Miracle-dz/UniFAR-FTUs huggingface dataset:
The MillenniumTNG Project: The large-scale clustering of galaxies

Modern redshift surveys are tasked with mapping out the galaxy distribution over enormous distance scales. Existing hydrodynamical simulations, however, do not reach the volumes needed to match upcoming surveys. We present results for the clustering of galaxies using a new, large volume hydrodynamical simulation as part of the MillenniumTNG (MTNG) project. With a computational volume that is $\approx15$ times larger than the next largest such simulation currently available, we show that MTNG is able to accurately reproduce the observed clustering of galaxies as a function of stellar mass. When separated by colour, there are some discrepancies with respect to the observed population, which can be attributed to the quenching of satellite galaxies in our model. We combine MTNG galaxies with those generated using a semi-analytic model to emulate the sample selection of luminous red galaxies (LRGs) and emission line galaxies (ELGs), and show that although the bias of these populations is approximately (but not exactly) constant on scales larger than $\approx10$ Mpc, there is significant scale-dependent bias on smaller scales. The amplitude of this effect varies between the two galaxy types, and also between the semi-analytic model and MTNG. We show that this is related to the distribution of haloes hosting LRGs and ELGs. Using mock SDSS-like catalogues generated on MTNG lightcones, we demonstrate the existence of prominent baryonic acoustic features in the large-scale galaxy clustering. We also demonstrate the presence of realistic redshift space distortions in our mocks, finding excellent agreement with the multipoles of the redshift-space clustering measured in SDSS data.
On Bisimulation in Absence of Restriction

We revisit the standard bisimulation equalities in process models free of the restriction operator. As is well-known, in general the weak bisimilarity is coarser than the strong bisimilarity because it abstracts from internal actions. In absence of restriction, those internal actions become somewhat visible, so one might wonder if the weak bisimilarity is still ‘weak’. We show that in both CCScore (i.e., Milner’s standard CCS without τ -preﬁx, summation and relabelling) and its higher-order variant (named HOCCScore), the weak bisimilarity indeed remains weak, i.e., still strictly coarser than the strong bisimilarity, even without the restriction operator. These results can be extended to other ﬁrst-order or higher-order process models. Essentially, this is due to the direct or indirect existence of the replication operation, which can keep a process retaining its state (i.e., capacity of interaction). By virtue of these observations, we examine a variant of the weak bisimilarity, called quasi-strong bisimilarity. This quasi-strong bisimilarity requires the matching of internal actions to be conducted in the strong manner, as for the strong bisimilarity, and the matching of visible actions to have no trailing internal actions. We exhibit that in CCScore without the restriction operator, the weak bisimilarity exactly collapses onto this quasi-strong bisimilarity, which is moreover shown to coincide with the branching bisimilarity. These results reveal that in absence of the restriction operation, some ingredient of the weak bisimilarity indeed turns into strong, particularly the matching of internal actions.
GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing

A lack of large-scale human-annotated data has hampered the hierarchical discourse parsing of Chinese. In this paper, we present GCDT, the largest hierarchical discourse treebank for Mandarin Chinese in the framework of Rhetorical Structure Theory (RST). GCDT covers over 60K tokens across five genres of freely available text, using the same relation inventory as contemporary RST treebanks for English. We also report on this dataset’s parsing experiments, including state-of-the-art (SOTA) scores for Chinese RST parsing and RST parsing on the English GUM dataset, using cross-lingual training in Chinese and English with multilingual embeddings.
Efficient Delivery of Robotics Programming Educational Content Using Cloud Robotics

In this paper, we report on our use of cloud-robotics solutions to teach a Robotics Applications Programming course at Zurich University of Applied Sciences (ZHAW). The usage of Kubernetes based cloud computing environment combined with real robots - turtlebots and Niryo arms - allowed us to: 1) minimize the set up times required to provide a Robotic Operating System (ROS) simulation and development environment to all students independently of their laptop architecture and OS; 2) provide a seamless “simulation to real” experience preserving the exciting experience of writing software interacting with the physical world; and 3) sharing GPUs across multiple student groups, thus using resources efficiently. We describe our requirements, solution design, experience working with the solution in the educational context and areas where it can be further improved. This may be of interest to other educators who may want to replicate our experience.
Aging Channel Modeling and Transmission Block Size Optimization for Massive MIMO Vehicular Networks in Non-Isotropic Scattering Environment

