Miracle-dz/UniFAR-FTUs huggingface dataset:
Taming Diffusion Models for Music-driven Conducting Motion Generation

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model. The code is released at https://github.com/viiika/Diffusion-Conductor.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives -- including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially because GPT-4 follows (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github. io/ ; our dataset can be previewed at https://huggingface.co/datasets/AI-Secure/DecodingTrust ; a concise version of this work is at https://openreview.net/pdf? id=kaHpo8OZw2 .
Dense Molecular Environments of B[e] Supergiants and Yellow Hypergiants

Massive stars expel large amounts of mass during their late evolutionary phases. We aim to unveil the physical conditions within the warm molecular environments of B[e] supergiants (B[e]SGs) and yellow hypergiants (YHGs), which are known to be embedded in circumstellar shells and disks. We present K-band spectra of two B[e]SGs from the Large Magellanic Cloud and four Galactic YHGs. The CO band emission detected from the B[e]SGs LHA 120-S 12 and LHA 120-S 134 suggests that these stars are surrounded by stable rotating molecular rings. The spectra of the YHGs display a rather diverse appearance. The objects 6 Cas and V509 Cas lack any molecular features. The star [FMR2006] 15 displays blue-shifted CO bands in emission, which might be explained by a possible close to pole-on oriented bipolar outflow. In contrast, HD 179821 shows blue-shifted CO bands in absorption. While the star itself is too hot to form molecules in its outer atmosphere, we propose that it might have experienced a recent outburst. We speculate that we currently can only see the approaching part of the expelled matter because the star itself might still block the receding parts of a (possibly) expanding gas shell.
IF2Net: Innately Forgetting-Free Networks for Continual Learning

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to design an Innately Forgetting-Free Network (IF2Net) for continual learning context. This study proposed a straightforward yet effective learning paradigm by ingeniously keeping the weights relative to each seen task untouched before and after learning a new task. We first presented the novel representation-level learning on task sequences with random weights. This technique refers to tweaking the drifted representations caused by randomization back to their separate task-optimal working states, but the involved weights are frozen and reused (opposite to well-known layer-wise updates of weights). Then, sequential decision-making without forgetting can be achieved by projecting the output weight updates into the parsimonious orthogonal space, making the adaptations not disturb old knowledge while maintaining model plasticity. IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time by integrating the respective strengths of randomization and orthogonalization. We validated the effectiveness of our approach in the extensive theoretical analysis and empirical study.
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift

We introduce a hyperbolic neural network approach to pixel-level active learning for semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the hyperbolic radius as an indicator of data scarcity. In HALO (Hyperbolic Active Learning Optimization), for the first time, we propose the use of epistemic uncertainty as a data acquisition strategy, following the intuition of selecting data points that are the least known. The hyperbolic radius, complemented by the widely-adopted prediction entropy, effectively approximates epistemic uncertainty. We perform extensive experimental analysis based on two established synthetic-to-real benchmarks, i.e. GTAV $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes. Additionally, we test HALO on Cityscape $\rightarrow$ ACDC for domain adaptation under adverse weather conditions, and we benchmark both convolutional and attention-based backbones. HALO sets a new state-of-the-art in active learning for semantic segmentation under domain shift and it is the first active learning approach that surpasses the performance of supervised domain adaptation while using only a small portion of labels (i.e., 1\%).
Aberration-driven tilted emission in degenerate cavities

The compensation of chromatic dispersion opened new avenues and extended the level of control upon pattern formation in the \textit{temporal domain}. In this manuscript, we propose the use of a nearly-degenerate laser cavity as a general framework allowing for the exploration of higher contributions to diffraction in the \textit{spatial} domain. Our approach leverages the interplay between optical aberrations and the proximity to the self-imaging condition which allows to cancel or reverse paraxial diffraction. As an example, we show how spherical aberrations materialize into a transverse bilaplacian operator and, thereby, explain the stabilization of temporal solitons travelling off-axis in an unstable mode-locked broad-area surface-emitting laser. We disclose an analogy between these regimes and the dynamics of a quantum particle in a double well potential.
A Bayesian Take on Gaussian Process Networks

Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network. The model allows the description of continuous joint distributions in a compact but flexible manner with minimal parametric assumptions on the dependencies between variables. Bayesian structure learning of GPNs requires computing the posterior over graphs of the network and is computationally infeasible even in low dimensions. This work implements Monte Carlo and Markov Chain Monte Carlo methods to sample from the posterior distribution of network structures. As such, the approach follows the Bayesian paradigm, comparing models via their marginal likelihood and computing the posterior probability of the GPN features. Simulation studies show that our method outperforms state-of-the-art algorithms in recovering the graphical structure of the network and provides an accurate approximation of its posterior distribution.
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning

