Miracle-dz/UniFAR-FTUs huggingface dataset:
Pt‐Rh/TiO2/activated carbon as highly active and stable HI decomposition catalyst for hydrogen production in sulfur‐iodine (SI) process

Pt‐TiO2 loaded on activated carbon was studied as an active and stable catalyst to HI decomposition for H2 formation in the sulfur‐iodine process. Although the activity of TiO2‐loaded catalyst was slightly lower HI conversion than that of CeO2 loaded one, the higher stability against HI decomposition reaction was achieved and almost equilibrium conversion was sustained over ~65 h examined. Moreover, effects of Rh or Ir addition on HI conversion were studied and it was found that Pt‐Rh bimetallic system was highly active and stable to HI decomposition. Scanning transmission electron micrograph observation suggested that the increased HI decomposition activity was assigned to the increased dispersion of Pt particles. High dispersion state of Pt was sustained after HI decomposition at 773 K by addition of Rh. Since the formation of PtI4 was suggested by X‐ray photoelectron spectroscopy measurement during HI decomposition, increased stability by addition of Rh seems to be assigned to the high chemical stability of Rh against iodine. Almost the equilibrium HI conversion on Pt‐Rh‐TiO2/M563 was sustained over 300 hours at 673 K.
Sensitivity Analyses for Atmospheric Scavenging Ratio of Total PCDD/Fs-TEQ Wet Deposition: Case of Wuhu City, China

This study investigated the monthly average dry, wet and total deposition fluxes in total-PCDD/Fs-WHO2005-TEQ in Wuhu and Bengbu, respectively. In addition, sensitivity analyses for both scavenging ratio and wet deposition of total-PCDD/Fs-WHO2005-TEQ in Wuhu were also conducted. In 2015, the annual dry deposition fluxes, wet deposition fluxes, and total deposition fluxes were 6407, 1538, and 7945 pg WHO2005-TEQ m–2 year–1 in Wuhu, and were 7101, 1525, and 8626 pg WHO2005-TEQ m–2 year–1 in Bengbu. The annual scavenging ratios of total-PCDD/Fs-WHO2005-TEQ were 30144 and 31267 in Wuhu and Bengbu, respectively. As to the sensitivity analysis for the scavenging ratio of total-PCDD/Fs-WHO2005-TEQ, the most positive sensitivity parameters were PCDD/Fs concentration and PM10 concentration; the second positively correlated sensitivity parameter was PM2.5 concentration, then, followed by atmospheric temperature, which was negatively correlated; the last negative correlated sensitivity factor was the rainfall. In terms of the sensitivity analysis for wet deposition of total-PCDD/Fs-WHO2005-TEQ, the most positive sensitivity parameter was air temperature; the second positive or negative correlated sensitivity factor was atmospheric PCDD/Fs mass concentration, followed by PM2.5 and PM10 concentration, which were positively correlated; the last positive correlated sensitivity factor was the rainfall. This study led to greater insight into the parameters affecting the atmospheric wet deposition of PCDD/Fs, which will benefit development of appropriate control strategies for PCDD/Fs and provided useful information for the scientific community.
Intrinsic energy flow in laser-excited 
3d
 ferromagnets

Daniela Zahn, ∗ Florian Jakobs, Hélène Seiler, Tim A. Butcher, † Dieter Engel, Jan Vorberger, Unai Atxitia, Yoav William Windsor, 5 and Ralph Ernstorfer 5, ‡ Abteilung Physikalische Chemie, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany Dahlem Center for Complex Quantum Systems and Fachbereich Physik, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany Institute of Radiation Physics, Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany Division B: Transient Electronic Structure and Nanophysics, Max-Born-Institut, Max-Born-Straße 2A, 12489 Berlin, Germany Institut für Optik und Atomare Physik, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
An Under-Actuated Whippletree Mechanism Gripper based on Multi-Objective Design Optimization with Auto-Tuned Weights

Current rigid linkage grippers are limited in flexibility, and gripper design optimality relies on expertise, experiments, or arbitrary parameters. Our proposed rigid gripper can accommodate irregular and off-center objects through a whippletree mechanism, improving adaptability. We present a whippletree-based rigid under-actuated gripper and its parametric design multi-objective optimization for a one-wall climbing task. Our proposed objective function considers kinematics and grasping forces simultaneously with a mathematical metric based on a model of an object environment. Our multi-objective problem is formulated as a single kinematic objective function with auto-tuning force-based weight. Our results indicate that our proposed objective function determines optimal parameters and kinematic ranges for our under-actuated gripper in the task environment with sufficient grasping forces.
ResNet strikes back: An improved training procedure in timm

