Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixing Time and Cutoff for the k-SEP

We investigate the mixing time of the capacity $k$ simple exclusion process (also called the partial exclusion process) of Schultz and Sandow with $m$ particles on a segment of length $N$. We show that the $k$-SEP exhibits cutoff at time $\frac{1}{2k\pi^2}N^2\log m$. We also introduce a related complete multi-species process that we call the $S_{k,N}$ shuffle and show that this process exhibits cutoff at time $\frac{1}{2k\pi^2}N^2\log (kN)$. This extends the celebrated result of Lacoin which determined the mixing time of the symmetric simple exclusion process on a segment of length $N$ and the adjacent transposition shuffle, and proved cutoff in both.
Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulator

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile's future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator's dynamic limits and extreme time constraints (<350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot's joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions.
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications

OBJECTIVES Extracting PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method, PICOX, to extract overlapping PICO entities. MATERIALS AND METHODS PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then, it uses a multi-label classifier to assign one or more PICO labels to a span candidate. PICOX was evaluated using one of the best-performing baselines, EBM-NLP, and three more datasets, ie, PICO-Corpus and RCT publications on Alzheimer's Disease or COVID-19, using entity-level precision, recall, and F1 scores. RESULTS PICOX achieved superior precision, recall, and F1 scores across the board, with the micro F1 score improving from 45.05 to 50.87 (p ≪ .01). On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56.66 to 67.33. On the COVID-19 dataset, PICOX also outperformed the baseline and improved the micro F1 score from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores with higher precision when compared to the baseline. CONCLUSION PICOX excels in identifying overlapping entities and consistently surpasses a leading baseline across multiple datasets. Ablation studies reveal that its data augmentation strategy effectively minimizes false positives and improves precision.
Effectiveness of winter rye for accumulating residual fertilizer N following corn

ABSTRACT: Excessive nitrogen fertilization rates applied to a summer annual crop and or unusually low crop yields due to drought, can result in accumulation of NO−3 -N in the rooting zone. The objectives of this study were to (1) measure the winter rye (Secale cereale) recovery and subsequent soil distribution of residual fertilizer N following corn (Zea mays L.) having received fertilizer N at 84, 168, 252, and 336 kg N ha−1, as 15N-depleted (MH4), and (2) estimate potential losses of fertilizer-derived mineral N to the environment following a corn-rye rotation. Total N uptake by winter rye averaged 82 and 49 kg ba−1 in 1990 and 1991, respectively. Recovery of fertilizer N by winter rye, as measured by the isotope method, increased with increasing N rate applied to the previous corn crop and ranged from 4 to 36 kg N ha−1 in 1990 and 2 to 26 kg N ha−1 in 1991. Fertilizer N recovery in 1991 was highest in ye plots where the previous corn crop had been planted no-till (NT) into rye stubble. Little or no fertilizer derived mineral N was measured to 90-cm depth following a winter rye cover crop. In contrast, amounts of fertilizer-derived mineral N increased with depth and previous fertilizer N rate with winter fallow. These results provide evidence to support the we of a winter rye cover crop on a silt loam soil to recover residual fertilizer-derived mineral N that might otherwise be lost to groundwater.
Understanding the Formation and Evolution of Dark Galaxies in a Simulated Universe

We study the formation and evolution of dark galaxies using the IllustrisTNG cosmological hydrodynamical simulation. We first identify dark galaxies with stellar-to-total mass ratios, M */M tot, smaller than 10−4, which differ from luminous galaxies with M */M tot ≥ 10−4. We then select the galaxies with a dark matter halo mass of ∼109 h −1 M ⊙ for mass completeness and compare their physical properties with those of luminous galaxies. We find that, at the present epoch (z = 0), dark galaxies are predominantly located in void regions without star-forming gas. We also find that dark galaxies tend to have larger sizes and higher spin parameters than luminous galaxies. In the early universe, dark and luminous galaxies show small differences in the distributions of spin and local environment estimates, and the difference between the two samples becomes more significant as they evolve. Our results suggest that, unlike luminous galaxies, dark galaxies tend to be initially formed in less dense regions and could not form stars because of heating from cosmic reionization and few interactions and mergers with other systems containing stars. This study based on numerical simulations can provide important hints for validating dark galaxy candidates in observations and for constraining galaxy formation models.
Cooling the Earth with $CO_2$ filled containers in space