While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge - one of the most challenging RL tasks in the Atari2600 game suite. We propose Int-HRL: Hierarchical RL with intention-based sub-goals that are inferred from human eye gaze. Our novel sub-goal extraction pipeline is fully automatic and replaces the need for manual sub-goal annotation by human experts. Our evaluations show that replacing hand-crafted sub-goals with automatically extracted intentions leads to a HRL agent that is significantly more sample efficient than previous methods.
SALSA VERDE: a machine learning attack on Learning With Errors with sparse small secrets

Learning with Errors (LWE) is a hard math problem used in post-quantum cryptography. Homomorphic Encryption (HE) schemes rely on the hardness of the LWE problem for their security, and two LWE-based cryptosystems were recently standardized by NIST for digital signatures and key exchange (KEM). Thus, it is critical to continue assessing the security of LWE and specific parameter choices. For example, HE uses secrets with small entries, and the HE community has considered standardizing small sparse secrets to improve efficiency and functionality. However, prior work, SALSA and PICANTE, showed that ML attacks can recover sparse binary secrets. Building on these, we propose VERDE, an improved ML attack that can recover sparse binary, ternary, and narrow Gaussian secrets. Using improved preprocessing and secret recovery techniques, VERDE can attack LWE with larger dimensions ($n=512$) and smaller moduli ($\log_2 q=12$ for $n=256$), using less time and power. We propose novel architectures for scaling. Finally, we develop a theory that explains the success of ML LWE attacks.
Magnetic Activity Evolution of Solar-like Stars. I. S ph–Age Relation Derived from Kepler Observations

The ages of solar-like stars have been at the center of many studies such as exoplanet characterization or Galactic-archeology. While ages are usually computed from stellar evolution models, relations linking ages to other stellar properties, such as rotation and magnetic activity, have been investigated. With the large catalog of 55,232 rotation periods, P rot, and photometric magnetic activity index, S ph from Kepler data, we have the opportunity to look for such magneto-gyro-chronology relations. Stellar ages are obtained with two stellar evolution codes that include treatment of angular momentum evolution, hence using P rot as input in addition to classical atmospheric parameters. We explore two different ways of predicting stellar ages on three subsamples with spectroscopic observations: solar analogs, late-F and G dwarfs, and K dwarfs. We first perform a Bayesian analysis to derive relations between S ph and ages between 1 and 5 Gyr, and other stellar properties. For late-F and G dwarfs, and K dwarfs, the multivariate regression favors the model with P rot and S ph with median differences of 0.1% and 0.2%, respectively. We also apply Machine Learning techniques with a Random Forest algorithm to predict ages up to 14 Gyr with the same set of input parameters. For late-F, G and K dwarfs together, predicted ages are on average within 5.3% of the model ages and improve to 3.1% when including P rot. These are very promising results for a quick age estimation for solar-like stars with photometric observations, especially with current and future space missions.
Practical demonstration of a six-telescope integrated optics beam combiner for the astronomical J- and H-band manufactured with ultrafast laser inscription

We have built and characterized a six-telescope near-infrared discrete beam combiner (DBC) for stellar interferometry using the technique of ultrafast laser inscription (ULI). The 3D beam combiner consists of evanescently coupled waveguides fabricated in borosilicate glass, with a throughput of around 56%. Devices of two design types are characterized over the astronomical J and H band. Using the 15 non-redundant combinations of pairs, we populate the elements of the visibility-to-pixel matrix (V2PM) of the beam combiner using a two-input Michelson interferometer setup. We identify the complex visibility as wavelength dependent, with different optimum wavelengths for the two types of devices. For the design that includes a fan-in region, a baseline-averaged mean visibility amplitude of 1.05 and relative precision of 2.9% and 3.8% are extracted for characterization at 1328 nm and 1380 nm, respectively. Operation is also possible in the H-band, with a relative precision of 4.8% at 1520 nm. Broadband characterization is subject to dispersion effects, but gives similar performance results to their monochromatic counterparts in the J-band at 1350 nm.
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap

Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical studies have demonstrated that the performance of Warm-Start RL can be improved \textit{quickly} in some cases but become \textit{stagnant} in other cases, especially when the function approximation is used. To this end, the primary objective of this work is to build a fundamental understanding on ``\textit{whether and when online learning can be significantly accelerated by a warm-start policy from offline RL? }''. Specifically, we consider the widely used Actor-Critic (A-C) method with a prior policy. We first quantify the approximation errors in the Actor update and the Critic update, respectively. Next, we cast the Warm-Start A-C algorithm as Newton's method with perturbation, and study the impact of the approximation errors on the finite-time learning performance with inaccurate Actor/Critic updates. Under some general technical conditions, we derive the upper bounds, which shed light on achieving the desired finite-learning performance in the Warm-Start A-C algorithm. In particular, our findings reveal that it is essential to reduce the algorithm bias in online learning. We also obtain lower bounds on the sub-optimality gap of the Warm-Start A-C algorithm to quantify the impact of the bias and error propagation.
Acceleration in Policy Optimization