The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization&data-augmentation have increased the effectiveness of the training recipes. In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224x224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure.
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains by either using image or instance-level feature alignment in an adversarial fashion. This often suffers due to the presence of unwanted background and as such lacks class-specific alignment. A common remedy to promote class-level alignment is to use high confidence predictions on the unlabelled domain as pseudo labels. These high confidence predictions are often fallacious since the model is poorly calibrated under domain shift. In this paper, we propose to leverage model predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment. Specifically, we measure predictive uncertainty on class assignments and the bounding box predictions. Model predictions with low uncertainty are used to generate pseudo-labels for self-supervision, whereas the ones with higher uncertainty are used to generate tiles for an adversarial feature alignment stage. This synergy between tiling around the uncertain object regions and generating pseudo-labels from highly certain object regions allows us to capture both the image and instance level context during the model adaptation stage. We perform extensive experiments covering various domain shift scenarios. Our approach improves upon existing state-of-the-art methods with visible margins.
Simulating Streamflow in Response to Climate Change in The Upper Ewaso Ngiro Catchment, Kenya

The study simulated streamflow response under changing climate for Ewaso Ngiro river in Upper Ewaso Ngiro Catchment (UENC), using Soil and Water Assessment Tool (SWAT). Data from National Centre for Meteorological research (CNRM) model of Co-Ordinated Regional Downscaling Experiment (CORDEX) was used to generate climate change scenarios (temperature and rainfall) for representative concentration pathway (RCP) 4.5 and 8.5 from 2021-2080 relative to the baseline 1976-2005. SWAT model was set up using historical daily rainfall and temperature data, soils, Digital Elevation Model and land cover map, and calibrated against observed streamflow. Decreasing trend in historical rainfall and streamflow was observed while increasing trend was observed for temperature. Projections indicated increasing trend in temperature in both RCPs, with RCP 8.5 having higher increase (1.1-2.6 0 C) than RCP 4.5 (1.0-1.7 0 C). Rainfall was found to increase from March-November, and decreased in December-February in all scenarios. Change in total annual rainfall ranged from 0.1-18.5% in 20212050 and 1.2-18.7% in 2051-2080, which corresponded to increase in streamflow of 20.9-23.6% and 21.2-28.2% respectively. Streamflow in March-May decreased (-26 to -10%) in all scenarios and increased in June-February (9-114%). This was found contrary to streamflow patterns simulated in neighboring catchments where studies indicate increasing streamflow trend in March-May. Streamflow response was found to be sensitive to changes in rainfall, thus emphasis should be put on water conservation and catchment management including protection of headwater forests through agroforestry, afforestation and reforestation.
Multi Expression Programming - an in-depth description

Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer programs. When MEP individuals encode expressions, their representation is similar to the way in which compilers translate C or Pascal expressions into machine code. A unique MEP feature is the ability to store multiple solutions to a problem in a single chromosome. Usually, the best solution is chosen for fitness assignment. When solving symbolic regression or classification problems (or any other problems for which the training set is known before the problem is solved) MEP has the same complexity as other techniques storing a single solution in a chromosome (such as GP, CGP, GEP, or GE). Evaluation of the expressions encoded into an MEP individual can be performed by a single parsing of the chromosome. Offspring obtained by crossover and mutation is always syntactically correct MEP individuals (computer programs). Thus, no extra processing for repairing newly obtained individuals is needed.
A type I conjecture and boundary representations of hyperbolic groups

We establish new results on the weak containment of quasi‐regular and Koopman representations of a second countable locally compact group G$G$ associated with nonsingular G$G$ ‐spaces. We deduce that any two boundary representations of a hyperbolic locally compact group are weakly equivalent. We also show that nonamenable hyperbolic locally compact groups with a cocompact amenable subgroup are characterized by the property that any two proper length functions are homothetic up to an additive constant. Combining those results with the work of Ł. Garncarek on the irreducibility of boundary representations of discrete hyperbolic groups, we deduce that a type I hyperbolic group with a cocompact lattice contains a cocompact amenable subgroup. Specializing to groups acting on trees, we answer a question of C. Houdayer and S. Raum.
BINet: Learning to Solve Partial Differential Equations with Boundary Integral Networks