We investigate the effect of channel aging on multi-cell massive multiple-input multiple-output (MIMO) vehicular networks in a generic non-isotropic scattering environment. Based on the single cluster scattering assumption and the von Mises distribution assumptions of the scatterers' angles, an aging channel model is established to capture the joint effect of spatial and temporal correlations resulting from different angular spread conditions in various application scenarios. Expressions of the user uplink transmission spectral efficiency (SE) are derived for maximum ratio (MR) and minimum mean square error (MMSE) combining. Through numerical studies, the area spectral efficiency (ASE) performance of the network is evaluated in freeway and urban Manhattan road grid scenarios, and easy-to-use empirical models for the optimal transmission block size for ASE maximization are obtained for the evaluated scenarios.
Explainable bilevel optimization: an application to the Helsinki deblur challenge

In this paper we present a bilevel optimization scheme for the solution of a general image deblurring problem, in which a parametric variational-like approach is encapsulated within a machine learning scheme to provide a high quality reconstructed image with automatically learned parameters. The ingredients of the variational lower level and the machine learning upper one are specifically chosen for the Helsinki Deblur Challenge 2021, in which sequences of letters are asked to be recovered from out-of-focus photographs with increasing levels of blur. Our proposed procedure for the reconstructed image consists in a fixed number of FISTA iterations applied to the minimization of an edge preserving and binarization enforcing regularized least-squares functional. The parameters defining the variational model and the optimization steps, which, unlike most deep learning approaches, all have a precise and interpretable meaning, are learned via either a similarity index or a support vector machine strategy. Numerical experiments on the test images provided by the challenge authors show significant gains with respect to a standard variational approach and performances comparable with those of some of the proposed deep learning based algorithms which require the optimization of millions of parameters.
Model Predictive Vehicle Yaw Stability Control via Integrated Active Front Wheel Steering and Individual Braking

Vehicle stability control systems are important components of active safety systems for road transport. The problem of vehicle lateral stability control is addressed in this paper using active front wheel steering and individual braking. Vehicle lateral stability control means keeping the vehicle yaw rate and the vehicle side slip angle in desired values. For this reason, a model - based controller is designed. The desired yaw rate is obtained from the single track vehicle model and the desired side slip angle is chosen as zero. Controller design consists of two parts, lower and upper controller parts. Upper controller is designed based on Model Predictive Control (MPC) method. This upper controller changes front wheel steering angles utilizing steer - by - wire system and also it generates the required control moment for stabilizing the yaw motion of the vehicle. Lower controller is an individual braking algorithm. It determines the individual braking wheel. In this way, the control moment can be applied to the vehicle. The designed controller is tested using the nonlinear single track vehicle model and the higher fidelity CarMaker vehicle model.
Transferable Unlearnable Examples

With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users' data before publishing, which aims to make the models trained on the perturbed published dataset invalidated. These perturbations have been generated for a specific training setting and a target dataset. However, their unlearnable effects significantly decrease when used in other training settings and datasets. To tackle this issue, we propose a novel unlearnable strategy based on Classwise Separability Discriminant (CSD), which aims to better transfer the unlearnable effects to other training settings and datasets by enhancing the linear separability. Extensive experiments demonstrate the transferability of the proposed unlearnable examples across training settings and datasets.
Extreme Mass Loss in Low-mass Type Ib/c Supernova Progenitors

Many core-collapse supernovae (SNe) with hydrogen-poor and low-mass ejecta, such as ultra-stripped SNe and type Ibn SNe, are observed to interact with dense circumstellar material (CSM). These events likely arise from the core collapse of helium stars that have been heavily stripped by a binary companion and have ejected significant mass during the last weeks to years of their lives. In helium star models run to days before core collapse we identify a range of helium core masses ≈2.5–3 M ⊙ whose envelopes expand substantially due to the helium shell burning while the core undergoes neon and oxygen burning. When modeled in binary systems, the rapid expansion of these helium stars induces extremely high rates of late-stage mass transfer ( Ṁ≳10−2M⊙yr−1 ) beginning weeks to decades before core collapse. We consider two scenarios for producing CSM in these systems: either mass transfer remains stable and mass loss is driven from the system in the vicinity of the accreting companion, or mass transfer becomes unstable and causes a common envelope event (CEE) through which the helium envelope is unbound. The ensuing CSM properties are consistent with the CSM masses (∼10−2–1 M ⊙) and radii (∼1013–1016 cm) inferred for ultra-stripped SNe and several type Ibn SNe. Furthermore, systems that undergo a CEE could produce short-period neutron star binaries that merge in less than 100 Myr.
Modeling of spin decoherence in a Si hole qubit perturbed by a single charge fluctuator

