Miracle-dz/UniFAR-FTUs huggingface dataset:
Pest categorisation of Urocerus albicornis

Abstract The EFSA Panel on Plant Health performed a pest categorisation of Urocerus albicornis (Hymenoptera: Siricidae), the black horntail sawfly, for the territory of the EU. U. albicornis is not listed in Annex II of Commission Implementing Regulation (EU) 2019/2072. U. albicornis occurs throughout Canada and continental USA and has established in northern Spain, and probably in southern France (based on two specimens caught in two sites) and Japan (based on one individual caught in one site). It attacks mostly stumps or fallen or weakened trees of at least 20 species of Pinaceae (Abies spp., Larix spp., Picea spp., Pinus spp., Pseudotsuga menziesii, Tsuga spp.) and of Cupressaceae (Thuja plicata). In Spain, the females fly between May and September with a peak in August and September. The eggs are deposited into the sapwood, together with mucus containing a venom and a white‐rot wood‐decay basidiomycete, either Amylostereum chailletii or A. areolatum. Each fungus is symbiotic with the insect. The larvae feed on wood infected by the fungus. All immature stages live in the host sapwood. In British Columbia, the lifecycle of the pest lasts 2 years but has not been fully characterised elsewhere. The wood of the host trees is impacted by decay due to the fungus, and structurally impaired by the larval galleries. U. albicornis can be carried in conifer wood, solid wood packaging material (SWPM) or plants for planting. Wood from North America is regulated by 2019/2072 (Annex VII) while SWPM is managed according to ISPM 15. The pathway plants for planting is largely closed by prohibition, with the exception of Thuja spp. Climatic conditions in several EU member states are conducive for establishment and the main host plants are widespread in those areas. Further spread and introduction of U. albicornis is likely to decrease the quality of host wood and may influence forest diversity by selectively affecting conifers. Phytosanitary measures are available to reduce the likelihood of additional entry and further spread, and there is a potential for biological control. Despite uncertainty regarding potential damage, U. albicornis satisfies all the criteria that are within the remit of EFSA to assess for it to be regarded as a potential Union quarantine pest.
Analysis of Risk Factors for Distant Metastasis of Pancreatic Ductal Adenocarcinoma without Regional Lymph Node Metastasis and a Nomogram Prediction Model for Survival

Background Negative regional lymph nodes do not indicate a lack of distant metastasis. A considerable number of patients with negative regional lymph node pancreatic cancer will skip the step of regional lymph node metastasis and directly develop distant metastasis. Methods We retrospectively analyzed the clinicopathological characteristics of patients with negative regional lymph node pancreatic cancer and distant metastasis in the Surveillance, Epidemiology, and End Results database from 2010 to 2015. Multivariate logistic analysis and Cox analysis were used to determine the independent risk factors that promoted distant metastasis and the 1-, 2-, and 3-year cancer-specific survival in this subgroup. Results Sex, age, pathological grade, surgery, radiotherapy, race, tumor location, and tumor size were significantly correlated with distant metastasis (P < 0.05). Among these factors, pathological grade II and above, tumor site other than the pancreatic head, and tumor size >40 mm were independent risk factors for distant metastasis; age ≥60 years, tumor size ≤21 mm, surgery, and radiation were protective factors against distant metastasis. Age, pathological grade, surgery, chemotherapy, and metastasis site were identified as predictors of survival. Among them, age ≥40 years, pathological grade II and above, and multiple distant metastasis were considered independent risk factors for cancer-specific survival. Surgery and chemotherapy were considered protective factors for cancer-specific survival. The prediction performance of the nomogram was significantly better than that of the traditional American Joint Committee on Cancer tumor, node, metastasis staging system. We also established an online dynamic nomogram calculator, which can predict the survival rate of patients at different follow-up time points. Conclusion Pathological grade, tumor location, and tumor size were independent risk factors for distant metastasis in pancreatic ductal adenocarcinoma with negative regional lymph nodes. Older age, smaller tumor size, surgery, and radiotherapy were protective factors against distant metastasis. A new nomogram that was constructed could effectively predict cancer-specific survival in pancreatic ductal adenocarcinoma with negative regional lymph nodes and distant metastasis. Furthermore, an online dynamic nomogram calculator was established.
Enumerating Steiner triple systems