We propose a method combining boundary integral equations and neural networks (BINet) to solve partial differential equations (PDEs) in both bounded and unbounded domains. Unlike existing solutions that directly operate over original PDEs, BINet learns to solve, as a proxy, associated boundary integral equations using neural networks. The benefits are three-fold. Firstly, only the boundary conditions need to be fitted since the PDE can be automatically satisfied with single or double layer representations according to the potential theory. Secondly, the dimension of the boundary integral equations is typically smaller, and as such, the sample complexity can be reduced significantly. Lastly, in the proposed method, all differential operators of the original PDEs have been removed, hence the numerical efficiency and stability are improved. Adopting neural tangent kernel (NTK) techniques, we provide proof of the convergence of BINets in the limit that the width of the neural network goes to infinity. Extensive numerical experiments show that, without calculating high-order derivatives, BINet is much easier to train and usually gives more accurate solutions, especially in the cases that the boundary conditions are not smooth enough. Further, BINet outperforms strong baselines for both one single PDE and parameterized PDEs in the bounded and unbounded domains.
Offline Reinforcement Learning with Reverse Model-based Imagination

In offline reinforcement learning (offline RL), one of the main challenges is to deal with the distributional shift between the learning policy and the given dataset. To address this problem, recent offline RL methods attempt to introduce conservatism bias to encourage learning in high-confidence areas. Model-free approaches directly encode such bias into policy or value function learning using conservative regularizations or special network structures, but their constrained policy search limits the generalization beyond the offline dataset. Model-based approaches learn forward dynamics models with conservatism quantifications and then generate imaginary trajectories to extend the offline datasets. However, due to limited samples in offline datasets, conservatism quantifications often suffer from overgeneralization in out-of-support regions. The unreliable conservative measures will mislead forward model-based imaginations to undesired areas, leading to overaggressive behaviors. To encourage more conservatism, we propose a novel model-based offline RL framework, called Reverse Offline Model-based Imagination (ROMI). We learn a reverse dynamics model in conjunction with a novel reverse policy, which can generate rollouts leading to the target goal states within the offline dataset. These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset. ROMI can effectively combine with off-the-shelf model-free algorithms to enable model-based generalization with proper conservatism. Empirical results show that our method can generate more conservative behaviors and achieve state-of-the-art performance on offline RL benchmark tasks.
Unbiased experiments in congested networks

When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new algorithm, and a control group, which uses the existing algorithm. However, because networks are congested, both treatment and control traffic compete against each other for resources in a way that biases the outcome of these tests. This bias can have a surprisingly large effect; for example, in lab A/B tests with two widely used congestion control algorithms, the treatment appeared to deliver 150% higher throughput when used by a few flows, and 75% lower throughput when used by most flows---despite the fact that the two algorithms have identical throughput when used by all traffic. Beyond the lab, we show that A/B tests can also be biased at scale. In an experiment run in cooperation with Netflix, estimates from A/B tests mistake the direction of change of some metrics, miss changes in other metrics, and overestimate the size of effects. We propose alternative experiment designs, previously used in online platforms, to more accurately evaluate new algorithms and allow experimenters to better understand the impact of congestion on their tests.
The Complexity of Learning Approval-Based Multiwinner Voting Rules

We study the PAC learnability of multiwinner voting, focusing on the class of approval-based committee scoring (ABCS) rules. These are voting rules applied on profiles with approval ballots, where each voter approves some of the candidates. According to ABCS rules, each committee of k candidates collects from each voter a score, that depends on the size of the voter's ballot and on the size of its intersection with the committee. Then, committees of maximum score are the winning ones. Our goal is to learn a target rule (i.e., to learn the corresponding scoring function) using information about the winning committees of a small number of sampled profiles. Despite the existence of exponentially many outcomes compared to single-winner elections, we show that the sample complexity is still low: a polynomial number of samples carries enough information for learning the target rule with high confidence and accuracy. Unfortunately, even simple tasks that need to be solved for learning from these samples are intractable. We prove that deciding whether there exists some ABCS rule that makes a given committee winning in a given profile is a computationally hard problem. Our results extend to the class of sequential Thiele rules, which have received attention due to their simplicity.
Neural Dependency Coding inspired Multimodal Fusion

