Miracle-dz/UniFAR-FTUs huggingface dataset:
Aluminum Josephson junction microstructure and electrical properties modification with thermal annealing

Superconducting qubits based on Al/AlOx/Al Josephson junction are one of the most promising candidates for the physical implementation of universal quantum computers. Due to scalability and compatibility with the state-of-the-art nanoelectronic processes one can fabricate hundreds of qubits on a single silicon chip. However, decoherence in these systems caused by two-level-systems in amorphous dielectrics, including a tunneling barrier AlOx, is one of the major problems. We report on a Josephson junction thermal annealing process development to crystallize an amorphous barrier oxide (AlOx). The dependences of the thermal annealing parameters on the room temperature resistance are obtained. The developed method allows not only to increase the Josephson junction resistance by 175%, but also to decrease by 60% with precisions of 10% in Rn. Finally, theoretical assumptions about the structure modification in tunnel barrier are proposed. The suggested thermal annealing approach can be used to form a stable and reproducible tunneling barriers and scalable frequency trimming for a widely used fixed-frequency transmon qubits.
SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos

We study the problem of procedure planning in instructional videos, which aims to make a goal-oriented sequence of action steps given partial visual state observations. The motivation of this problem is to learn a structured and plannable state and action space. Recent works succeeded in sequence modeling of steps with only sequence-level annotations accessible during training, which overlooked the roles of states in the procedures. In this work, we point out that State CHangEs MAtter (SCHEMA) for procedure planning in instructional videos. We aim to establish a more structured state space by investigating the causal relations between steps and states in procedures. Specifically, we explicitly represent each step as state changes and track the state changes in procedures. For step representation, we leveraged the commonsense knowledge in large language models (LLMs) to describe the state changes of steps via our designed chain-of-thought prompting. For state change tracking, we align visual state observations with language state descriptions via cross-modal contrastive learning, and explicitly model the intermediate states of the procedure using LLM-generated state descriptions. Experiments on CrossTask, COIN, and NIV benchmark datasets demonstrate that our proposed SCHEMA model achieves state-of-the-art performance and obtains explainable visualizations.
Optimizing Illuminant Estimation in Dual-Exposure HDR Imaging

High dynamic range (HDR) imaging involves capturing a series of frames of the same scene, each with different exposure settings, to broaden the dynamic range of light. This can be achieved through burst capturing or using staggered HDR sensors that capture long and short exposures simultaneously in the camera image signal processor (ISP). Within camera ISP pipeline, illuminant estimation is a crucial step aiming to estimate the color of the global illuminant in the scene. This estimation is used in camera ISP white-balance module to remove undesirable color cast in the final image. Despite the multiple frames captured in the HDR pipeline, conventional illuminant estimation methods often rely only on a single frame of the scene. In this paper, we explore leveraging information from frames captured with different exposure times. Specifically, we introduce a simple feature extracted from dual-exposure images to guide illuminant estimators, referred to as the dual-exposure feature (DEF). To validate the efficiency of DEF, we employed two illuminant estimators using the proposed DEF: 1) a multilayer perceptron network (MLP), referred to as exposure-based MLP (EMLP), and 2) a modified version of the convolutional color constancy (CCC) to integrate our DEF, that we call ECCC. Both EMLP and ECCC achieve promising results, in some cases surpassing prior methods that require hundreds of thousands or millions of parameters, with only a few hundred parameters for EMLP and a few thousand parameters for ECCC.
Demonstrating a Robust Walking Algorithm for Underactuated Bipedal Robots in Non-flat, Non-stationary Environments

This work explores an innovative algorithm designed to enhance the mobility of underactuated bipedal robots across challenging terrains, especially when navigating through spaces with constrained opportunities for foot support, like steps or stairs. By combining ankle torque with a refined angular momentum-based linear inverted pendulum model (ALIP), our method allows variability in the robot's center of mass height. We employ a dual-strategy controller that merges virtual constraints for precise motion regulation across essential degrees of freedom with an ALIP-centric model predictive control (MPC) framework, aimed at enforcing gait stability. The effectiveness of our feedback design is demonstrated through its application on the Cassie bipedal robot, which features 20 degrees of freedom. Key to our implementation is the development of tailored nominal trajectories and an optimized MPC that reduces the execution time to under 500 microseconds--and, hence, is compatible with Cassie's controller update frequency. This paper not only showcases the successful hardware deployment but also demonstrates a new capability, a bipedal robot using a moving walkway.
Collective Certified Robustness against Graph Injection Attacks