We argue that geostationary (GEO) reflective containers filled with $CO_2$ could be used as shading devices to selectively cool areas on Earth's surface. This proposal would be an interesting addition to the recently discussed suggestion of dumping $CO_2$ to space through the well of a space lift \cite{OB2023}. We also explore the possibility of producing propellants in GEO out of greenhouse gases expelled from the space lift. Finally, we discuss the much less effective idea of filtering the most prominent infrared bands of the incoming solar radiation using the $CO_2$ wrapped in transparent vessels.
AdaBB: Adaptive Barzilai-Borwein Method for Convex Optimization

In this paper, we propose AdaBB, an adaptive gradient method based on the Barzilai-Borwein stepsize. The algorithm is line-search-free and parameter-free, and essentially provides a convergent variant of the Barzilai-Borwein method for general unconstrained convex optimization. We analyze the ergodic convergence of the objective function value and the convergence of the iterates for solving general unconstrained convex optimization. Compared with existing works along this line of research, our algorithm gives the best lower bounds on the stepsize and the average of the stepsizes. Moreover, we present an extension of the proposed algorithm for solving composite optimization where the objective function is the summation of a smooth function and a nonsmooth function. Our numerical results also demonstrate very promising potential of the proposed algorithms on some representative examples.
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models

With the increased capabilities at the edge (e.g., mobile device) and more stringent privacy requirement, it becomes a recent trend for deep learning-enabled applications to pre-process sensitive raw data at the edge and transmit the features to the backend cloud for further processing. A typical application is to run machine learning (ML) services on facial images collected from different individuals. To prevent identity theft, conventional methods commonly rely on an adversarial game-based approach to shed the identity information from the feature. However, such methods can not defend against adaptive attacks, in which an attacker takes a countermove against a known defence strategy. We propose Crafter, a feature crafting mechanism deployed at the edge, to protect the identity information from adaptive model inversion attacks while ensuring the ML tasks are properly carried out in the cloud. The key defence strategy is to mislead the attacker to a non-private prior from which the attacker gains little about the private identity. In this case, the crafted features act like poison training samples for attackers with adaptive model updates. Experimental results indicate that Crafter successfully defends both basic and possible adaptive attacks, which can not be achieved by state-of-the-art adversarial game-based methods.
Recent Advances in Soil Liquefaction Engineering and Seismic Site Response Evaluation

Over the past decade, major advances have occurred in both understanding and practice with regard to engineering treatment of seismic soil liquefaction and assessment of seismic site response. Seismic soil liquefaction engineering has evolved into a sub-field in its own right, and assessment and treatment of site effects affecting seismic site response has gone from a topic of controversy to a mainstream issue addressed in most modem building codes and addressed in both research and practice. This rapid evolution in the treatment of both liquefaction and site response issues has been pushed by a confluence of lessons and data provided by a series of earthquakes over the past eleven years, as well as by the research and professional/political will engendered by these major seismic events. Although the rate of progress has been laudable, further advances are occurring, and more remains to be done. As we enter a “new millenium”, engineers are increasingly well able to deal with important aspects of these two seismic problem areas. This paper will highlight a few major recent and ongoing developments in each of these two important areas of seismic practice, and will offer insights regarding work/research in progress, as well as suggestions regarding further advances needed. The first part of the paper will address soil liquefaction, and the second portion will (briefly) address engineering assessment of seismic site response.
Spatial-Semantic Collaborative Cropping for User Generated Content

A large amount of User Generated Content (UGC) is uploaded to the Internet daily and displayed to people world-widely through the client side (mobile and PC). This requires the cropping algorithms to produce the aesthetic thumbnail within a specific aspect ratio on different devices. However, existing image cropping works mainly focus on landmark or landscape images, which fail to model the relations among the multi-objects with the complex background in UGC. Besides, previous methods merely consider the aesthetics of the cropped images while ignoring the content integrity, which is crucial for UGC cropping. In this paper, we propose a Spatial-Semantic Collaborative cropping network (S2CNet) for arbitrary user generated content accompanied by a new cropping benchmark. Specifically, we first mine the visual genes of the potential objects. Then, the suggested adaptive attention graph recasts this task as a procedure of information association over visual nodes. The underlying spatial and semantic relations are ultimately centralized to the crop candidate through differentiable message passing, which helps our network efficiently to preserve both the aesthetics and the content integrity. Extensive experiments on the proposed UGCrop5K and other public datasets demonstrate the superiority of our approach over state-of-the-art counterparts.
Transformer for Object Re-Identification: A Survey

