations might indeed be beneficial depending on the type of background noise. Conversely, speech perception can only be ameliorated if distortion is avoided and if the positive effects of binaural listening are retained.
ORBGRAND Is Almost Capacity-Achieving

Decoding via sequentially guessing the error pattern in a received noisy sequence has received attention recently, and ORBGRAND has been proposed as one such decoding algorithm that is capable of utilizing the soft information embedded in the received noisy sequence. An information theoretic study is conducted for ORBGRAND, and it is shown that the achievable rate of ORBGRAND using independent and identically distributed random codebooks almost coincides with the channel capacity, for an additive white Gaussian noise channel under antipodal input. For finite-length codes, improved guessing schemes motivated by the information theoretic study are proposed that attain lower error rates than ORBGRAND, especially in the high signal-to-noise ratio regime.
Moduli of relative stable maps to $\mathbb{P}^1$: cut-and-paste invariants

We study constructible invariants of the moduli space $\overline{\mathcal{M}}(\boldsymbol{x})$ of stable maps from genus zero curves to $\mathbb{P}^1$, relative to $0$ and $\infty$, with ramification profiles specified by ${\boldsymbol{x}\in \mathbb{Z}^n}$. These spaces are central to the enumerative geometry of $\mathbb{P}^1$, and provide a large family of birational models of the Deligne--Mumford--Knudsen moduli space $\overline{\mathcal{M}}_{0,n}$. For the sequence of vectors $\boldsymbol{x}$ corresponding to maps which are maximally ramified over $0$ and unramified over $\infty$, we prove that a generating function for the topological Euler characteristics of these spaces satisfies a differential equation which allows for its recursive calculation. We also show that the class of the moduli space in the Grothendieck ring of varieties is constant as $\boldsymbol{x}$ varies within a fixed chamber in the resonance decomposition of $\mathbb{Z}^n$. We conclude by suggesting several further directions in the study of these spaces, giving conjectures on (1) the asymptotic behavior of the Euler characteristic and (2) a potential chamber structure for the Chern numbers.
Regularized Q-learning

Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm that converges when linear function approximation is used. We prove that simply adding an appropriate regularization term ensures convergence of the algorithm. We prove its stability using a recent analysis tool based on switching system models. Moreover, we experimentally show that it converges in environments where Q-learning with linear function approximation has known to diverge. We also provide an error bound on the solution where the algorithm converges.
Grasp Control of a Cable-Driven Robotic Hand Using a PVDF Slip Detection Sensor

Detecting and preventing slip is a major challenge in robotic hand operation, underpinning the robot's ability to perform safe and reliable grasps. Using the robotic hand design from the authors' earlier work, a sensing and control strategy is proposed here to prevent object slippage. The robotic hand is cable-driven, single-actuated, has five fingers, and is capable of replicating most human hand motions. The slip sensing approach utilizes a piezoelectric vibration sensor, namely, polyvinylidene fluoride (PVDF), which is a flexible, thin, cheap, and highly sensitive material. The power of the filtered PVDF signal is shown to exhibit identifiable signatures during slip, thus providing a suitable slip detection mechanism. Using the PVDF feedback, an integral controller is implemented to prevent the grasped object from falling and ensure a safe, powerful, and reliable grasp. The extension movement of the robotic hand is controlled using a bend sensor, through a proportional-integral (PI) controller. The robotic hand weights 338 gr. The functionality and robustness of the proposed slip-detection sensory system and control logic implementation are evaluated through experiments.
Fairness-aware Configuration of Machine Learning Libraries

This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Data-driven software is increasingly applied in social-critical applications where ensuring fairness is of paramount importance. The existing approaches focus on addressing fairness bugs by either modifying the input dataset or modifying the learning algorithms. On the other hand, the selection of hyperparameters, which provide finer controls of ML algorithms, may enable a less intrusive approach to influence the fairness. Can hyperparameters amplify or suppress discrimination present in the input dataset? How can we help programmers in detecting, understanding, and exploiting the role of hyperparameters to improve the fairness? We design three search-based software testing algorithms to un-cover the precision-fairness frontier of the hyperparameter space. We complement these algorithms with statistical debugging to explain the role of these parameters in improving fairness. We implement the proposed approaches in the tool Parfait-ML (PARameter FAIrness Testing for ML Libraries) and show its effectiveness and utility over five mature ML algorithms as used in six social-critical applications. In these applications, our approach successfully iden-tified hyperparameters that significantly improve (vis-a-vis the state-of-the-art techniques) the fairness without sacrificing precision. Surprisingly, for some algorithms (e.g., random forest), our approach showed that certain configuration of hyperparameters (e.g., restricting the search space of attributes) can amplify biases across applications. Upon further investigation, we found intuitive explanations of these phenomena, and the results corroborate simi-lar observations from the literature.
Interacting 1D Chiral Fermions with Pairing: Transition from Integrable to Chaotic

