Miracle-dz/UniFAR-FTUs huggingface dataset:
An Application of Deep Learning for Sweet Cherry Phenotyping using YOLO Object Detection

Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3. A system capable of analyzing the image data for other traits such as size and color was also developed using Python. The YOLO model obtained close to 99% accuracy in object detection and counting of cherries and 90% on the Intersection over Union metric for object localization when extracting size and colour information. The model surpasses human performance and offers a significant improvement compared to manual counting.
Adversarial Path Planning for Optimal Camera Positioning

The use of visual sensors is flourishing, driven among others by the several applications in detection and prevention of crimes or dangerous events. While the problem of optimal camera placement for total coverage has been solved for a decade or so, that of the arrangement of cameras maximizing the recognition of objects"in-transit"is still open. The objective of this paper is to attack this problem by providing an adversarial method of proven optimality based on the resolution of Hamilton-Jacobi equations. The problem is attacked by first assuming the perspective of an adversary, i.e. computing explicitly the path minimizing the probability of detection and the quality of reconstruction. Building on this result, we introduce an optimality measure for camera configurations and perform a simulated annealing algorithm to find the optimal camera placement.
Model-Based Underwater 6D Pose Estimation From RGB

Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, however, the data is not clear and the sensors expensive. For this reason, the community has focused on extracting pose estimates from RGB input. In this work, we propose an approach that leverages 2D object detection to reliably compute 6D pose estimates in different underwater scenarios. We test our proposal with 4 objects with symmetrical shapes and poor texture spanning across <inline-formula><tex-math notation="LaTeX">$33{,}920$</tex-math></inline-formula> synthetic and 10 real scenes. All objects and scenes are made available in an open-source dataset that includes annotations for object detection and pose estimation. When benchmarking against similar end-to-end methodologies for 6D object pose estimation, our pipeline provides estimates that are <inline-formula><tex-math notation="LaTeX">$\sim \! 8{\%}$</tex-math></inline-formula> more accurate. We also demonstrate the real-world usability of our pose estimation pipeline on an underwater robotic manipulator in a reaching task.
Depopulation of the Ag(111) Surface State Assigned to Strain in Epitaxial Films

Photoemission spectra from epitaxial Ag(111) films on Si(111)-( $7\ifmmode\times\else\texttimes\fi{}7$) show that the $\overline{\ensuremath{\gamma}}$ surface state in Ag(111) is depopulated in films deposited on the silicon surface when compared with ideal Ag(111) surfaces. This is interpreted as a shift of the surface state up in energy, beyond the Fermi level, since it can be partly reversed by a lowering of the work function, in accordance with phase-accumulation model calculations. This shift is ascribed, through band structure calculations, to the strain in the Ag layers induced by film growth.
First-Order Statistical Framework for Multi-Channel Passive Detection

In this paper we establish a general first-order statistical framework for the detection of a common signal impinging on spatially distributed receivers. We consider three types of channel models: 1) the propagation channel is completely known, 2) the propagation is known but channel gains are unknown, and 3) the propagation channel is unknown. For each problem, we address the cases of a) known noise variances, b) common but unknown noise variances, and c) different and unknown noise variances. For all 9 cases, we establish generalized-likelihood-ratio (GLR) detectors, and show that each one can be decomposed into two terms. The first term is a weighted combination of the GLR detectors that arise from considering each channel separately. This result is then modified by a fusion or cross-validation term, which expresses the level of confidence that the single-channel detectors have detected a common source. Of particular note are the constant false-alarm rate (CFAR) detectors that allow for scale-invariant detection in multiple channels with different noise powers.
Embedded quantum correlations in thermalized quantum Rabi systems

We study the quantum correlations embedded in open quantum Rabi systems. Specifically, we study how the quantum correlation depends on the coupling strength, number of qubits, and reservoir temperatures. We numerically calculate the quantum correlations of up to three qubits interacting with a single field mode. We find that the embedded quantum correlations exhibit a maximum for a given coupling strength, which depends inversely on the number of subsystems and the reservoir temperature. We explore how this feature affects the performance of a many-qubit Otto heat engine, finding numerical evidence of a direct correspondence between the minimum of the extractable work and the maximum of the embedded quantum correlations in the qubit-cavity bi-partition. Furthermore, as we increase the number of qubits, the maximum extractable work is reached at smaller values of the coupling strength. This work could help design more sophisticated quantum heat engines that rely on many-body systems with embedded correlations as working substances.
DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii<2 R(Earth) or periods<1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol \citep{liu2022pykt}, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 \citep{minn2018deep,yeung2018addressing}. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named \textsc{simpleKT}. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}. }.
Rank-preserving additions for topological vector bundles, after a construction of Horrocks

