Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of axial contact stiffness of a double-nut with preloads

Ball screw drives are commonly used to provide linear motion in machine tools. And stiffness is one of the most important performance indexes. However, stiffness of nuts with different preloads is difficult to be calculated precisely because of complex structures. In order to improve the calculating accuracy, a new model is proposed with the consideration of geometry errors of grooves and balls based on the existing theoretical model. The influence of geometry errors on axial deformation of double-nut is analyzed and modeled. Meanwhile, a preload-adjustable ball screw drive is constructed on the basis of a modified double-nut mechanism. A novel loading mechanism is designed to apply axial load on the working table and test the force in real time. Two laser displacement sensors are adopted to test axial deformation of the double-nut. The axial stiffness of the double-nut is analyzed based on the axial load and the axial deformation. Stiffness simulations of the new improved model, the theoretical model, and the empirical model are also analyzed. The contrastive analysis shows that the experimental results agree much better with the axial stiffness calculated by the new improved model. This study provides a more accurate model to calculate the stiffness of the double-nut with preloads for preloaded ball screw drives.
Can Pre-trained Models Really Generate Single-Step Textual Entailment?

We investigate the task of generating textual 001 entailment (GTE). Different from prior works 002 on recognizing textual entailment, also known 003 as NLI, GTE requires the models with deeper 004 reasoning capabilities - generating entailment 005 from premises rather than making prediction 006 on given premises and the entailment. We ar-007 gue that existing adapted datasets are limited 008 and inadequate to train and evaluate human-009 like reasoning in the GTE. In this paper, we 010 propose a new large-scale benchmark, named 011 SEG , targeted for learning and evaluating mod-012 els’ capabilities towards RTE. SEG consists of 013 15k instances with each containing a pair of 014 premise statements and a human-annotated en-015 tailment. It is constructed by ﬁrst retrieving 016 instances from a knowledge base, and then 017 augmenting each instance with several comple-018 mentary instances by 7 manually crafted trans-019 formations. We demonstrate that even exten-020 sively ﬁne-tuned pre-trained models perform 021 poorly on SEG . The best baseline can only gen-022 erate valid textual entailment for 59.1% cases. 023 Further, to motivate future advances, we pro-024 vide detailed analysis to show signiﬁcant gaps 025 between baselines and human performance. 026
The combined use of Building Information Modelling (BIM) and Unmanned Aerial Vehicle (UAV) technologies for the 3D illustration of the progress of works in infrastructure construction projects

Building Information Modelling (BIM) technology is already part of the construction industry and is considered by professionals as a very useful tool for all phases of a construction project. BIM technology, with the particularly useful 3D illustrations which it provides, can be used to illustrate and monitor the progress of works effectively through the entire lifetime of the project. Unmanned Aerial Vehicles (UAVs) have undergone significant advances in equipment capabilities and now have the capacity to acquire high resolution imagery from different angles in a cost effective and efficient manner. By using photogrammetry, characteristics such as distances, areas, volumes, elevations, object sizes, and object shape can be determined within overlapping areas. This paper explores the combined use of BIM and UAV technologies in order to achieve efficient and accurate as-built data collection and 3D illustrations of the works progress during an infrastructure construction project.
Risk-related perceptions for rural household decisions to grow Vitamin A biofortified sweetpotatoes in Uganda

The vitamin A bio-fortified sweetpotato, commonly known as the Orange-Fleshed Sweetpotato (OFSP) is an important food for fighting vitamin A deficiency in Uganda, particularly in rural households, although its use remains low. This study investigated the role of risk perceptions in rural households’ decisions to grow OFSP. Data were derived from a cross-sectional questionnaire survey of 341 male and female household heads randomly selected from two rural districts of Uganda. Scaling and factor analyses verified data reliability and construct validity, after which multiple regression analyses tested hypothesized relationships. Gender differences were tested. Results found that risk perceptions encouraged rural household heads to grow the OFSP (F=3.937, p<.001, Adjusted R 2 = 0.102). Further, perceived risks were motivators and perceived effectiveness of control might be the feasible path to sustained cultivation. Although male and female farmers differed, a stronger estimation was obtained when the entire sample was tested. In sum, information dissemination programmes targeting household heads using media campaigns should disseminate messages to at-risk households regarding the feasibility of OFSP cultivation and consumption to reduce vitamin A deficiency.
GESTURE RECOGNITION TECHNOLOGY FOR INTERPRETING HUMAN GESTURE VIA MATHEMATICAL ALGORITHMS