Information integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different sensory perceptive fields to interact with the environment and each other. Recent work in deep fusion models via neural networks has led to substantial improvements over unimodal approaches in areas like speech recognition, emotion recognition and analysis, captioning and image description. However, such research has mostly focused on architectural changes allowing for fusion of different modalities while keeping the model complexity manageable. Inspired by recent neuroscience ideas about multisensory integration and processing, we investigate the effect of synergy maximizing loss functions. Experiments on multimodal sentiment analysis tasks: CMU-MOSI and CMU-MOSEI with different models show that our approach provides a consistent performance boost.
A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems

In compact stellar triple systems, an evolved tertiary star can overflow its Roche lobe around the inner binary. Subsequently, the tertiary star can transfer mass to the inner binary in a stable manner, or Roche lobe overflow (RLOF) can be unstable and lead to common-envelope (CE) evolution. In the latter case, the inner binary enters the extended envelope of the tertiary star and spirals in toward the donor’s core, potentially leading to mergers or ejections. Although studied in detail for individual systems, a comprehensive statistical view on the various outcomes of triple RLOF is lacking. Here, we carry out 105 population synthesis simulations of tight triples, self-consistently taking into account stellar evolution, binary interactions, and gravitational dynamics. Also included are prescriptions for the long-term evolution of stable triple mass transfer, and triple CE evolution. Although simple and ignoring hydrodynamic effects, these prescriptions allow for a qualitative statistical study. We find that triple RLOF occurs in ∼0.06% of all triple systems. Of these 0.06%, ∼64% of cases lead to stable mass transfer, and ∼36% to triple CE evolution. Triple CE is most often (∼76%) followed by one or multiple mergers in short succession, most likely an inner binary merger of two main-sequence stars. Other outcomes of triple CE are a binary+single system (∼23%, most of which do not involve exchange interactions), and a stable triple (∼1%). We also estimate the rate of type Ia supernovae involving white dwarf mergers following triple RLOF, but find only a negligible contribution.
On the Importance of Gradients for Detecting Distributional Shifts in the Wild

Detecting out-of-distribution (OOD) data has become a critical component in ensuring the safe deployment of machine learning models in the real world. Existing OOD detection approaches primarily rely on the output or feature space for deriving OOD scores, while largely overlooking information from the gradient space. In this paper, we present GradNorm, a simple and effective approach for detecting OOD inputs by utilizing information extracted from the gradient space. GradNorm directly employs the vector norm of gradients, backpropagated from the KL divergence between the softmax output and a uniform probability distribution. Our key idea is that the magnitude of gradients is higher for indistribution (ID) data than that for OOD data, making it informative for OOD detection. GradNorm demonstrates superior performance, reducing the average FPR95 by up to 10.89% compared to the previous best method. Code and data available: https://github.com/deeplearning-wisc/gradnorm_ood.
Deadlock Freedom for Asynchronous and Cyclic Process Networks

This paper considers the challenging problem of establishing deadlock freedom for message-passing processes using behavioral type systems. In particular, we consider the case of processes that implement session types by communicating asynchronously in cyclic process networks. We present APCP, a typed process framework for deadlock freedom which supports asynchronous communication, delegation, recursion, and a general form of process composition that enables specifying cyclic process networks. We discuss the main decisions involved in the design of APCP and illustrate its expressiveness and flexibility using several examples.
A Survey of Knowledge Enhanced Pre-trained Models

—Pre-trained models learn informative representations on large-scale training data through a self-supervised or supervised learning method, which has achieved promising performance in natural language processing (NLP), computer vision (CV), and cross-modal ﬁelds after ﬁne-tuning. These models, however, suffer from poor robustness and lack of interpretability. Pre-trained models with knowledge injection, which we call knowledge enhanced pre-trained models (KEPTMs), possess deep understanding and logical reasoning and introduce interpretability. In this survey, we provide a comprehensive overview of KEPTMs in NLP and CV. We ﬁrst introduce the progress of pre-trained models and knowledge representation learning. Then we systematically categorize existing KEPTMs from three different perspectives. Finally, we outline some potential directions of KEPTMs for future research.
MemBERT: Injecting Unstructured Knowledge into BERT

Transformers changed modern NLP in many ways. However, they can hardly exploit domain knowledge, and like other blackbox models, they lack interpretability. Unfortunately, structured knowledge injection, in the long run, risks to suffer from a knowledge acquisition bottleneck. We thus propose a memory enhancement of transformer models that makes use of unstructured domain knowledge expressed in plain natural language. An experimental evaluation conducted on two challenging NLP tasks demonstrates that our approach yields better performance and model interpretability than baseline transformer-based architectures.