Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacokinetics and Pharmacokinetic/Pharmacodynamic Properties of Rozibafusp Alfa, a Bispecific Inhibitor of BAFF and ICOSL: Analyses of Phase I Clinical Trials

Rozibafusp alfa (AMG 570) is a first‐in‐class bispecific IgG2‐peptide fusion designed to inhibit inducible T‐cell costimulator ligand (ICOSL) and B‐cell activating factor (BAFF). The pharmacokinetics (PK) and pharmacodynamics (PD) of rozibafusp alfa were investigated in two randomized, placebo‐controlled clinical studies: a phase Ia single ascending‐dose study (7–700 mg subcutaneously (s.c.)) in healthy subjects and a phase Ib multiple ascending‐dose study (70–420 mg s.c. every 2 weeks (q2w)) in patients with rheumatoid arthritis. Rozibafusp alfa exhibited nonlinear PK and dose‐related and reversible dual‐target engagement. Maximal reduction of naïve B cells from baseline (> 40%), reflective of BAFF inhibition, was achieved with rozibafusp alfa exposure (area under the concentration‐time curve from time 0 to time infinity (AUCinf) and AUC within a dosing interval from day 0 to day 14 (AUCtau)) above 51 and 57 days•μg/mL for the single‐dose (≥ 70 mg) and multiple‐dose studies (≥ 70 mg q2w), respectively. ICOSL receptor occupancy on circulating B cells, a surrogate PD end point for ICOSL inhibition, was directly related to drug concentration. PK/PD analysis showed > 90% RO at rozibafusp alfa ≥ 22.2 μg/mL (≥ 420‐mg single dose or ≥ 210 mg q2w multiple dose), with saturation occurring at higher drug concentrations. These results informed the design and dose selection of a phase IIb study assessing the safety and efficacy of rozibafusp alfa in patients with active systemic lupus erythematosus.
Incompleteness Theorems for Observables in General Relativity.

The quest for complete observables in general relativity has been a long-standing open problem. We employ methods from descriptive set theory to show that no complete observable on rich enough collections of spacetimes is Borel definable. In fact, we show that it is consistent with the Zermelo-Fraenkel and dependent choice axioms that no complete observable for rich collections of spacetimes exists whatsoever. In a nutshell, this implies that the problem of observables is to "analysis" what the Delian problem was to "straightedge and compass. " Our results remain true even after restricting the space of solutions to vacuum solutions. In other words, the issue can be traced to the presence of local degrees of freedom. We discuss the next steps in a research program that aims to further uncover this novel connection between theoretical physics and descriptive set theory.
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Point cloud data has been extensively studied due to its compact form and flexibility in representing complex 3D structures. The ability of point cloud data to accurately capture and represent intricate 3D geometry makes it an ideal choice for a wide range of applications, including computer vision, robotics, and autonomous driving, all of which require an understanding of the underlying spatial structures. Given the challenges associated with annotating large-scale point clouds, self-supervised point cloud representation learning has attracted increasing attention in recent years. This approach aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotations. In this paper, we present a comprehensive survey of self-supervised point cloud representation learning using DNNs. We begin by presenting the motivation and general trends in recent research. We then briefly introduce the commonly used datasets and evaluation metrics. Following that, we delve into an extensive exploration of self-supervised point cloud representation learning methods based on these techniques. Finally, we share our thoughts on some of the challenges and potential issues that future research in self-supervised learning for pre-training 3D point clouds may encounter.
A thermal model of the sailback pelycosaur

Standard techniques of energy exchange analysis are applied to modelling the thermal regimes of several species of sailback pelycosaurs. Of particular interest is the role played by the sail in thermoregulation. Although the sail did increase the rate at which the model could warm up in the morning, its effectiveness fell short of previous estimates. The sail increased the daytime internal temperature by typically 3°C to 6°C. The more massive subjects had the highest temperature increases owing partly to their higher thermal inertia and partly to their disproportionately large sails. The sail had no impact upon the nocturnal minimums in temperature. Overheating does not appear to have been a significant problem in the Permian environment, particularly in view of the high thermal inertia of the subjects modeled. When overheating occurred, the sail was of limited value for dumping excess heat.
On High-dimensional and Low-rank Tensor Bandits

Most existing studies on linear bandits focus on a one-dimensional characterization of the overall system. While being representative, this formulation may fail to model applications with high-dimensional but favorable structures, such as the low-rank tensor representation for recommender systems. To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank. A novel bandit algorithm, coined TOFU (Tensor Optimism in the Face of Uncertainty), is developed. TOFU first leverages flexible tensor regression techniques to estimate low-dimensional subspaces associated with the system tensor. These estimates are then utilized to convert the original problem to a new one with norm constraints on its system parameters. Lastly, a norm-constrained bandit subroutine is adopted by TOFU, which utilizes these constraints to avoid exploring the entire high-dimensional parameter space. Theoretical analyses show that TOFU improves the best-known regret upper bound by a multiplicative factor that grows exponentially in the system order. A novel performance lower bound is also established, which further corroborates the efficiency of TOFU.
Debugging Flaky Tests using Spectrum-based Fault Localization