We produce group structures on certain sets of topological vector bundles of fixed rank. In particular, we put a group structure on complex rank $2$ bundles on $\mathbb{C}P^3$ with fixed first Chern class. We show that this binary operation coincides with a construction on locally free sheaves due to Horrocks, provided Horrocks' construction is defined. Using similar ideas, we give group structures on certain sets of rank $3$ bundles on $\mathbb{C}P^5$. These groups arise from the study of relative infinite loop space structures on truncated diagrams. Specifically, we show that the $(2n-2)$-truncation of an $n$-connective map $X\to Y$ with a section is a highly structured group object over the $(2n-2)$-truncation of $Y$. Applying these results to classifying spaces yields the group structures of interest.
An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film’s electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi2Te3, at high temperatures up to 500 K. Working reliably and precisely, the in-situ measurement system would help to study physics during film growth or speedup our search for better thermoelectric materials.
A lightweight method for detecting dynamic target occlusions by the robot body

Robot vision is greatly affected by occlusions, which poses challenges to autonomous systems. The robot itself may hide targets of interest from the camera, while it moves within the field of view, leading to failures in task execution. For example, if a target of interest is partially occluded by the robot, detecting and grasping it correctly, becomes very challenging. To solve this problem, we propose a computationally lightweight method to determine the areas that the robot occludes. For this purpose, we use the Unified Robot Description Format (URDF) to generate a virtual depth image of the 3D robot model. Using the virtual depth image, we can effectively determine the partially occluded areas to improve the robustness of the information given by the perception system. Due to the real-time capabilities of the method, it can successfully detect occlusions of moving targets by the moving robot. We validate the effectiveness of the method in an experimental setup using a 6-DoF robot arm and an RGB-D camera by detecting and handling occlusions for two tasks: Pose estimation of a moving object for pickup and human tracking for robot handover. The code is available in \url{https://github.com/auth-arl/virtual\_depth\_image}.
Primordial black holes and scalar-induced gravitational waves from the perturbations on the inflaton potential in peak theory

A perturbation on the background inflaton potential can lead inflation into the ultraslow-roll stage and can thus remarkably enhance the power spectrum ${\cal P}_{\cal R}(k)$ of the primordial curvature perturbation on small scales. Such an enhanced ${\cal P}_{\cal R}(k)$ will result in primordial black holes (PBHs), contributing a significant fraction of dark matter, and will simultaneously generate sizable scalar-induced gravitational waves (SIGWs) as a secondorder effect. In this work, we calculate the PBH abundances $f_{\rm PBH}(M)$ and SIGW spectra $\Omega_{\rm GW}(f)$ in peak theory. We obtain the PBHs with desirable abundances in one or two typical mass windows at $10^{-17}\, M_\odot$, $10^{-13}\, M_\odot$, and $30\, M_\odot$, respectively. At the same time, the relevant SIGWs are expected to be observed by the next-generation gravitational wave detectors, without spoiling the current constraint. Especially, the SIGW associated with the PBH of $30\, M_\odot$ can also interpret the potential isotropic stochastic gravitational wave background from the NANOGrav 12.5-year dataset.
A Wasserstein distance-based spectral clustering method for transaction data analysis

With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing merchants' behavior patterns. This enables payment platforms to provide differentiated services or implement risk management strategies. However, traditional methods exploit transactions by generating low-dimensional features, leading to inevitable information loss. In this study, we use the empirical cumulative distribution of transactions to characterize merchants. We adopt Wasserstein distance to measure the dissimilarity between any two merchants and propose the Wasserstein-distance-based spectral clustering (WSC) approach. Based on the similarities between merchants' transaction distributions, a graph of merchants is generated. Thus, we treat the clustering of merchants as a graph-cut problem and solve it under the framework of spectral clustering. To ensure feasibility of the proposed method on large-scale datasets with limited computational resources, we propose a subsampling method for WSC (SubWSC). The associated theoretical properties are investigated to verify the efficiency of the proposed approach. The simulations and empirical study demonstrate that the proposed method outperforms feature-based methods in finding behavior patterns of merchants.
GenAug: Retargeting behaviors to unseen situations via Generative Augmentation

Robot learning methods have the potential for widespread generalization across tasks, environments, and objects. However, these methods require large diverse datasets that are expensive to collect in real-world robotics settings. For robot learning to generalize, we must be able to leverage sources of data or priors beyond the robot's own experience. In this work, we posit that image-text generative models, which are pre-trained on large corpora of web-scraped data, can serve as such a data source. We show that despite these generative models being trained on largely non-robotics data, they can serve as effective ways to impart priors into the process of robot learning in a way that enables widespread generalization. In particular, we show how pre-trained generative models can serve as effective tools for semantically meaningful data augmentation. By leveraging these pre-trained models for generating appropriate"semantic"data augmentations, we propose a system GenAug that is able to significantly improve policy generalization. We apply GenAug to tabletop manipulation tasks, showing the ability to re-target behavior to novel scenarios, while only requiring marginal amounts of real-world data. We demonstrate the efficacy of this system on a number of object manipulation problems in the real world, showing a 40% improvement in generalization to novel scenes and objects.
Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

