Miracle-dz/UniFAR-FTUs huggingface dataset:
Xylem vessel type and structure influence the water transport characteristics of Panax notoginseng

Panax notoginseng plays a very important role in medicinal and economic value. The restriction imposed by the hydraulic pathway is considered to be the main limitation on the optimal growth state of Panax notoginseng. The flow resistance and water transport efficiency of vessel were affected by vessel type and secondary thickening structure. The vessel structure parameters of Panax notoginseng were obtained by experimental anatomy, and the flow resistance characteristics were analyzed by numerical simulation. The results showed that the xylem vessels had annular thickening and pit thickening walls. The flow resistance coefficient (ξ) of the pitted thickening vessel was significantly lower than that of annular thickening vessel in four cross-sectional types. The ξ of the circular cross-sectional vessel was the largest, followed by the hexagon, pentagon cross-sectional vessel and the lowest was the quadrilateral cross-sectional vessel, and the structure coefficient (S) was just the opposite. The ξ of the vessel model was positively correlated with the annular height, pitted width and pitted height, and negatively correlated with the annular inscribed circle diameter, annular width, annular spacing, pitted inscribed circle diameter and pitted spacing. Among them, annular (pitted) height and the annular (pitted) inscribed circle diameter had a great influence on the ξ. The increasing and decreasing trend of the S and ξ were opposite in the change of annular (pitted) inscribed circle diameter, and consistent in the change of in other structural parameters, indicating that the secondary wall thickening structure limited the inner diameter of the vessel to maintain a balance between flow resistance and transport efficiency.
Roles of p38 MAPK signalling in intervertebral disc degeneration

Intervertebral disc degeneration (IVDD) is a common degenerative disease mediated by multiple factors. Because of its complex aetiology and pathology, no specific molecular mechanisms have yet been identified and no definitive treatments are currently available for IVDD. p38 mitogen‐activated protein kinase (MAPK) signalling, part of the serine and threonine (Ser/Thr) protein kinases family, is associated with the progression of IVDD, by mediating the inflammatory response, increasing extracellular matrix (ECM) degradation, promoting cell apoptosis and senescence and suppressing cell proliferation and autophagy. Meanwhile, the inhibition of p38 MAPK signalling has a significant effect on IVDD treatment. In this review, we first summarize the regulation of p38 MAPK signalling and then highlight the changes in the expression of p38 MAPK signalling and their impact on pathological process of IVDD. Moreover, we discuss the current applications and future prospects of p38 MAPK as a therapeutic target for IVDD treatment.
Leisure‐time physical activity and sarcopenia among older adults from low‐ and middle‐income countries

There are no data on the association between leisure‐time physical activity (LTPA) and sarcopenia in older adults from low‐ and middle‐income countries (LMICs). This study aimed to investigate the association between LTPA and sarcopenia in individuals aged ≥65 years living in six LMICs.
MOVER: Medical Informatics Operating Room Vitals and Events Repository

Artificial Intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository, which includes data from 58,799 unique patients and 83,468 surgeries collected from the UCI Medical Center over a period of seven years. MOVER is freely available to all researchers who sign a data usage agreement, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.
Understanding the Use of e-Prints on Reddit and 4chan’s Politically Incorrect Board

The dissemination and reach of scientific knowledge have increased at a blistering pace. In this context, e-Print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without waiting for the (lengthy) peer review process. While helping the scientific community in several ways, e-Print servers also provide scientific communicators and the general public with access to a wealth of knowledge without paying hefty subscription fees. This motivates us to study how e-Prints are positioned within Web community discussions. In this paper, we analyze data from two Web communities: 14 years of Reddit data and over 4 from 4chan’s Politically Incorrect board. Our findings highlight the presence of e-Prints in both science-enthusiast and general-audience communities. Real-world events and distinct factors influence the e-Prints people’s discussions; e.g., there was a surge of COVID-19-related research publications during the early months of the outbreak and increased references to e-Prints in online discussions. Text in e-Prints and in online discussions referencing them has a low similarity, suggesting that the latter are not exclusively talking about the findings in the former. Further, our analysis of a sample of threads highlights: 1) misinterpretation and generalization of research findings, 2) early research findings being amplified as a source for future predictions, and 3) questioning findings from a pseudoscientific e-Print. Overall, our work emphasizes the need to quickly and effectively validate non-peer-reviewed e-Prints that get substantial press/social media coverage to help mitigate wrongful interpretations of scientific outputs.
Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction

Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibility for any member trying to not only successfully participate in the task but to ensure the team achieves its goals. The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action, thereby forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such iterated human-robot interaction settings, where the human adopts a supervisory role. In our model, the robot integrates human's trust and their expectations about the robot into its planning process to build and maintain trust over the interaction horizon. By establishing the required level of trust, the robot can focus on maximizing the team goal by eschewing explicit explanatory or explicable behavior without worrying about the human supervisor monitoring and intervening to stop behaviors they may not necessarily understand. We model this reasoning about trust levels as a meta reasoning process over individual planning tasks. We additionally validate our model through a human subject experiment.
Resonant Two-Laser Spin-State Spectroscopy of a Negatively Charged Quantum-Dot–Microcavity System with a Cold Permanent Magnet