Spin qubits in semiconductor quantum dots are one of the promizing devices to realize a quantum processor. A better knowledge of the noise sources affecting the coherence of such a qubit is therefore of prime importance. In this work, we study the effect of telegraphic noise induced by the fluctuation of a single electric charge. We simulate as realistically as possible a hole spin qubit in a quantum dot defined electrostatically by a set of gates along a silicon nanowire channel. Calculations combining Poisson and time-dependent Schr\"odinger equations allow to simulate the relaxation and the dephasing of the hole spin as a function of time for a classical random telegraph signal. We show that dephasing time $T_2$ is well given by a two-level model in a wide range of frequency. Remarkably, in the most realistic configuration of a low frequency fluctuator, the system has a non-Gaussian behavior in which the phase coherence is lost as soon as the fluctuator has changed state. The Gaussian description becomes valid only beyond a threshold frequency $\omega_{th}$, when the two-level system reacts to the statistical distribution of the fluctuator states. We show that the dephasing time $T_{2}(\omega_{th})$ at this threshold frequency can be considerably increased by playing on the orientation of the magnetic field and the gate potentials, by running the qubit along"sweet"lines. However, $T_{2}(\omega_{th})$ remains bounded due to dephasing induced by the non-diagonal terms of the stochastic perturbation Hamiltonian. Our simulations reveal that the spin relaxation cannot be described cleanly in the two-level model because the coupling to higher energy hole levels impacts very strongly the spin decoherence. This result suggests that multi-level simulations including the coupling to phonons should be necessary to describe the relaxation phenomenon in this type of qubit.
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning

Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algorithms. However, commonly employed IL methods are often data-inefficient, requiring the collection of a large number of demonstrations and producing policies with limited robustness to uncertainties. In this work, we combine IL with an output feedback robust tube model predictive controller (RTMPC) to co-generate demonstrations and a data augmentation strategy to efficiently learn neural network-based sensorimotor policies. Thanks to the augmented data, we reduce the computation time and the number of demonstrations needed by IL, while providing robustness to sensing and process uncertainty. We tailor our approach to the task of learning a trajectory tracking visuomotor policy for an aerial robot, leveraging a 3D mesh of the environment as part of the data augmentation process. We numerically demonstrate that our method can learn a robust visuomotor policy from a single demonstration—a two-orders of magnitude improvement in demonstration efficiency compared to existing IL methods.
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation

Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph. However, the linearisation is known to ignore the structural information. Additionally, PLMs are typically pre-trained on free text which introduces domain mismatch between pre-training and downstream G2T generation tasks. To address these shortcomings, we propose graph masking pre-training strategies that neither require supervision signals nor adjust the architecture of the underlying pre-trained encoder-decoder model. When used with a pre-trained T5, our approach achieves new state-of-the-art results on WebNLG+2020 and EventNarrative G2T generation datasets. Our method also shows to be very effective in the low-resource setting.
A Robust Pedestrian Detection Approach for Autonomous Vehicles

Nowadays, utilizing Advanced Driver-Assistance Systems (ADAS) has absorbed a huge interest as a potential solution for reducing road traffic issues. Despite recent technological advances in such systems, there are still many inquiries that need to be overcome. For instance, ADAS requires accurate and real-time detection of pedestrians in various driving scenarios. To solve the mentioned problem, this paper aims to finetune the YOLOv5s model for handling pedestrian detection challenges on the real-world instances of Caltech pedestrian dataset. We also introduce a developed toolbox for preparing training and test data and annotations of Caltech pedestrian dataset into the format recognizable by YOLOv5. Experimental results on the Caltech pedestrian dataset have verified that the mean Average Precision (mAP) of our fine-tuned model for pedestrian detection task is equal to 91 percent when performing at the highest rate of 70 FPS. Moreover, our proposed approach can outperform other existing methodologies in terms of accuracy and speed.
Non-Iterative Optimization of Pseudo-Labeling Thresholds for Training Object Detection Models from Multiple Datasets