Mixed-precision quantization (MPQ) suffers from time-consuming policy search process ( i.e., the bit-width assignment for each layer) on large-scale datasets ( e.g., ISLVRC-2012), which heavily limits its practicability in real-world deployment scenarios. In this paper, we propose to search the effective MPQ policy by using a small proxy dataset for the model trained on a large-scale one. It breaks the routine that requires a consistent dataset at model training and MPQ policy search time, which can improve the MPQ searching efﬁciency signiﬁcantly. However, the discrepant data distributions bring difﬁculties in searching for such a transferable MPQ policy. Motivated by the observation that quantization narrows the class margin and blurs the decision boundary, we search the policy that guarantees a general and dataset-independent property: discriminability of feature representations . Namely, we seek the policy that can robustly keep the intra-class compactness and inter-class separation. Our method offers several advantages, i.e., high proxy data utilization, no extra hyper-parameter tuning for approximating the relationship between full-precision and quantized model and high searching efﬁciency. We search high-quality MPQ policies with the proxy dataset that has only 4% of the data scale compared to the large-scale target dataset, achieving the same accuracy as searching directly on the latter, and improving the MPQ searching efﬁciency by up to 300 × .
Random boundaries: quantifying segmentation uncertainty in solutions to boundary-value problems

Engineering simulations using boundary-value partial differential equations often implicitly assume that the uncertainty in the location of the boundary has a negligible impact on the output of the simulation. In this work, we develop a novel method for describing the geometric uncertainty in image-derived models and use a naive method for subsequently quantifying a simulation's sensitivity to that uncertainty. A Gaussian random field is constructed to represent the space of possible geometries, based on image-derived quantities such as pixel size, which can then be used to probe the simulation's output space. The algorithm is demonstrated with examples from biomechanics where patient-specific geometries are often segmented from low-resolution, three-dimensional images. These examples show the method's wide applicability with examples using linear elasticity and fluid dynamics. We show that important biomechanical outputs of these example simulations, namely maximum principal stress and wall shear stress, can be highly sensitive to realistic uncertainties in geometry.
Are sketch-and-precondition least squares solvers numerically stable?

Sketch-and-precondition techniques are efficient and popular for solving large least squares (LS) problems of the form $Ax=b$ with $A\in\mathbb{R}^{m\times n}$ and $m\gg n$. This is where $A$ is ``sketched"to a smaller matrix $SA$ with $S\in\mathbb{R}^{\lceil cn\rceil\times m}$ for some constant $c>1$ before an iterative LS solver computes the solution to $Ax=b$ with a right preconditioner $P$, where $P$ is constructed from $SA$. Prominent sketch-and-precondition LS solvers are Blendenpik and LSRN. We show that the sketch-and-precondition technique in its most commonly used form is not numerically stable for ill-conditioned LS problems. For provable and practical backward stability and optimal residuals, we suggest using an unpreconditioned iterative LS solver on $(AP)z=b$ with $x=Pz$. Provided the condition number of $A$ is smaller than the reciprocal of the unit round-off, we show that this modification ensures that the computed solution has a backward error comparable to the iterative LS solver applied to a well-conditioned matrix. Using smoothed analysis, we model floating-point rounding errors to argue that our modification is expected to compute a backward stable solution even for arbitrarily ill-conditioned LS problems. Additionally, we provide experimental evidence that using the sketch-and-solve solution as a starting vector in sketch-and-precondition algorithms (as suggested by Rokhlin and Tygert in 2008) should be highly preferred over the zero vector. The initialization often results in much more accurate solutions -- albeit not always backward stable ones.
Bounds on Depth of Decision Trees Derived from Decision Rule Systems

Systems of decision rules and decision trees are widely used as a means for knowledge representation, as classifiers, and as algorithms. They are among the most interpretable models for classifying and representing knowledge. The study of relationships between these two models is an important task of computer science. It is easy to transform a decision tree into a decision rule system. The inverse transformation is a more difficult task. In this paper, we study unimprovable upper and lower bounds on the minimum depth of decision trees derived from decision rule systems depending on the various parameters of these systems.
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals

Non-technical end-users are silent and invisible users of the state-of-the-art explainable artificial intelligence (XAI) technologies. Their demands and requirements for AI explainability are not incorporated into the design and evaluation of XAI techniques, which are developed to explain the rationales of AI decisions to end-users and assist their critical decisions. This makes XAI techniques ineffective or even harmful in high-stakes applications, such as healthcare, criminal justice, finance, and autonomous driving systems. To systematically understand end-users' requirements to support the technical development of XAI, we conducted the EUCA user study with 32 layperson participants in four AI-assisted critical tasks. The study identified comprehensive user requirements for feature-, example-, and rule-based XAI techniques (manifested by the end-user-friendly explanation forms) and XAI evaluation objectives (manifested by the explanation goals), which were shown to be helpful to directly inspire the proposal of new XAI algorithms and evaluation metrics. The EUCA study findings, the identified explanation forms and goals for technical specification, and the EUCA study dataset support the design and evaluation of end-user-centered XAI techniques for accessible, safe, and accountable AI.