Object Re-Identification (Re-ID) aims to identify and retrieve specific objects from varying viewpoints. For a prolonged period, this field has been predominantly driven by deep convolutional neural networks. In recent years, the Transformer has witnessed remarkable advancements in computer vision, prompting an increasing body of research to delve into the application of Transformer in Re-ID. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single-/cross modal tasks. Besides, this survey also covers a wide range of Re-ID research objects, including progress in animal Re-ID. Given the diversity of species in animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task to facilitate future research. Finally, we discuss some important yet under-investigated open issues in the big foundation model era, we believe it will serve as a new handbook for researchers in this field.
Correspondence Theory for Many-valued Modal Logic

The aim of the present paper is to generalise Sahlqvist correspondence theory to the many-valued setting. For truth algebras that are perfect Heyting algebras, we present the standard translation between many-valued modal languages and suitably defined first-order and second-order correspondence languages; and prove its correctness. We then define a general class of inequalities, called Sahlqvist inequalities, to which algebraic methods can be applied to obtain first-order local frame correspondents in the many-valued setting. Difficulties that arise due to the lack of classical negation in the many-valued setting are overcome by making use of signed generation trees. Next we show that algorithmic approaches to obtain first-order local frame correspondents can be generalise to the many-valued setting and present the generalisation of the ALBA algorithm for this purpose. We conclude with a comparison between classical (two-valued) and many-valued frame correspondents.
Disease Transmission on Random Graphs Using Edge-Based Percolation

Edge-based percolation methods can be used to analyze disease transmission on complex social networks. This allows us to include complex social heterogeneity in our models while maintaining tractability. Here we review the seminal works on this field by Newman et al (2001); Newman (2002, 2003), and Miller et al (2012). We present a systematic discussion of the theoretical background behind these models, including an extensive derivation of the major results. We also connect these results relate back to the classical literature in random graph theory Molloy and Reed (1995, 1998). Finally, we also present an accompanying R package that takes epidemic and network parameters as input and generates estimates of the epidemic trajectory and final size. This manuscript and the R package was developed to help researchers easily understand and use network models to investigate the interaction between different community structures and disease transmission.
Freezing of short-range ordered antiferromagnetic clusters in the CrFeTi$_2$O$_7$ system

We report on the CrFeTi$_2$O$_7$ (CFTO) system using a combination of x-ray diffraction, dc magnetization, ac susceptibility, specific heat and neutron diffraction measurements. CFTO is seen to crystallize in a monoclinic $P21/a$ symmetry. It shows a glassy freezing at $T{_f}$ ~ 22 K, characterized by the observation of bifurcation between ZFC and FC ${\chi}$ (T) curves, frequency dispersion across $T{_f}$ in ac susceptibility, appearance of non-zero remanent magnetization and coercivity below $T{_f}$, very slow relaxation of iso-thermal remanent magnetization with time and a linear temperature dependence of magnetic contribution to specific heat $C{_m}$ below $T{_f}$. The microscopic neutron diffraction analysis of CFTO not only confirms the absence of long-range antiferromagnetic ordering but also exhibits diffuse scattering due to the presence of shortrange ordered antiferromagnetically correlated spin clusters.
Fundamental Convergence Analysis of Sharpness-Aware Minimization

The paper investigates the fundamental convergence properties of Sharpness-Aware Minimization (SAM), a recently proposed gradient-based optimization method (Foret et al., 2021) that significantly improves the generalization of deep neural networks. The convergence properties including the stationarity of accumulation points, the convergence of the sequence of gradients to the origin, the sequence of function values to the optimal value, and the sequence of iterates to the optimal solution are established for the method. The universality of the provided convergence analysis based on inexact gradient descent frameworks (Khanh et al., 2023b) allows its extensions to the normalized versions of SAM such as VaSSO (Li&Giannakis, 2023), RSAM (Liu et al., 2022), and to the unnormalized versions of SAM such as USAM (Andriushchenko&Flammarion, 2022). Numerical experiments are conducted on classification tasks using deep learning models to confirm the practical aspects of our analysis.
Unsupervised Domain Adaptation Using Compact Internal Representations