We study a generic one-dimensonal quantum model of two ﬂavors (pseudospins) chiral complex fermions by exact diagonalization, which can have local interﬂavor interaction and superconducting pairings (with all irrelevant terms ignored). Analytically, the model has two solvable (integrable) points in the parameter space: it is a free fermion model when the fermion interaction is zero, and is a free boson Luttinger liquid when there is a global U(1) ( ↑ ) × U(1) ( ↓ ) symmetry (with nonzero interaction). When the global symmetry of the interacting model is lowered by turning on symmetry breaking parameters, the model undergoes a transition from a quantum integrable model to a fully quantum chaotic model, as we demonstrate by examining the level spacing statistics (LSS) of the many-body energy spectrum. In particular, there is a possibly integrable regime with intermediate global symmetries, where the model is neither free bosons nor free fermions, but shows Poisson LSS in each global symmetry charge sector. This implies the existence of hidden (quasi)local conserved quantities. When the global symmetries are further lowered, the LSS in each charge sector becomes Wigner-Dyson, implying quantum chaos.
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer

We investigate the performance of a state-of-the-art (SoTA) architecture T5 (available on the SuperGLUE) and compare it with 3 other previous SoTA architectures across 5 different tasks from 2 relatively diverse datasets. The datasets are diverse in terms of the number and types of tasks they have. To improve performance, we augment the training data by using a new autoregressive conversational AI model checkpoint. We achieve near-SoTA results on a couple of the tasks - macro F1 scores of 81.66% for task A of the OLID 2019 dataset and 82.54% for task A of the hate speech and offensive content (HASOC) 2021 dataset, where SoTA are 82.9% and 83.05%, respectively. We perform error analysis and explain why one of the models (Bi-LSTM) makes the predictions it does by using a publicly available algorithm: Integrated Gradient (IG). This is because explainable artificial intelligence (XAI) is essential for earning the trust of users. The main contributions of this work are the implementation method of T5, which is discussed; the data augmentation, which brought performance improvements; and the revelation on the shortcomings of the HASOC 2021 dataset. The revelation shows the difficulties of poor data annotation by using a small set of examples where the T5 model made the correct predictions, even when the ground truth of the test set were incorrect (in our opinion). We also provide our model checkpoints on the HuggingFace hub11https://huggingface.co/sana-ngu/HaT5_augmentation https://huggingface.co/sana-ngu/HaT5.
The Open Cluster Chemical Abundances and Mapping Survey. V. Chemical Abundances of CTIO/Hydra Clusters Using The Cannon

Open clusters are key chemical and age tracers of Milky Way evolution. While open clusters provide significant constraints on galaxy evolution, their use has been limited due to discrepancies in measuring abundances from different studies. We analyze medium-resolution (R ∼ 19,000) Cerro Tololo Inter-American Observatory/Hydra spectra of giant stars in 58 open clusters using The Cannon to determine [Fe/H], [Mg/Fe], [Si/Fe], [Al/Fe], and [O/Fe]. This work adds an additional 55 primarily southern hemisphere open clusters calibrated to the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment DR16 metallicity system. This uniform analysis is compared to previous studies [Fe/H] measurements for 23 clusters and we present spectroscopic metallicities for the first time for 35 open clusters.
Finding Dynamics Preserving Adversarial Winning Tickets

Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and adversarial training has been shown to be a promising method for improving the adversarial robustness of DNNs. Pruning methods have been considered in adversarial context to reduce model capacity and improve adversarial robustness simultaneously in training. Existing adversarial pruning methods generally mimic the classical pruning methods for natural training, which follow the three-stage 'training-pruning-fine-tuning' pipelines. We observe that such pruning methods do not necessarily preserve the dynamics of dense networks, making it potentially hard to be fine-tuned to compensate the accuracy degradation in pruning. Based on recent works of \textit{Neural Tangent Kernel} (NTK), we systematically study the dynamics of adversarial training and prove the existence of trainable sparse sub-network at initialization which can be trained to be adversarial robust from scratch. This theoretically verifies the \textit{lottery ticket hypothesis} in adversarial context and we refer such sub-network structure as \textit{Adversarial Winning Ticket} (AWT). We also show empirical evidences that AWT preserves the dynamics of adversarial training and achieve equal performance as dense adversarial training.