: Gesture recognition is a topic in computer science and language technology with the goal of using mathematical algorithms to analyze human gestures. Gestures can come from any physical move or state; however, they are most typically made with the hands or the face. Emotion identification from the face and hand gesture recognition are two of the field's current hot topics. Cameras and computer vision algorithms have been used in a variety of ways to read sign language. Gesture recognition techniques, on the other hand, are used to identify and recognize posture, gait, proxemics, and human behaviors. Gesture recognition can be seen of as a means for computers to begin to grasp human body language, creating a richer bridge between machines and humans than simple text user interfaces or even graphical user interfaces (graphical user interfaces). Gesture recognition technology aims to develop a system that can recognize certain human movements and utilize them to convey information or control devices. Gestures of the human body, especially hand movements, are used to interact with computers. A camera analyses the movements of the human body and sends the information to a computer, which then uses the gestures as input to operate objects or apps.
CFD Based Analysis and Parametric Study of a Novel Wind Turbine Design: the Dual Vertical Axis Wind Turbine

Small Vertical Axis Wind Turbines (VAWTs) are good candidates to extract energy from wind in urban areas because they are easy to install, service and do not generate much noise; however, the aerodynamic efficiency of small turbines is low. Here-in a new turbine, with high aerodynamic efficiency, is proposed. The novel design is based on the classical H-Darrieus VAWT. VAWTs produce the highest power when the blade chord is perpendicular to the incoming wind direction. The basic idea behind the proposed turbine is to extend that said region of maximum power by having the blades continue straight instead of following a circular path. This motion can be performed if the blades turn along two axes; hence it was named Dual Vertical Axis Wind Turbine (D-VAWT). The analysis of this new turbine is done through the use of Computational Fluid Dynamics (CFD) with 2D and 3D simulations. While 2D is used to validate the methodology, 3D is used to get an accurate estimate of the turbine performance. The analysis of a single blade is performed and the turbine shows that a power coefficient of 0.4 can be achieved. So far, reaching performance levels high enough to compete with the most efficient VAWTs. The D-VAWT is still far from full optimization, but the analysis presented here shows the hidden potential and serves as proof of concept. The study of the D-VAWT is concluded with a preliminary parametric study of the turbine sensitivity to different incoming wind angles, turbine axes spacing, number of blades, airfoil profile and blade mounting point.
Regional gradients in intraspecific seed mass variation are associated with species biotic attributes and niche breadth

Abstract Quantifying intraspecific trait variation (ITV) is crucial for understanding species local adaptation and regional distribution. Intraspecific seed mass variation (ITVsm) is expected to vary with environmental gradients or co-vary with related biotic attributes, but these relationships are not well known in a multispecies space. We performed interspecific and phylogenetic analyses to evaluate the relative power of three species biotic attributes and four niche breadth traits in explaining ITVsm variation for 434 eastern Qinghai–Tibetan species. We showed a positive relationship between species’ ITVsm and their niche breadth in the light, moisture and disturbance dimensions, supporting the idea that high ITV allows species to match their traits to different habitat conditions and thus to distribute in a wide range of environments. However, we did find significant direct effect of species’ thermal niche on individual seed mass variation. Meanwhile, we showed significant effects of seed dispersal mode, but not of life form and pollination type, on ITVsm. This suggests that the covariation or co-evolution between seed and disperser was related to the pattern and magnitude of ITVsm, but not to plant lifespan, the quality and allocation pattern of available resources and the availability of pollination vector. Lastly, all multivariate models showed a significant combined contribution of species’ biotic attributes and niche breadth to their ITVsm, implying that intrinsic biotic limitations and extrinsic abiotic pressures may operate simultaneously in controlling regional-scale intraspecific seed development.
Development of an amphibian sperm biobanking protocol for genetic management and population sustainability

Sperm cryopreservation is a crucial tool in amphibian assisted reproductive technologies that aids in population and genetic management of at-risk species. Using Anaxyrus fowleri as a model species, this study found that fertilization success and embryonic development are dependent on the cryoprotectant and freezing rate applied to sperm during cryopreservation.
Analyzing the effects of Cis-elements and trans-factors on the stability of the Gal1 mRNP

The highly dynamic and nonrandom spatial organization of the eukaryotic nucleus plays an important role in the regulation of gene expression. For example, in S. cerevisiae, several conditionally expressed genes relocate to the nuclear periphery upon activation. Moreover, these genes can be retained at the nuclear periphery for a considerable time after transcriptional shutoff. Sequence specific DNA binding proteins, transcription, chromatin remodeling, and mRNP quality control factors have all been implicated in perinuclear gene repositioning, but their relative contributions to the events of gene recruitment, capture and retention at the periphery remain unresolved. Sus1 is a conserved eukaryotic protein involved in transcription, mRNA export and perinuclear gene repositioning. Here, we show that the functions of Sus1p in perinuclear repositioning of GAL genes and its chromatin-linked functions can be genetically uncoupled, and that the role of Sus1p in the retention of mRNA in gene-proximal foci is chromatin-independent. Histone variant H2A.Z, likewise has been suggested to play a role in the post-transcriptional association of the yeast genes with the nuclear periphery. Our findings indicate that the loss of H2A.Z doesn’t alter gene-proximal mRNA retention, but has a differential effect on perinuclear repositioning of GAL genes.
Activity Identification for Gait Recognition Using Mobile Devices