We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. Leveraging the connection between policy iteration and policy gradient methods, we view policy optimization algorithms as iteratively solving a sequence of surrogate objectives, local lower bounds on the original objective. We define optimism as predictive modelling of the future behavior of a policy, and adaptivity as taking immediate and anticipatory corrective actions to mitigate accumulating errors from overshooting predictions or delayed responses to change. We use this shared lens to jointly express other well-known algorithms, including model-based policy improvement based on forward search, and optimistic meta-learning algorithms. We analyze properties of this formulation, and show connections to other accelerated optimization algorithms. Then, we design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents

Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To solve this reproducibility crisis, we propose a theoretically sound methodology to compare the overall performance of multiple algorithms with stochastic returns. We exemplify our methodology in Deep RL. Indeed, the performance of one execution of a Deep RL algorithm is random. Therefore, several independent executions are needed to accurately evaluate the overall performance. When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound. When comparing several algorithms at once, the error of each comparison may accumulate and must be taken into account with a multiple tests procedure to preserve low error guarantees. We introduce AdaStop, a new statistical test based on multiple group sequential tests. When comparing algorithms, AdaStop adapts the number of executions to stop as early as possible while ensuring that we have enough information to distinguish algorithms that perform better than the others in a statistical significant way. We prove theoretically and empirically that AdaStop has a low probability of making a (family-wise) error. Finally, we illustrate the effectiveness of AdaStop in multiple Deep RL use-cases, including toy examples and challenging Mujoco environments. AdaStop is the first statistical test fitted to this sort of comparisons: AdaStop is both a significant contribution to statistics, and a major contribution to computational studies performed in reinforcement learning and in other domains. To summarize our contribution, we introduce AdaStop, a formally grounded statistical tool to let anyone answer the practical question: ``Is my algorithm the new state-of-the-art? ''.
A Simple and Effective Pruning Approach for Large Language Models

As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance. Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight reconstruction problem reliant on second-order information, which may also be computationally expensive. In this paper, we introduce a novel, straightforward yet effective pruning method, termed Wanda (Pruning by Weights and activations), designed to induce sparsity in pretrained LLMs. Motivated by the recent observation of emergent large magnitude features in LLMs, our approach prunes weights with the smallest magnitudes multiplied by the corresponding input activations, on a per-output basis. Notably, Wanda requires no retraining or weight update, and the pruned LLM can be used as is. We conduct a thorough evaluation of our method Wanda on LLaMA and LLaMA-2 across various language benchmarks. Wanda significantly outperforms the established baseline of magnitude pruning and performs competitively against recent method involving intensive weight update. Code is available at https://github.com/locuslab/wanda.
An intuitionistically complete system of basic intuitionistic conditional logic

We introduce a basic intuitionistic conditional logic $\mathsf{IntCK}$ that we show to be complete both relative to a special type of Kripke models and relative to a standard translation into first-order intuitionistic logic. We show that $\mathsf{IntCK}$ stands in a very natural relation to other similar logics, like the basic classical conditional logic $\mathsf{CK}$ and the basic intuitionistic modal logic $\mathsf{IK}$. As for the basic intuitionistic conditional logic $\mathsf{ICK}$ proposed by Y. Weiss, $\mathsf{IntCK}$ extends its language with a diamond-like conditional modality, but its diamond-conditional-free fragment is also a proper extension of $\mathsf{ICK}$. We briefly discuss the resulting gap between the two candidate systems of basic intuitionistic conditional logic and the possible pros and cons of both candidates.
Bloated Disclosures: Can ChatGPT Help Investors Process Financial Information?

Generative AI tools such as ChatGPT can fundamentally change the way investors process information. We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory. The unconstrained summaries are dramatically shorter, often by more than 70% compared to the originals, whereas their information content is amplified. When a document has a positive (negative) sentiment, its summary becomes more positive (negative). More importantly, the summaries are more effective at explaining stock market reactions to the disclosed information. Motivated by these findings, we propose a measure of information “bloat. ” We show that bloated disclosure is associated with adverse capital markets consequences, such as lower price efficiency and higher information asymmetry. Finally, we show that the model is effective at constructing targeted summaries that identify firms’ (non-)financial performance and risks. Collectively, our results indicate that generative language modeling adds considerable value for investors with information processing constraints.
Unsupervised Open-domain Keyphrase Generation

In this work, we study the problem of unsupervised open-domain keyphrase generation, where the objective is a keyphrase generation model that can be built without using human-labeled data and can perform consistently across domains. To solve this problem, we propose a seq2seq model that consists of two modules, namely phraseness and informativeness module, both of which can be built in an unsupervised and open-domain fashion. The phraseness module generates phrases, while the informativeness module guides the generation towards those that represent the core concepts of the text. We thoroughly evaluate our proposed method using eight benchmark datasets from different domains. Results on in-domain datasets show that our approach achieves state-of-the-art results compared with existing unsupervised models, and overall narrows the gap between supervised and unsupervised methods down to about 16%. Furthermore, we demonstrate that our model performs consistently across domains, as it surpasses the baselines on out-of-domain datasets.