A major technique for tackling unsupervised domain adaptation involves mapping data points from both the source and target domains into a shared embedding space. The mapping encoder to the embedding space is trained such that the embedding space becomes domain agnostic, allowing a classifier trained on the source domain to generalize well on the target domain. To further enhance the performance of unsupervised domain adaptation (UDA), we develop an additional technique which makes the internal distribution of the source domain more compact, thereby improving the model's ability to generalize in the target domain.We demonstrate that by increasing the margins between data representations for different classes in the embedding space, we can improve the model performance for UDA. To make the internal representation more compact, we estimate the internally learned multi-modal distribution of the source domain as Gaussian mixture model (GMM). Utilizing the estimated GMM, we enhance the separation between different classes in the source domain, thereby mitigating the effects of domain shift. We offer theoretical analysis to support outperofrmance of our method. To evaluate the effectiveness of our approach, we conduct experiments on widely used UDA benchmark UDA datasets. The results indicate that our method enhances model generalizability and outperforms existing techniques.
Measuring competition in the Olympic Winter Games 1992–2014 using economic indices

Since the early 1990s, competition in the Olympic Winter Games has changed notably in terms of events contested and nations taking part. Despite, these changes, which are overseen by the International Olympic Committee (IOC), the number of medal-winning nations has remained relatively stable. As a first attempt to illustrate this issue on a discipline by discipline basis, economic techniques are used to examine the outcome of competition between 1992 and 2014. The purpose of this paper is to measure: market size; the number of competing nations; and the balance between competitive nations in six disciplines. Focusing on competitive balance, the Herfindahl–Hirschman Index is applied to measure the concentration of domination; while the Przeworski Index is used to quantify instability over time. Important changes are identified in biathlon (2010) and short track (2014). While the change in the former is consistent with the IOC’s substantial increase in biathlon events, the latter can be attributed to athletes changing their nationality. IOC policy-makers can benefit from this research as it provides a method by which to monitor competition in a discipline. This method provides the potential for evaluating the likely effects of governing the Olympic Games by increasing the number of events.
CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in-Memory Architectures

The demand for efficient machine learning (ML) accelerators is growing rapidly, driving the development of novel computing concepts such as resistive random access memory (RRAM)-based tiled computing-in-memory (CIM) architectures. CIM allows to compute within the memory unit, resulting in faster data processing and reduced power consumption. Efficient compiler algorithms are essential to exploit the potential of tiled CIM architectures. While conventional ML compilers focus on code generation for CPUs, GPUs, and other von Neumann architectures, adaptations are needed to cover CIM architectures. Cross-layer scheduling is a promising approach, as it enhances the utilization of CIM cores, thereby accelerating computations. Although similar concepts are implicitly used in previous work, there is a lack of clear and quantifiable algorithmic definitions for cross-layer scheduling for tiled CIM architectures. To close this gap, we present CLSA-CIM, a cross-layer scheduling algorithm for tiled CIM architectures. We integrate CLSA-CIM with existing weight-mapping strategies and compare performance against state-of-the-art (SOTA) scheduling algorithms. CLSA-CIM improves the utilization by up to 17.9 x , resulting in an overall speedup increase of up to 29.2 x compared to SOTA.
Multi-task real-robot data with gaze attention for dual-arm fine manipulation

In the field of robotic manipulation, deep imitation learning is recognized as a promising approach for acquiring manipulation skills. Additionally, learning from diverse robot datasets is considered a viable method to achieve versatility and adaptability. In such research, by learning various tasks, robots achieved generality across multiple objects. However, such multi-task robot datasets have mainly focused on single-arm tasks that are relatively imprecise, not addressing the fine-grained object manipulation that robots are expected to perform in the real world. This paper introduces a dataset of diverse object manipulations that includes dual-arm tasks and/or tasks requiring fine manipulation. To this end, we have generated dataset with 224k episodes (150 hours, 1,104 language instructions) which includes dual-arm fine tasks such as bowl-moving, pencil-case opening or banana-peeling, and this data is publicly available. Additionally, this dataset includes visual attention signals as well as dual-action labels, a signal that separates actions into a robust reaching trajectory and precise interaction with objects, and language instructions to achieve robust and precise object manipulation. We applied the dataset to our Dual-Action and Attention (DAA), a model designed for fine-grained dual arm manipulation tasks and robust against covariate shifts. The model was tested with over 7k total trials in real robot manipulation tasks, demonstrating its capability in fine manipulation.