We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a subset of all the object classes. A popular approach to this problem is first to train teacher models and then to use their confident predictions as pseudo ground-truth labels when training a student model. To obtain the best result, however, thresholds for prediction confidence must be adjusted. This process typically involves iterative search and repeated training of student models and is time-consuming. Therefore, we develop a method to optimize the thresholds without iterative optimization by maximizing the Fβ-score on a validation dataset, which measures the quality of pseudo labels and can be measured without training a student model. We experimentally demonstrate that our proposed method achieves an mAP comparable to that of grid search on the COCO and VOC datasets.
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mixes all these roles, obscuring the problem definitions and research goals of the security role that aims to reveal the practical concerns of NLP models. In this paper, we rethink the research paradigm of textual adversarial samples in security scenarios. We discuss the deficiencies in previous work and propose our suggestions that the research on the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their methods on security tasks to demonstrate the real-world concerns; (2) consider real-world attackers’ goals, instead of developing impractical methods. To this end, we first collect, process, and release a security datasets collection Advbench. Then, we reformalize the task and adjust the emphasis on different goals in SoadNLP. Next, we propose a simple method based on heuristic rules that can easily fulfill the actual adversarial goals to simulate real-world attack methods. We conduct experiments on both the attack and the defense sides on Advbench. Experimental results show that our method has higher practical value, indicating that the research paradigm in SoadNLP may start from our new benchmark. All the code and data of Advbench can be obtained at https://github.com/thunlp/Advbench.
Convexity Certificates from Hessians

The Hessian of a differentiable convex function is positive semidefinite. Therefore, checking the Hessian of a given function is a natural approach to certify convexity. However, implementing this approach is not straightforward since it requires a representation of the Hessian that allows its analysis. Here, we implement this approach for a class of functions that is rich enough to support classical machine learning. For this class of functions, it was recently shown how to compute computational graphs of their Hessians. We show how to check these graphs for positive semidefiniteness. We compare our implementation of the Hessian approach with the well-established disciplined convex programming (DCP) approach and prove that the Hessian approach is at least as powerful as the DCP approach for differentiable functions. Furthermore, we show for a state-of-the-art implementation of the DCP approach that, for differentiable functions, the Hessian approach is actually more powerful. That is, it can certify the convexity of a larger class of differentiable functions.
Optimal investment and reinsurance under exponential forward preferences

We study the optimal investment and proportional reinsurance problem of an insurance company, whose investment preferences are described via a forward dynamic utility of exponential type in a stochastic factor model allowing for a possible dependence between the ﬁnancial and insurance markets. Speciﬁcally, we assume that the asset price process dynamics and the claim arrival intensity are both aﬀected by a common stochastic process and we account for a possible environmental contagion eﬀect through the non-zero correlation parameter between the underlying Brownian motions driving the asset price process and the stochastic factor dynamics. By stochastic control techniques, we construct a forward dynamic exponential utility, and we characterize the optimal investment and reinsurance strategy. Moreover, we investigate in detail the zero-volatility case and provide a comparison analysis with classical results in an analogous setting under backward utility preferences. We also discuss an extension of the conditional certainty equivalent. Finally, we perform a numerical analysis to highlight some features of the optimal strategy.
Causal Structure Learning with Recommendation System

A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However, there are numerous phenomenons where domain knowledge is insufficient, and the causal mechanisms must be learnt from the feedback data. Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact. Also for this reason, most existing solutions become inappropriate since they require data collected free from any RS. In this paper, we first formulate the underlying causal mechanism as a causal structural model and describe a general causal structure learning framework grounded in the real-world working mechanism of RS. The essence of our approach is to acknowledge the unknown nature of RS intervention. We then derive the learning objective from our framework and propose an augmented Lagrangian solver for efficient optimization. We conduct both simulation and real-world experiments to demonstrate how our approach compares favorably to existing solutions, together with the empirical analysis from sensitivity and ablation studies.
Adversarial De-confounding in Individualised Treatment Effects Estimation

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.