A high-efficiency spin-photon interface is an essential piece of quantum hardware necessary for various quantum technologies. Self-assembled InGaAs quantum dots have excellent optical properties, if embedded into an optical micro-cavity they can show near-deterministic spin-photon entanglement and spin readout, but an external magnetic field is required to address the individual spin states, which usually is done using a superconducting magnet. Here, we show a compact cryogenically compatible SmCo magnet design that delivers 475 mT in-plane Voigt geometry magnetic field at 5 K, which is suitable to lift the energy degeneracy of the electron spin states and trion transitions of a single InGaAs quantum dot. This quantum dot is embedded in a birefringent high-finesse optical micro-cavity which enables efficient collection of single photons emitted by the quantum dot. We demonstrate spin-state manipulation by addressing the trion transitions with a single and two laser fields. The experimental data agrees well to our model which covers single- and two-laser cross-polarized resonance fluorescence, Purcell enhancement in a birefringent cavity, and variation of the laser powers.
A Survey on Incremental Update for Neural Recommender Systems

Recommender Systems (RS) aim to provide personalized suggestions of items for users against consumer over-choice. Although extensive research has been conducted to address different aspects and challenges of RS, there still exists a gap between academic research and industrial applications. Specifically, most of the existing models still work in an offline manner, in which the recommender is trained on a large static training set and evaluated on a very restrictive testing set in a one-time process. RS will stay unchanged until the next batch retrain is performed. We frame such RS as Batch Update Recommender Systems (BURS). In reality, they have to face the challenges where RS are expected to be instantly updated with new data streaming in, and generate updated recommendations for current user activities based on the newly arrived data. We frame such RS as Incremental Update Recommender Systems (IURS). In this article, we offer a systematic survey of incremental update for neural recommender systems. We begin the survey by introducing key concepts and formulating the task of IURS. We then illustrate the challenges in IURS compared with traditional BURS. Afterwards, we detail the introduction of existing literature and evaluation issues. We conclude the survey by outlining some prominent open research issues in this area.
From Rolling Over to Walking: Enabling Humanoid Robots to Develop Complex Motor Skills

This paper presents an innovative method for humanoid robots to acquire a comprehensive set of motor skills through reinforcement learning. The approach utilizes an achievement-triggered multi-path reward function rooted in developmental robotics principles, facilitating the robot to learn gross motor skills typically mastered by human infants within a single training phase. The proposed method outperforms standard reinforcement learning techniques in success rates and learning speed within a simulation environment. By leveraging the principles of self-discovery and exploration integral to infant learning, this method holds the potential to significantly advance humanoid robot motor skill acquisition.
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers’ own data as well as city-level data, we create probes—interactive data visuals—that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers’ well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.
Differentially Private Confidence Interval for Extrema of Parameters

This paper aims to construct a valid and efficient confidence interval for the extrema of parameters under privacy protection. The usual statistical inference on the extrema of parameters often suffers from the selection bias issue, and the problem becomes more acute, as in many application scenarios of extrema parameters, we often need to protect the privacy of the data. In this paper, we focus on the exponential family of distributions and propose a privatized parametric bootstrap method to address selection bias in the extrema of parameters problem under the scheme of differential privacy. While the usual privatized parametric bootstrap does not address selection bias appropriately, we prove that with a privatized bias correction term, the proposed parametric bootstrap method can lead to a valid and efficient confidence interval for the extrema of parameters. We illustrate the proposed method with the Gaussian case and regression case and demonstrate the advantages of the proposed method via numerical experiments.
Complexity of (arc)-connectivity problems involving arc-reversals or deorientations

By a well known theorem of Robbins, a graph $G$ has a strongly connected orientation if and only if $G$ is 2-edge-connected and it is easy to find, in linear time, either a cut edge of $G$ or a strong orientation of $G$. A result of Durand de Gevigny shows that for every $k\geq 3$ it is NP-hard to decide if a given graph $G$ has a $k$-strong orientation. Thomassen showed that one can check in polynomial time whether a given graph has a 2-strong orientation. This implies that for a given digraph $D$ we can determine in polynomial time whether we can reorient (=reverse) some arcs of $D=(V,A)$ to obtain a 2-strong digraph $D'=(V,A')$. This naturally leads to the question of determining the minimum number of such arcs to reverse before the resulting graph is 2-strong. In this paper we show that finding this number is NP-hard. If a 2-connected graph $G$ has no 2-strong orientation, we may ask how many of its edges we may orient so that the resulting mixed graph is still 2-strong. Similarly, we may ask for a 2-edge-connected graph $G$ how many of its edges we can orient such that the resulting mixed graph remains 2-arc-strong. We prove that when restricted to graphs satisfying suitable connectivity conditions, both of these problems are equivalent to finding the minimum number of edges we must double in a 2-edge-connected graph in order to obtain a 4-edge-connected graph. Using this, we show that all these three problems are NP-hard. Finally, we consider the operation of deorienting an arc $uv$ of a digraph $D$ meaning replacing it by an undirected edge between the same vertices. In terms of connectivity properties, this is equivalent to adding the opposite arc $vu$ to $D$. We prove that for every $\ell\geq 3$ it is NP-hard to find the minimum number of arcs to deorient in a digraph $D$ in order to obtain an $\ell$-strong digraph $D'$.
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization

The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents and satisfies content diversity. This paper proposes a multi-document summarization approach based on hierarchical clustering of documents. It utilizes the constructed class tree of documents to extract both the sentences reflecting the commonality of all documents and the sentences reflecting the specificity of some subclasses of these documents for generating a summary, so as to satisfy the coverage and diversity requirements of multi-document summarization. Comparative experiments with different variant approaches on DUC'2002-2004 datasets prove the effectiveness of mining both the commonality and specificity of documents for multi-document summarization. Experiments on DUC'2004 and Multi-News datasets show that our approach achieves competitive performance compared to the state-of-the-art unsupervised and supervised approaches.
Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI agent with the goal of generating more human-like behavior. We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents with human-generated behavior. Our proposed agent passes a Turing Test, while the baseline agents do not. By passing a Turing Test, we mean that human judges could not quantitatively distinguish between videos of a person and an AI agent navigating. To understand what people believe constitutes human-like navigation, we extensively analyze the justifications of these assessments. This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation, which is a key step for further improving human interactions with AI agents.
Formation of a simple cubic antiferromagnet through charge ordering in a double Dirac material

The appearance of spontaneous charge order in chemical systems is often associated with the emergence of novel, and useful, properties. Here we show through single crystal diffraction that the Eu ions in the mixed valent metal EuPd$_3$S$_4$ undergo long-range charge ordering at $T_{\mathrm{CO}} = 340 \mathrm{~K}$ resulting in simple cubic lattices of Eu$^{2+}$ ($J = 7/2$) and Eu$^{3+}$ ($J = 0$) ions. As only one of the two sublattices has a non-magnetic ground state, the charge order results in the emergence of remarkably simple G-type antiferromagnetic order at $T_{\mathrm{N}} = 2.85(6) \mathrm{~K}$, observed in magnetization, specific heat, and neutron diffraction. Application of a $0.3 \mathrm{~T}$ field is sufficient to induce a spin flop transition to a magnetically polarized, but still charge ordered, state. Density functional theory calculations show that this charge order also modifies the electronic degeneracies present in the material: without charge order, EuPd$_3$S$_4$ is an example of a double Dirac material containing 8-fold degenerate electronic states, greater than the maximum degeneracy of six possible in molecular systems. The symmetry reduction from charge order transmutes 8-fold double Dirac states into 4-fold Dirac states, a degeneracy that can be preserved even in the presence of the magnetic order. Our results show not only how charge order can be used to produce interesting magnetic lattices, but also how it can be used to engineer controlled degeneracies in electronic states.
Confidence-Based Event-Centric Online Video Question Answering on a Newly Constructed ATBS Dataset

Deep neural networks facilitate video question answering (VideoQA), but the real-world applications on video streams such as CCTV and live cast place higher demands on the solver. To address the challenges of VideoQA on long videos of unknown length, we define a new set of problems called Online Open-ended Video Question Answering (O2VQA). It requires an online state-updating mechanism for the solver to decide if the collected information is sufficient to conclude an answer. We then propose a Confidence-based Event-centric Online Video Question Answering (CEO-VQA) model to solve this problem. Furthermore, a dataset called Answer Target in Background Stream (ATBS) is constructed to evaluate this newly developed online VideoQA application. Compared to the baseline VideoQA method that watches the whole video, the experimental results show that the proposed method achieves a significant performance gain.
MACARONS: Mapping and Coverage Anticipation with RGB Online Self-Supervision

We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only. This is closely related to the Next Best View problem (NBV), where one has to identify where to move the camera next to improve the coverage of an unknown scene. However, most of the current NBV methods rely on depth sensors, need 3D supervision and/or do not scale to large scenes. Our method requires only a color camera and no 3D supervision. It simultaneously learns in a self-supervised fashion to predict a “volume occupancy field” from color images and, from this field, to predict the NBV. Thanks to this approach, our method performs well on new scenes as it is not biased towards any training 3D data. We demonstrate this on a recent dataset made of various 3D scenes and show it performs even better than recent methods requiring a depth sensor, which is not a realistic assumption for outdoor scenes captured with a flying drone.
A unified treatment of families of partition functions

We present a unified framework of combinatorial descriptions, and the analogous asymptotic growth of the coefficients of two general families of functions related to integer partitions. In particular, we resolve several conjectures and verify several claims that are posted on the On-Line Encyclopedia of Integer Sequences. We perform the asymptotic analysis by systematically applying the Mellin transform, residue analysis, and the saddle point method. The combinatorial descriptions of these families of generalized partition functions involve colorings of Young tableaux, along with their ``divisor diagrams'', denoted with sets of colors whose sizes are controlled by divisor functions.
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers

Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack; 2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy; 3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://github.com/yhlleo/MJP.