We investigate certified robustness for GNNs under graph injection attacks. Existing research only provides sample-wise certificates by verifying each node independently, leading to very limited certifying performance. In this paper, we present the first collective certificate, which certifies a set of target nodes simultaneously. To achieve it, we formulate the problem as a binary integer quadratic constrained linear programming (BQCLP). We further develop a customized linearization technique that allows us to relax the BQCLP into linear programming (LP) that can be efficiently solved. Through comprehensive experiments, we demonstrate that our collective certification scheme significantly improves certification performance with minimal computational overhead. For instance, by solving the LP within 1 minute on the Citeseer dataset, we achieve a significant increase in the certified ratio from 0.0% to 81.2% when the injected node number is 5% of the graph size. Our step marks a crucial step towards making provable defense more practical.
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling

In science and engineering, we often work with models designed for accurate prediction of variables of interest. Recognizing that these models are approximations of reality, it becomes desirable to apply multiple models to the same data and integrate their outcomes. In this paper, we operate within the Bayesian paradigm, relying on Gaussian processes as our models. These models generate predictive probability density functions (pdfs), and the objective is to integrate them systematically, employing both linear and log-linear pooling. We introduce novel approaches for log-linear pooling, determining input-dependent weights for the predictive pdfs of the Gaussian processes. The aggregation of the pdfs is realized through Monte Carlo sampling, drawing samples of weights from their posterior. The performance of these methods, as well as those based on linear pooling, is demonstrated using a synthetic dataset.
DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization

Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain task changes to directly adapt the pre-trained source model to arbitrary target domains equipped with prior domain knowledge, and we name this task Adaptive Domain Generalization (ADG). However, current cross-domain datasets have many limitations, such as unrealistic domains, unclear domain definitions, and the inability to fine-grained domain decomposition, which drives us to establish a novel dataset DomainVerse for ADG. Benefiting from the introduced hierarchical definition of domain shifts, DomainVerse consists of about 0.5 million images from 390 fine-grained realistic domains. With the help of the constructed DomainVerse and VLMs, we propose two methods called Domain CLIP and Domain++ CLIP for tuning-free adaptive domain generalization. Extensive and comprehensive experiments demonstrate the significance of the dataset and the effectiveness of the proposed methods.
Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis

Sentiment analysis (SA) has been, and is still, a thriving research area. However, the task of Arabic sentiment analysis (ASA) is still underrepresented in the body of research. This study offers the first in-depth and in-breadth analysis of existing ASA studies of textual content and identifies their common themes, domains of application, methods, approaches, technologies and algorithms used. The in-depth study manually analyses 133 ASA papers published in the English language between 2002 and 2020 from four academic databases (SAGE, IEEE, Springer, WILEY) and from Google Scholar. The in-breadth study uses modern, automatic machine learning techniques, such as topic modelling and temporal analysis, on Open Access resources, to reinforce themes and trends identified by the prior study, on 2297 ASA publications between 2010-2020. The main findings show the different approaches used for ASA: machine learning, lexicon-based and hybrid approaches. Other findings include ASA 'winning' algorithms (SVM, NB, hybrid methods). Deep learning methods, such as LSTM can provide higher accuracy, but for ASA sometimes the corpora are not large enough to support them. Additionally, whilst there are some ASA corpora and lexicons, more are required. Specifically, Arabic tweets corpora and datasets are currently only moderately sized. Moreover, Arabic lexicons that have high coverage contain only Modern Standard Arabic (MSA) words, and those with Arabic dialects are quite small. Thus, new corpora need to be created. On the other hand, ASA tools are stringently lacking. There is a need to develop ASA tools that can be used in industry, as well as in academia, for Arabic text SA. Hence, our study offers insights into the challenges associated with ASA research and provides suggestions for ways to move the field forward such as lack of Dialectical Arabic resource, Arabic tweets, corpora and data sets for SA.
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction

In Multiple Object Tracking, objects often exhibit non-linear motion of acceleration and deceleration, with irregular direction changes. Tacking-by-detection (TBD) trackers with Kalman Filter motion prediction work well in pedestrian-dominant scenarios but fall short in complex situations when multiple objects perform non-linear and diverse motion simultaneously. To tackle the complex non-linear motion, we propose a real-time diffusion-based MOT approach named DiffMOT. Specifically, for the motion predictor component, we propose a novel Decoupled Diffusion-based Motion Predictor (D$^2$MP). It models the entire distribution of various motion presented by the data as a whole. It also predicts an individual object's motion conditioning on an individual's historical motion information. Furthermore, it optimizes the diffusion process with much fewer sampling steps. As a MOT tracker, the DiffMOT is real-time at 22.7FPS, and also outperforms the state-of-the-art on DanceTrack and SportsMOT datasets with $62.3\%$ and $76.2\%$ in HOTA metrics, respectively. To the best of our knowledge, DiffMOT is the first to introduce a diffusion probabilistic model into the MOT to tackle non-linear motion prediction.
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects

This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient's recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, how to trade-off between them to achieve optimal treatment remains an open challenge. Moreover, when multiple objectives are directly estimated using conventional causal representation learning, the optimization directions among various tasks can conflict as well. In this paper, we systematically investigate these issues and introduce a Pareto-Efficient algorithm, comprising Pareto-Optimal Estimation (POE) and Pareto-Optimal Policy Learning (POPL), to tackle them. POE incorporates a continuous Pareto module with representation balancing, enhancing estimation efficiency across multiple tasks. As for POPL, it involves deriving short-term and long-term outcomes linked with various treatment levels, facilitating an exploration of the Pareto frontier emanating from these outcomes. Results on both the synthetic and real-world datasets demonstrate the superiority of our method.
Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions

Hill function is one of the widely used gene transcription regulation models. Its attribute of fitting may result in a lack of an underlying physical picture, yet the fitting parameters can provide information about biochemical reactions, such as the number of transcription factors (TFs) and the binding energy between regulatory elements. However, it remains unclear when and how much biochemical information can Hill function provide in addition to fitting. Here, started from the interactions between TFs and RNA polymerase during transcription regulation and both of their association-dissociation reactions at specific/nonspecific sites on DNA, the regulatory effect of TFs was deduced as fold change. We found that, for weak promoter, fold change can degrade into the regulatory factor (Freg) which is closely correlated with Hill function. By directly comparing and fitting with Hill function, the fitting parameters and corresponding biochemical reaction parameters in Freg were analyzed and discussed, where the single TF and multiple TFs that with cooperativity and basic logic effects were considered. We concluded the strength of promoter and interactions between TFs determine whether Hill function can reflect the corresponding biochemical information. Our findings highlight the role of Hill function in modeling/fitting for transcriptional regulation, which also benefits the preparation of synthetic regulatory elements.
Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control

Navigating robots safely and efficiently in crowded and complex environments remains a significant challenge. However, due to the dynamic and intricate nature of these settings, planning efficient and collision-free paths for robots to track is particularly difficult. In this paper, we uniquely bridge the robot's perception, decision-making and control processes by utilizing the convex obstacle-free region computed from 2D LiDAR data. The overall pipeline is threefold: (1) We proposes a robot navigation framework that utilizes deep reinforcement learning (DRL), conceptualizing the observation as the convex obstacle-free region, a departure from general reliance on raw sensor inputs. (2) We design the action space, derived from the intersection of the robot's kinematic limits and the convex region, to enable efficient sampling of inherently collision-free reference points. These actions assists in guiding the robot to move towards the goal and interact with other obstacles during navigation. (3) We employ model predictive control (MPC) to track the trajectory formed by the reference points while satisfying constraints imposed by the convex obstacle-free region and the robot's kinodynamic limits. The effectiveness of proposed improvements has been validated through two sets of ablation studies and a comparative experiment against the Timed Elastic Band (TEB), demonstrating improved navigation performance in crowded and complex environments.
LC-Tsalis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits

This study considers the linear contextual bandit problem with independent and identically distributed (i.i.d.) contexts. In this problem, existing studies have proposed Best-of-Both-Worlds (BoBW) algorithms whose regrets satisfy $O(\log^2(T))$ for the number of rounds $T$ in a stochastic regime with a suboptimality gap lower-bounded by a positive constant, while satisfying $O(\sqrt{T})$ in an adversarial regime. However, the dependency on $T$ has room for improvement, and the suboptimality-gap assumption can be relaxed. For this issue, this study proposes an algorithm whose regret satisfies $O(\log(T))$ in the setting when the suboptimality gap is lower-bounded. Furthermore, we introduce a margin condition, a milder assumption on the suboptimality gap. That condition characterizes the problem difficulty linked to the suboptimality gap using a parameter $\beta \in (0, \infty]$. We then show that the algorithm's regret satisfies $O\left(\left\{\log(T)\right\}^{\frac{1+\beta}{2+\beta}}T^{\frac{1}{2+\beta}}\right)$. Here, $\beta= \infty$ corresponds to the case in the existing studies where a lower bound exists in the suboptimality gap, and our regret satisfies $O(\log(T))$ in that case. Our proposed algorithm is based on the Follow-The-Regularized-Leader with the Tsallis entropy and referred to as the $\alpha$-Linear-Contextual (LC)-Tsallis-INF.
PAVITS: Exploring Prosody-Aware VITS for End-to-End Emotional Voice Conversion

In this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotional naturalness, which are crucial for meeting the demands of human perception. To improve the content naturalness of converted audio, we have developed an end-to-end EVC architecture inspired by the high audio quality of VITS. By seamlessly integrating an acoustic converter and vocoder, we effectively address the common issue of mismatch between emotional prosody training and run-time conversion that is prevalent in existing EVC models. To further enhance the emotional naturalness, we introduce an emotion descriptor to model the subtle prosody variations of different speech emotions. Additionally, we propose a prosody predictor, which predicts prosody features from text based on the provided emotion label. Notably, we introduce a prosody alignment loss to establish a connection between latent prosody features from two distinct modalities, ensuring effective training. Experimental results show that the performance of PAVITS is superior to the state-of-the-art EVC methods. Speech Samples are available at https://jeremychee4.github. io/pavits4EVC/ .
Deep Incremental Model Based Reinforcement Learning: A One-Step Lookback Approach for Continuous Robotics Control

Model-based reinforcement learning (MBRL) attempts to use an available or a learned model to improve the data efficiency of reinforcement learning. This work proposes a one-step lookback approach that jointly learns the latent-space model and the policy to realize the sample-efficient continuous robotic control, wherein the control-theoretical knowledge is utilized to decrease the model learning difficulty. Specifically, the so-called one-step backward data is utilized to facilitate the incremental evolution model, an alternative structured representation of the robotics evolution model in the MBRL field. The incremental evolution model accurately predicts the robotics movement but with low sample complexity. This is because the formulated incremental evolution model degrades the model learning difficulty into a parametric matrix learning problem, which is especially favourable to high-dimensional robotics applications. The imagined data from the learned incremental evolution model is used to supplement training data to enhance the sample efficiency. Comparative numerical simulations on benchmark continuous robotics control problems are conducted to validate the efficiency of our proposed one-step lookback approach.
Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds

We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor. To cope with the diversity of shapes and inhomogeneous and incomplete object coverage, we introduce a generative model that directly predicts 3D polygonal meshes from input point clouds. Our autoregressive model, called Point2Building, iteratively builds up the mesh by generating sequences of vertices and faces. This approach enables our model to adapt flexibly to diverse geometries and building structures. Unlike many existing methods that rely heavily on pre-processing steps like exhaustive plane detection, our model learns directly from the point cloud data, thereby reducing error propagation and increasing the fidelity of the reconstruction. We experimentally validate our method on a collection of airborne LiDAR data of Zurich, Berlin and Tallinn. Our method shows good generalization to diverse urban styles.
Constructing monads from cubical diagrams and homotopy colimits

This paper is the first step in a general program for defining cocalculus towers of functors via sequences of compatible monads. Goodwillie's calculus of homotopy functors inspired many new functor calculi in a wide range of contexts in algebra, homotopy theory and geometric topology. Recently, the third and fourth authors have developed a general program for constructing generalized calculi from sequences of compatible comonads. In this paper, we dualize the first step of the Hess-Johnson program, focusing on monads rather than comonads. We consider categories equipped with an action of the poset category $\mathcal{P}(n)$, called $\mathcal{P}(n)$-modules. We exhibit a functor from $\mathcal{P}(n)$-modules to the category of monads. The resulting monads act on categories of functors whose codomain is equipped with a suitable notion of homotopy colimits. In the final section of the paper, we demonstrate the monads used to construct McCarthy's dual calculus as an example of a monad arising from a $\mathcal{P}(n)$-module. This confirms that our dualization of the Hess-Johnson program generalizes McCarthy's dual calculus, and serves as a proof of concept for further development of this program.