Steiner triple systems (STSs) have been classified up to order 19. Earlier estimations of the number of isomorphism classes of STSs of order 21, the smallest open case, are discouraging as for classification, so it is natural to focus on the easier problem of merely counting the isomorphism classes. Computational approaches for counting STSs are here considered and lead to an algorithm that is used to obtain the number of isomorphism classes for order 21: 14,796,207,517,873,771.
The gravito-electromagnetic approximation to the gravimagnetic dipole and its velocity rotation curve

In view of the observed flat rotation curves of spiral galaxies and motivated by the simple fact that within Newtonian gravity a stationary axisymmetric mass distribution or dark matter vortex of finite extent readily displays a somewhat flattened out velocity rotation curve up to distances comparable to the extent of such a vortex transverse to the galaxy’s disk, the possibility that such a flattening out of rotation curves may rather be a manifestation of some stationary axisymmetric space-time curvature of purely gravitational character, without the need of some dark matter particles, is considered in the case of the gravimagnetic dipole carrying opposite Newman–Unti–Tamburino charges and in the tensionless limit of its Misner string, as an exact vacuum solution to Einstein’s equations. Aiming for a first assessment of the potential of such a suggestion easier than a full-fledged study of its geodesics, the situation is analysed within the limits of weak field gravito-electromagnetism and nonrelativistic dynamics. Thereby leading indeed to interesting and encouraging results.
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems

Deep learning models frequently suffer from various problems such as class imbalance and lack of robustness to distribution shift. It is often difficult to find data suitable for training beyond the available benchmarks. This is especially the case for computer vision models. However, with the advent of Generative Adversarial Networks (GANs), it is now possible to generate high-quality synthetic data. This synthetic data can be used to alleviate some of the challenges faced by deep learning models. In this work we present a detailed analysis of the effect of training computer vision models using different proportions of synthetic data along with real (organic) data. We analyze the effect that various quantities of synthetic data, when mixed with original data, can have on a model's robustness to out-of-distribution data and the general quality of predictions.
Specformer: Spectral Graph Neural Networks Meet Transformers

Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions. However, most existing spectral graph filters are scalar-to-scalar functions, i.e., mapping a single eigenvalue to a single filtered value, thus ignoring the global pattern of the spectrum. Furthermore, these filters are often constructed based on some fixed-order polynomials, which have limited expressiveness and flexibility. To tackle these issues, we introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter. We also design a decoder with learnable bases to enable non-local graph convolution. Importantly, Specformer is equivariant to permutation. By stacking multiple Specformer layers, one can build a powerful spectral GNN. On synthetic datasets, we show that our Specformer can better recover ground-truth spectral filters than other spectral GNNs. Extensive experiments of both node-level and graph-level tasks on real-world graph datasets show that our Specformer outperforms state-of-the-art GNNs and learns meaningful spectrum patterns. Code and data are available at https://github.com/bdy9527/Specformer.
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation

In recent times, monocular depth estimation (MDE) has experienced significant advancements in performance, largely attributed to the integration of innovative architectures, i.e., convolutional neural networks (CNNs) and Transformers. Nevertheless, the susceptibility of these models to adversarial attacks has emerged as a noteworthy concern, especially in domains where safety and security are paramount. This concern holds particular weight for MDE due to its critical role in applications like autonomous driving and robotic navigation, where accurate scene understanding is pivotal. To assess the vulnerability of CNN-based depth prediction methods, recent work tries to design adversarial patches against MDE. However, the existing approaches fall short of inducing a comprehensive and substantially disruptive impact on the vision system. Instead, their influence is partial and confined to specific local areas. These methods lead to erroneous depth predictions only within the overlapping region with the input image, without considering the characteristics of the target object, such as its size, shape, and position. In this paper, we introduce a novel adversarial patch named APARATE. This patch possesses the ability to selectively undermine MDE in two distinct ways: by distorting the estimated distances or by creating the illusion of an object disappearing from the perspective of the autonomous system. Notably, APARATE is designed to be sensitive to the shape and scale of the target object, and its influence extends beyond immediate proximity. APARATE, results in a mean depth estimation error surpassing $0.5$, significantly impacting as much as $99\%$ of the targeted region when applied to CNN-based MDE models. Furthermore, it yields a significant error of $0.34$ and exerts substantial influence over $94\%$ of the target region in the context of Transformer-based MDE.
Epidemic spreading on dynamical networks with temporary hubs and stable scale-free degree distribution