The need to increase the security measures and make the users of different devices feel safer has led the researchers focus on finding new security mechanisms with better performance. A field where not much has been done so far is finding a solution is keeping the data secured by ensuring that only the authorized user can access the data. Even though gait recognition is an important element in this project and in the entire authentication process the issue is that it is not the only element in a successful implementation of the project. This is due to different routine activities people do daily. A solution to this issue could be activity recognition that would reduce the disadvantages of gait recognition by identifying the activities of a person continuously. Activity recognition would not only make it possible to authenticate the user in different daily activities like: slow walking, normal walking, fast walking even running, but it would also help in avoiding authentication when the user is in passive state like: sitting, standing still, etc. This is one of the key factors that motivated us in making our choice since it is an interesting challenge and it would be benefiting in the data security area if not revolutionized it. This project work has resulted in two publications papers. One paper has been accepted and attached in the appendix. The other paper is submitted and under review.
Guiding Vector Fields for Following Occluded Paths

Accurately following a geometric desired path in a two-dimensional (2-D) space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this article, we develop a composite guiding vector field via the use of smooth bump functions and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with obstacles of arbitrary shapes. These two behaviors are reactive since path (re)planning and global map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. Simulations are conducted to support the theoretical results.
SOPHIE: Soft and Flexible Aerial Vehicle for Physical Interaction With the Environment

This letter presents the first design of a soft and lightweight UAV, entirely 3D-printed in flexible filament. The drone's flexible arms are equipped with a tendon-actuated bending system, which is used for applications that require physical interaction with the environment. The flexibility of the UAV can be controlled during the additive manufacturing process by adjusting the infill rate <inline-formula><tex-math notation="LaTeX">$\rho _{TPU}$</tex-math></inline-formula> distribution. However, the increase in flexibility implies difficulties in controlling the UAV, as well as structural, aerodynamic, and aeroelastic effects. This article provides insight into the dynamics of the system and validates the flyability of the vehicle for densities as low as 6<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. Within this range, quasi-static arm deformations can be considered, thus the autopilot is fed back through a static arm deflection model. At lower densities, strong non-linear elastic dynamics appear, which translates to complex modeling, and it is suggested to switch to data-based approaches. Moreover, this work demonstrates the ability of the soft UAV to perform full-body perching, specifically landing and stabilizing on pipelines and irregular surfaces without the need for an auxiliary system.
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning

Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. However, poor feedback-efficiency still remains a problem in current preference-based RL algorithms, as tailored human feedback is very expensive. To handle this issue, previous methods have mainly focused on improving query selection and policy initialization. At the same time, recent exploration methods have proven to be a recipe for improving sample-efficiency in RL. We present an exploration method specifically for preference-based RL algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Specifically, we utilize disagreement across ensemble of learned reward models. Our intuition is that disagreement in learned reward model reflects uncertainty in tailored human feedback and could be useful for exploration. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms on complex robot manipulation tasks from MetaWorld benchmarks, compared with other existing exploration methods that measure the novelty of state visitation.
A weighted $L_p$-regularity theory for parabolic partial differential equations with time measurable pseudo-differential operators