Non-deterministically behaving (i.e., flaky) tests hamper regression testing as they destroy trust and waste computational and human resources. Eradicating flakiness in test suites is therefore an important goal, but automated debugging tools are needed to support developers when trying to understand the causes of flakiness. A popular example for an automated approach to support regular debugging is spectrum-based fault localization (SFL), a technique that identifies software components that are most likely the causes of failures. While it is possible to also apply SFL for locating likely sources of flakiness in code, unfortunately the flakiness makes SFL both imprecise and non-deterministic. In this paper we introduce SFFL (Spectrum-based Flaky Fault Localization), an extension of traditional coverage-based SFL that exploits our observation that 80% of flaky tests exhibit varying coverage behavior between different runs. By distinguishing between stable and flaky coverage, SFFL is able to locate the sources of flakiness more precisely and keeps the localization itself deterministic. An evaluation on 101 flaky tests taken from 48 open-source Python projects demonstrates that SFFL is effective: Of five prominent SFL formulas, DStar, Ochiai, and Op2 yield the best overall performance. On average, they are able to narrow down the fault’s location to 3.5% of the project’s code base, which is 18.7% better than traditional SFL (for DStar). SFFL’s effectiveness, however, depends on the root causes of flakiness: The source of non-order-dependent flaky tests can be located far more precisely than order-dependent faults.
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning

Policy optimization methods with function approximation are widely used in multi-agent reinforcement learning. However, it remains elusive how to design such algorithms with statistical guarantees. Leveraging a multi-agent performance difference lemma that characterizes the landscape of multi-agent policy optimization, we find that the localized action value function serves as an ideal descent direction for each local policy. Motivated by the observation, we present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO. We prove that with standard regularity conditions on the Markov game and problem-dependent quantities, our algorithm converges to the globally optimal policy at a sublinear rate. We extend our algorithm to the off-policy setting and introduce pessimism to policy evaluation, which aligns with experiments. To our knowledge, this is the first provably convergent multi-agent PPO algorithm in cooperative Markov games.
The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation

In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a promising avenue. However, current methods often assume full observability of the scene and struggle with scale invariance. In many tasks and settings, this assumption does not hold as objects in the scene are often occluded or lie outside the field of view of the camera, rendering the camera observation ambiguous with regard to their location. To tackle this problem, we present BASK, a Bayesian approach to tracking scale-invariant keypoints over time. Our approach successfully resolves inherent ambiguities in images, enabling keypoint tracking on symmetrical objects and occluded and out-of-view objects. We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques. Furthermore, we show outstanding robustness towards disturbances such as clutter, occlusions, and noisy depth measurements, as well as generalization to unseen objects both in simulation and real-world robotic experiments.
A Nonparametric Mixed-Effects Mixture Model for Patterns of Clinical Measurements Associated with COVID-19 (preprint)

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.
Improving Cross-Task Generalization with Step-by-Step Instructions

Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target tasks following the instructions, as the instructions are general and lack intermediate steps. To address this problem, we propose to incorporate the step-by-step instructions to help language models to decompose the tasks, which can provide the detailed and specific procedures for completing the target tasks. The step-by-step instructions are obtained automatically by prompting ChatGPT, which are further combined with the original instructions to tune language models. The extensive experiments on SUP-NATINST show that the high-quality step-by-step instructions can improve cross-task generalization across different model sizes. Moreover, the further analysis indicates the importance of the order of steps of the step-by-step instruction for the improvement. To facilitate future research, we release the step-by-step instructions and their human quality evaluation results.
Towards indicating interdisciplinarity: Characterizing interdisciplinary knowledge flow

This study contributes to the recent discussions on indicating interdisciplinarity, that is, going beyond catch‐all metrics of interdisciplinarity. We propose a contextual framework to improve the granularity and usability of the existing methodology for interdisciplinary knowledge flow (IKF) in which scientific disciplines import and export knowledge from/to other disciplines. To characterize the knowledge exchange between disciplines, we recognize three aspects of IKF under this framework, namely broadness, intensity, and homogeneity. We show how to utilize them to uncover different forms of interdisciplinarity, especially between disciplines with the largest volume of IKF. We apply this framework in two use cases, one at the level of disciplines and one at the level of journals, to show how it can offer a more holistic and detailed viewpoint on the interdisciplinarity of scientific entities than aggregated and context‐unaware indicators. We further compare our proposed framework, an indicating process, with established indicators and discuss how such information tools on interdisciplinarity can assist science policy practices such as performance‐based research funding systems and panel‐based peer review processes.
Multi-tracer power spectra and bispectra: formalism