Recent empirical analyses of some realistic dynamical networks have demonstrated that their degree distributions are stable scale-free (SF), but the instantaneous well-connected hubs at one point of time can quickly become weakly connected. Motivated by these empirical results, we propose a simple toy dynamical agent-to-agent contact network model, in which each agent stays at one node of a static underlay network and the nearest neighbors swap their positions with each other. Although the degree distribution of the dynamical network model at any one time is equal to that in the static underlay network, the numbers and identities of each agent’s contacts will change over time. It is found that the dynamic interaction tends to suppress epidemic spreading in terms of larger epidemic threshold, smaller prevalence (the fraction of infected individuals) and smaller velocity of epidemic outbreak. Furthermore, the dynamic interaction results in the prevalence to undergo a phase transition at a finite threshold of the epidemic spread rate in the thermodynamic limit, which is in contradiction to the absence of an epidemic threshold in static SF networks. Some of these findings obtained from heterogeneous mean-field theory are in good agreement with numerical simulations.
Uses and Gratifications of Alternative Social Media: Why do people use Mastodon?

The primary purpose of this investigation is to answer the research questions; 1) What are users' motivations for joining Mastodon? ; 2) What are users' gratifications for using Mastodon? ; and 3) What are the primary reasons that the users continue to use Mastodon? We analyzed the collected data from the perspective of the Uses and Gratifications Theory. A questionnaire was designed to measure the opinions of Mastodon users from 15 different Mastodon instances. We examined 47 items through exploratory factor analysis using principal components extraction with Varimax with Kaiser Normalization. The results extracted 7 factors of gratification sought (expectation) and 7 factors of gratification obtained. We discovered that the primary reason that the users join and use Mastodon is the ease of controlling and sheltering users' information from data mining. The findings of the gratification sought structure are similar to findings of the gratification obtained structure, and the comparison between the two groups of data suggests that users are satisfied with the ongoing use of Mastodon.
Active Mass Distribution Estimation from Tactile Feedback

In this work, we present a method to estimate the mass distribution of a rigid object through robotic interactions and tactile feedback. This is a challenging problem because of the complexity of physical dynamics modeling and the action dependencies across the model parameters. We propose a sequential estimation strategy combined with a set of robot action selection rules based on the analytical formulation of a discrete-time dynamics model. To evaluate the performance of our approach, we also manufactured re-configurable block objects that allow us to modify the object mass distribution while having access to the ground truth values. We compare our approach against multiple baselines and show that our approach can estimate the mass distribution with around 10% error, while the baselines have errors ranging from 18% to 68%.
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates

We propose a new overidentifying restriction test for linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and/or instruments to be larger than the sample size and is robust to heteroskedastic errors. We show that the test has the desired theoretical properties under sparse high-dimensional models and is more powerful than existing overidentification tests. First, we introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power based on the maximum norm is shown to be higher than that in the modified Cragg-Donald test (Koles\'{a}r, 2018), which is the only existing test allowing for large-dimensional covariates. Second, following the principle of power enhancement (Fan et al., 2015), we introduce the power-enhanced test, with an asymptotically zero component used to enhance the empirical power against some extreme alternatives with many locally invalid instruments. Focusing on hypothesis testing, we also provide a feasible estimator of endogenous effects for practitioners when instrument validity is not rejected. The simulation results show the superior performance of the proposed test, and the empirical power enhancement is clear. Finally, an empirical example of the trade and economic growth nexus demonstrates the usefulness of the proposed tests.
Nonlinear Subsystem-Based Adaptive Impedance Control of Physical Human-Robot-Environment Interaction in Contact-Rich Tasks

Haptic upper limb exoskeletons are robots that assist human operators during task execution while having the ability to render virtual or remote environments. Therefore, ensuring the stability of such robots in physical human-robot-environment interaction (pHREI) is crucial. Having a wide range of Z-width, which indicates the region of passively renderable impedance by a haptic display, is also important for rendering a broad range of virtual environments. To address these issues, this study designs subsystem-based adaptive impedance control to achieve a stable pHREI for 7 degrees of freedom haptic exoskeleton. The presented controller decomposes the entire system into subsystems and designs the controller at the subsystem level. The stability of the controller in the presence of contact with a virtual environment and human arm force is proven by employing the concept of virtual stability. Additionally, the Z-width of the 7-DoF haptic exoskeleton is illustrated using experimental data and improved by exploiting varying virtual mass element. Experimental results are provided to demonstrate the performance of the controller. The control results are also compared to state-of-the-art control methods, highlighting the excellence of the designed controller.
STRUCTURE-BORNE SOUND POWER AND SOURCE CHARACTERIZATION IN MULTI-POINT-CONNECTED SYSTEMS. PART 3: FORCE RATIO ESTIMATES