We obtain the existence, uniqueness, and regularity estimates of the following Cauchy problem \begin{equation}\label{ab eqn} \begin{cases} \partial_t u(t,x)=\psi(t,-i\nabla)u(t,x)+f(t,x),\quad&(t,x)\in(0,T)\times\mathbb{R}^d,\\ u(0,x)=0,\quad&x\in\mathbb{R}^d \end{cases} \end{equation} in (Muckenhoupt) weighted $L_p$-spaces with time-measurable pseudo-differential operators \begin{equation} \label{ab op} \psi(t,-i\nabla)u(t,x):=\mathcal{F}^{-1}\left[\psi(t,\cdot)\mathcal{F}[u](t,\cdot)\right](x). \end{equation} More precisely, we find sufficient conditions of the symbol $\psi(t,\xi)$ (especially depending on the smoothness of the symbol with respect to $\xi$) to guarantee that equation is well-posed in (Muckenhoupt) weighted $L_p$-spaces. Here the symbol $\psi(t,\xi)$ is merely measurable with respect to $t$, and the sufficient smoothness of $\psi(t,\xi)$ with respect to $\xi$ is characterized by a property of each weight. In particular, we prove the existence of a positive constant $N$ such that for any solution $u$ to the equation, \begin{equation} \label{ab est} \int_0^T \int_{\mathbb{R}^d} |(-\Delta)^{\gamma/2} u(t,x) |^p (t^2 + |x|^2)^{\alpha/2} \mathrm{d}x\mathrm{d}t \leq N\int_0^T \int_{\mathbb{R}^d} |f(t,x)|^p (t^2 + |x|^2)^{\alpha/2} \mathrm{d}x\mathrm{d}t \end{equation} and \begin{equation} \label{ab est 2} \int_0^T \left(\int_{\mathbb{R}^d} |(-\Delta)^{\gamma/2} u(t,x) |^p |x|^{\alpha_2} \mathrm{d}x \right)^{q/p} t^{\alpha_1}\mathrm{d}t \leq N\int_0^T \left(\int_{\mathbb{R}^d} |f(t,x) |^p |x|^{\alpha_2} \mathrm{d}x \right)^{q/p} t^{\alpha_1}\mathrm{d}t, \end{equation} where $p,q\in(1,\infty)$, $-d-1<\alpha<(d+1)(p-1)$, $-1<\alpha_1
Open-Domain Sign Language Translation Learned from Online Video

Existing work on sign language translation – that is, translation from sign language videos into sentences in a written language – has focused mainly on (1) data collected in a controlled environment or (2) data in a specific domain, which limits the applicability to real-world settings. In this paper, we introduce OpenASL, a large-scale American Sign Language (ASL) - English dataset collected from online video sites (e.g., YouTube). OpenASL contains 288 hours of ASL videos in multiple domains from over 200 signers and is the largest publicly available ASL translation dataset to date. To tackle the challenges of sign language translation in realistic settings and without glosses, we propose a set of techniques including sign search as a pretext task for pre-training and fusion of mouthing and handshape features. The proposed techniques produce consistent and large improvements in translation quality, over baseline models basedon prior work.
Maximising the Influence of Temporary Participants in Opinion Formation

DeGroot-style opinion formation presumes a continuous interaction among agents of a social network. Hence, it cannot handle agents external to the social network that interact only temporarily with the permanent ones. Many real-world organisations and individuals fall into such a category. For instance, a company tries to persuade as many as possible to buy its products and, due to various constraints, can only exert its influence for a limited amount of time. We propose a variant of the DeGroot model that allows an external agent to interact with the permanent ones for a preset period of time. We obtain several insights on maximising an external agent's influence in opinion formation by analysing and simulating the variant.
Measurement Incompatibility versus Bell Nonlocality: An Approach via Tensor Norms

Measurement incompatibility and quantum non-locality are two key features of quantum theory. Violations of Bell inequalities require quantum entanglement and incompatibility of the measurements used by the two parties involved in the protocol. We analyze the converse question: for which Bell inequalities is the incompatibility of measurements enough to ensure a quantum violation? We relate the two questions by comparing two tensor norms on the space of dichotomic quantum measurements: one characterizing measurement compatibility and the second one characterizing violations of a given Bell inequality. We provide sufficient conditions for the equivalence of the two notions in terms of the matrix describing the correlation Bell inequality. We show that the CHSH inequality and its variants are the only ones satisfying it.
Boosting Tail Neural Network for Realtime Custom Keyword Spotting

In this paper, we propose a Boosting Tail Neural Network (BTNN) for improving the performance of Realtime Custom Keyword Spotting (RCKS) that is still an industrial challenge for demanding powerful classification ability with limited computation resources. Inspired by Brain Science that a brain is only partly activated for a nerve simulation and numerous machine learning algorithms are developed to use a batch of weak classifiers to resolve arduous problems, which are often proved to be effective. We show that this method is helpful to the RCKS problem. The proposed approach achieve better performances in terms of wakeup rate and false alarm. In our experiments compared with those traditional algorithms that use only one strong classifier, it gets 18\% relative improvement. We also point out that this approach may be promising in future ASR exploration.
Amortized Inference for Causal Structure Learning

Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is difficult. In this work, we propose to amortize causal structure learning. Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data. This allows our inference model to acquire domain-specific inductive biases for causal discovery solely from data generated by a simulator, bypassing both the hand-engineering of suitable score functions and the search over graphs. The architecture of our inference model emulates permutation invariances that are crucial for statistical efficiency in structure learning, which facilitates generalization to significantly larger problem instances than seen during training. On synthetic data and semisynthetic gene expression data, our models exhibit robust generalization capabilities when subject to substantial distribution shifts and significantly outperform existing algorithms, especially in the challenging genomics domain. Our code and models are publicly available at: https://github.com/larslorch/avici.