The power spectrum and bispectrum of dark matter tracers are key and complementary probes of the Universe. Next-generation surveys will deliver good measurements of the bispectrum, opening the door to improved cosmological constraints and the breaking of parameter degeneracies, from the combination of the power spectrum and bispectrum. Multi-tracer power spectra have been used to suppress cosmic variance and mitigate the effects of nuisance parameters and systematics. We present a bispectrum multi-tracer formalism that can be applied to next-generation survey data. Then we perform a simple Fisher analysis to illustrate qualitatively the improved precision on primordial non-Gaussianity that is expected to come from the bispectrum multi-tracer. In addition, we investigate the parametric dependence of conditional errors from multi-tracer power spectra and multi-tracer bispectra, on the differences between the biases and the number densities of two tracers. Our results suggest that optimal constraints arise from maximising the ratio of number densities, the difference between the linear biases, the difference between the quadratic biases, and the difference between the products b 1 b Φ for each tracer, where b Φ is the bias for the primordial potential.
Adaptive loose optimization for robust question answering

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalizability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in \url{https://github.com/reml-group/ALO}.
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization

Policy learning (PL) is a module of a task-oriented dialogue system that trains an agent to make actions in each dialogue turn. Imitating human action is a fundamental problem of PL. However, both supervised learning (SL) and reinforcement learning (RL) frameworks cannot imitate humans well. Training RL models require online interactions with user simulators, while simulating complex human policy is hard. Performances of SL-based models are restricted because of the covariate shift problem. Specifically, a dialogue is a sequential decision-making process where slight differences in current utterances and actions will cause significant differences in subsequent utterances. Therefore, the generalize ability of SL models is restricted because statistical characteristics of training and testing dialogue data gradually become different. This study proposed an offline imitation learning model that learns policy from real dialogue datasets and does not require user simulators. It also utilizes state transition information, which alleviates the influence of the covariate shift problem. We introduced a regularization trick to make our model can be effectively optimized. We investigated the performance of our model on four independent public dialogue datasets. The experimental result showed that our model performed better in the action prediction task.
Gradient Leakage Defense with Key-Lock Module for Federated Learning

Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local model gradients between local clients and third-party parameter servers. However, recent findings reveal that privacy may be compromised and sensitive information potentially recovered from shared gradients. In this study, we offer detailed analysis and a novel perspective on understanding the gradient leakage problem. These theoretical works lead to a new gradient leakage defense technique that secures arbitrary model architectures using a private key-lock module. Only the locked gradient is transmitted to the parameter server for global model aggregation. Our proposed learning method is resistant to gradient leakage attacks, and the key-lock module is designed and trained to ensure that, without the private information of the key-lock module: a) reconstructing private training data from the shared gradient is infeasible; and b) the global model's inference performance is significantly compromised. We discuss the theoretical underpinnings of why gradients can leak private information and provide theoretical proof of our method's effectiveness. We conducted extensive empirical evaluations with a total of forty-four models on several popular benchmarks, demonstrating the robustness of our proposed approach in both maintaining model performance and defending against gradient leakage attacks.
The impact of mountain wakes on the drag exerted on downstream mountains

The pressure drag exerted on two identical high idealised mountain ridges aligned perpendicular to the mean flow, with the second mountain lying directly downstream of the first, is investigated using a numerical model. The drag is measured for a variety of different distances between the two peaks, mountain heights and lengths and incident flows. Each two‐mountain simulation has a companion single mountain simulation, with an identical mountain and identical upstream flow, with which to compare the drag. The maximum modelled drag in the two‐mountain simulations is close to two times the drag in the single mountain case, while the minimum two‐mountain drag is similar to that exerted on the single mountain. This change in drag is a result of the wake behind the first mountain reducing the speed of the flow impacting on the second mountain. A parametrization of this effect is suggested which combines the distance between the two mountain peaks, a measure of the velocity deficit in the wake of the first mountain and the wake decay‐scale, a measure of the length‐scale over which the flow perturbation generated by the first mountain decays downwind. Copyright © 2008 Royal Meteorological Society and Crown Copyright 2008, published by John Wiley & Sons, Ltd.
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising

Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning. Secondly, learning-based algorithms predominantly rely on CNN and fail to capture long-range dependencies, resulting in limited interpretability. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues. Firstly, DNA-Net models sparse noise, Gaussian noise, and explicitly represent image prior using transformer. Then the model is unfolded into an end-to-end network, the hyperparameters within the model are estimated from the noisy HSI and degradation model and utilizes them to control each iteration. Additionally, we introduce a novel U-Shaped Local-Non-local-Spectral Transformer (U-LNSA) that captures spectral correlation, local contents, and non-local dependencies simultaneously. By integrating U-LNSA into DNA-Net, we present the first Transformer-based deep unfolding HSI denoising method. Experimental results show that DNA-Net outperforms state-of-the-art methods, and the modeling of noise distributions helps in cases with heavy noise.
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other. The need of protecting such federated models from being plagiarized or misused, therefore, motivates us to propose a provable secure model ownership verification scheme using zero-knowledge proof, named FedZKP. It is shown that the FedZKP scheme without disclosing credentials is guaranteed to defeat a variety of existing and potential attacks. Both theoretical analysis and empirical studies demonstrate the security of FedZKP in the sense that the probability for attackers to breach the proposed FedZKP is negligible. Moreover, extensive experimental results confirm the fidelity and robustness of our scheme.