Abstract A fundamental problem in the field of structure-borne sound is the characterization of dynamic sources with respect to vibrational power. Though one of the proposed approaches based upon the ability of a source to deliver power is promising, it necessitates estimating the spatial distribution of the forces at the multiple contacts between the source and receiver structure. Hence, by way of both an analytical and a statistical study, in this paper methods are investigated through which the force ratios manifested in a system can be estimated. The analytical study considers methods of estimating the force ratios absolutely whilst the statistical study considers their statistical domain. For the analytical study all attempts in estimating failed under the condition of approximately equal point and transfer mobilities. With respect to the statistical part of the study, the mobilities and free velocities in a system are considered as “populations” and grouped functions are introduced to describe their distributions. Based upon these grouped functions, simple expressions are derived for the statistical distribution of the force ratios. A generalized approach is used throughout so that the work is applicable to many systems.
Photovoltaic Panel Defect Detection Based on Ghost Convolution with BottleneckCSP and Tiny Target Prediction Head Incorporating YOLOv5

Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of traditional worker field inspections. However, multiple tiny defects on the PV panel surface and the high similarity between different defects make it challenging to {accurately identify and detect such defects}. This paper proposes an approach named Ghost convolution with BottleneckCSP and a tiny target prediction head incorporating YOLOv5 (GBH-YOLOv5) for PV panel defect detection. To ensure better accuracy on multiscale targets, the BottleneckCSP module is introduced to add a prediction head for tiny target detection to alleviate tiny defect misses, using Ghost convolution to improve the model inference speed and reduce the number of parameters. First, the original image is compressed and cropped to enlarge the defect size physically. Then, the processed images are input into GBH-YOLOv5, and the depth features are extracted through network processing based on Ghost convolution, the application of the BottleneckCSP module, and the prediction head of tiny targets. Finally, the extracted features are classified by a Feature Pyramid Network (FPN) and a Path Aggregation Network (PAN) structure. Meanwhile, we compare our method with state-of-the-art methods to verify the effectiveness of the proposed method. The proposed PV panel surface-defect detection network improves the mAP performance by at least 27.8%.
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition

The awareness for biased ASR datasets or models has increased notably in recent years. Even for English, despite a vast amount of available training data, systems perform worse for non-native speakers. In this work, we improve an accent-conversion model (ACM) which transforms native US-English speech into accented pronunciation. We include phonetic knowledge in the ACM training to provide accurate feedback about how well certain pronunciation patterns were recovered in the synthesized waveform. Furthermore, we investigate the feasibility of learned accent representations instead of static embeddings. Generated data was then used to train two state-of-the-art ASR systems. We evaluated our approach on native and non-native English datasets and found that synthetically accented data helped the ASR to better understand speech from seen accents. This observation did not translate to unseen accents, and it was not observed for a model that had been pre-trained exclusively with native speech.
Predicting Motion Plans for Articulating Everyday Objects

Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet seat require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce $\mathbf{SeqIK}+\theta_{0}$, a fast and flexible representation for motion plans. Finally, we learn models that use $\mathbf{SeqIK}+\theta_{0}$ to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods.
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series

The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of metrics, which vary widely in their properties. Despite the existence of new evaluation metrics, there is limited agreement on which metrics are best suited for specific scenarios and domains, and the most commonly used metrics have faced criticism in the literature. This paper provides a comprehensive overview of the metrics used for the evaluation of time series anomaly detection methods, and also defines a taxonomy of these based on how they are calculated. By defining a set of properties for evaluation metrics and a set of specific case studies and experiments, twenty metrics are analyzed and discussed in detail, highlighting the unique suitability of each for specific tasks. Through extensive experimentation and analysis, this paper argues that the choice of evaluation metric must be made with care, taking into account the specific requirements of the task at hand.