Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Strawberry Picking Robotic System (Robofruit)

Challenges in strawberry picking made selective harvesting robotic technology very demand-ing. However, the selective harvesting of strawberries is a complicated robotic task forming a few scientiﬁc research questions. Most available solutions only deal with a speciﬁc picking scenario, e.g., picking only a single variety of fruit in isolation. Nonetheless, most econom-ically viable (e.g. high-yielding and/or disease-resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is ineﬃcient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, e.g. dense clusters. Our concept of a modular system makes our system reconﬁgurable to adapt to diﬀerent picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5 degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit ﬂesh to avoid damage and bruising. In addition, we developed a novel perception system to localise strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new datasets. Finally, we tested the system in a commercial strawberry growing ﬁeld and our research farm with three diﬀerent strawberry varieties. The results show the eﬀectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries eﬀectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.
Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning

Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users’ data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users’ satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users’ historical data for the task of sequentially predicting users’ skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users’ historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users’ behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour.
On minimal coverings and pairwise generation of some primitive groups of wreath product type

The covering number of a finite group $G$, denoted $\sigma(G)$, is the smallest positive integer $k$ such that $G$ is a union of $k$ proper subgroups. We calculate $\sigma(G)$ for a family of primitive groups $G$ with a unique minimal normal subgroup $N$, isomorphic to $A_n^m$ with $n$ divisible by $6$ and $G/N$ cyclic. This is a generalization of a result of E. Swartz concerning the symmetric groups. We also prove an asymptotic result concerning pairwise generation.
Manifold Restricted Interventional Shapley Values

Shapley values are model-agnostic methods for explaining model predictions. Many commonly used methods of computing Shapley values, known as off-manifold methods, rely on model evaluations on out-of-distribution input samples. Consequently, explanations obtained are sensitive to model behaviour outside the data distribution, which may be irrelevant for all practical purposes. While on-manifold methods have been proposed which do not suffer from this problem, we show that such methods are overly dependent on the input data distribution, and therefore result in unintuitive and misleading explanations. To circumvent these problems, we propose ManifoldShap, which respects the model's domain of validity by restricting model evaluations to the data manifold. We show, theoretically and empirically, that ManifoldShap is robust to off-manifold perturbations of the model and leads to more accurate and intuitive explanations than existing state-of-the-art Shapley methods.
Hierarchical core-periphery structure in networks

We study core-periphery structure in networks using inference methods based on a flexible network model that allows for traditional onionlike cores within cores, but also for hierarchical treelike structures and more general non-nested types of structures. We propose an efficient Monte Carlo scheme for fitting the model to observed networks and report results for a selection of real-world data sets. Among other things, we observe an empirical distinction between networks showing traditional core-periphery structure with a dense core weakly connected to a sparse periphery, and an alternative structure in which the core is strongly connected both within itself and to the periphery. Networks vary in whether they are better represented by one type of structure or the other. We also observe structures that are a hybrid between core-periphery structure and community structure, in which networks have a set of nonoverlapping cores that roughly correspond to communities, surrounded by a single undifferentiated periphery. Computer code implementing our methods is available.
Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting

Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Smart contracts from various fields now hold over one trillion dollars worth of virtual coins, attracting numerous attacks. Quite a few practitioners have devoted themselves to developing tools for detecting bugs in smart contracts. One line of efforts revolve around static analysis techniques, which heavily suffer from high false positive rates. Another line of works concentrate on fuzzing techniques. Unfortunately, current fuzzing approaches for smart contracts tend to conduct fuzzing starting from the initial state of the contract, which expends too much energy revolving around the initial state of the contract and thus is usually unable to unearth bugs triggered by other states. Moreover, most existing methods treat each branch equally, failing to take care of the branches that are rare or more likely to possess bugs. This might lead to resources wasted on normal branches. In this paper, we try to tackle these challenges from three aspects: 1) generating function invocation sequences, we explicitly consider data dependencies between functions to facilitate exploring richer states. We further prolong a function invocation sequence $\mathcal {S}_{1}$ by appending a new sequence $\mathcal {S}_{2}$ , so that the appended sequence $\mathcal {S}_{2}$ can start fuzzing from states that are different from the initial state; 2) we incorporate a branch distance-based measure to evolve test cases iteratively towards a target branch; 3) we engage a branch search algorithm to discover rare and vulnerable branches, and design an energy allocation mechanism to take care of exercising these crucial branches. We implement IR-Fuzz and extensively evaluate it over 12K real-world contracts. Empirical results show that: (i) IR-Fuzz achieves 28% higher branch coverage than state-of-the-art fuzzing approaches, (ii) IR-Fuzz detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods, and (iii) IR-Fuzz is fast, generating an average of 350 test cases per second. Our implementation and dataset are released at https://github.com/Messi-Q/IR-Fuzz, hoping to facilitate future research.
Supersolvable saturated matroids and chordal graphs

A matroid is supersolvable if it has a maximal chain of flats each of which is modular. A matroid is saturated if every round flat is modular. In this article we present supersolvable saturated matroids as analogues to chordal graphs, and we show that several results for chordal graphs hold in this matroid context. In particular, we consider matroid analogues of the reduced clique graph and clique trees for chordal graphs. The latter is a maximum-weight spanning tree of the former. We also show that the matroid analogue of a clique tree is an optimal decomposition for the matroid parameter of tree-width.
BiCurNet: Premovement EEG-Based Neural Decoder for Biceps Curl Trajectory Estimation

Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robots. However, surface EEG-based early KP estimation studies are sparse in the literature. In this study, simultaneous surface EEG and kinematics data of five participants is collected during the biceps-curl motor task. The feasibility of early estimation of KPs is demonstrated using brain source imaging (BSI). Discrete wavelet transform (DWT) is utilized for subband extraction from preprocessed EEG signals. Further, spherical and head harmonics domain features are extracted from subbands of the EEG signals. A deep-learning-based decoding model, BiCurNet, is proposed for early KP estimation using spatial and harmonics domain EEG features during the biceps-curl task. The proposed model utilizes lightweight architecture with depthwise separable convolution layers and a customized attention module (CAM). The best Pearson correlation coefficient (PCC) between the estimated and actual trajectory of 0.7 is achieved when combined EEG features (spatial and harmonics domain) in the delta band are utilized. Intra- and intersubject performance analyses are performed to evaluate the subject-adaptability of the proposed decoding model. The performance of the proposed BiCurNet is compared with the existing multilinear regression (mLR) counterpart. The robustness of the proposed model is additionally illustrated using an ablation study. The robust performance and lightweight architecture will facilitate the real-time implementation of the model with deployment on a microcontroller to control BCI-based wearable robots.
CDA: Contrastive-adversarial Domain Adaptation

Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains. While such adversarial approaches achieve domain-level alignment, they ignore the class (label) shift. When class-conditional data distributions are significantly different between the source and target domain, it can generate ambiguous features near class boundaries that are more likely to be misclassified. In this work, we propose a two-stage model for domain adaptation called \textbf{C}ontrastive-adversarial \textbf{D}omain \textbf{A}daptation \textbf{(CDA)}. While the adversarial component facilitates domain-level alignment, two-stage contrastive learning exploits class information to achieve higher intra-class compactness across domains resulting in well-separated decision boundaries. Furthermore, the proposed contrastive framework is designed as a plug-and-play module that can be easily embedded with existing adversarial methods for domain adaptation. We conduct experiments on two widely used benchmark datasets for domain adaptation, namely, \textit{Office-31} and \textit{Digits-5}, and demonstrate that CDA achieves state-of-the-art results on both datasets.
SiFall: Practical Online Fall Detection with RF Sensing

Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to its wide coverage and privacy preserving nature. Existing RF-based fall detection systems process fall as an activity classification problem and assume that human falls introduce reproducible patterns to the RF signals. We, however, argue that the fall is essentially an accident, hence, its impact is uncontrollable and unforeseeable. We propose to solve the fall detection problem in a fundamentally different manner. Instead of directly identifying the human falls which are difficult to quantify, we recognize the normal repeatable human activities and then identify the fall as abnormal activities out of the normal activity distribution. We implement our idea and build a prototype based on commercial Wi-Fi. We conduct extensive experiments with 16 human subjects. The experiment results show that our system can achieve high fall detection accuracy and adapt to different environments for real-time fall detection.
LS And: WZ Sge-type outburst first time since the 1971 discovery

LS And was a transient discovered in 1971 in the M 31 region and it has been argued whether it could be an intergalactic nova or a dwarf nova. Using the Zwicky Transient Facility (ZTF) data, I found that the object underwent the second known outburst in 2022 April. The behavior was that of a WZ Sge-type dwarf nova with a long fading tail and the light curves of the 1971 and 2022 outbursts matched very well. The light curves suggest that LS And is a typical WZ Sge-type dwarf nova near (but before reaching) the period minimum of cataclysmic variables. The true observed peak of the 1971 outburst was likely 12.2 mag. The outburst parameters were similar to those of other WZ Sge-type dwarf novae. The fading tail lasts more than a year and the object is still currently on this tail. There was a hint of 0.5-mag temporary brightening on the fading tail and the object appears still active after the outburst.
STAR-TO-STAR IRON ABUNDANCE VARIATIONS IN RED GIANT BRANCH STARS IN THE GALACTIC GLOBULAR CLUSTER NGC 3201

We present the metallicity as traced by the abundance of iron in the retrograde globular cluster NGC 3201, measured from high-resolution, high signal-to-noise spectra of 24 red giant branch stars. A spectroscopic analysis reveals a spread in [Fe/H] in the cluster stars at least as large as 0.4 dex. Star-to-star metallicity variations are supported both through photometry and through a detailed examination of spectra. We find no correlation between iron abundance and distance from the cluster core, as might be inferred from recent photometric studies. NGC 3201 is the lowest mass halo cluster to date to contain stars with significantly different [Fe/H] values.
Design Optimization of Noise Filter Using Quantum Annealer

The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems has been limited. Here, we demonstrate the applicability of black-box optimization with a quantum annealer to the design of electric circuit systems, focusing on $\pi $ -type noise filters as an example. We develop a framework that uses quantum annealing to find the optimal location of electrical components and conductor paths connecting the components, and confirm that the learning process appropriately works over a number of trials to efficiently search for a design with high performance. The results show the potential applicability of quantum annealing to design problems of electric circuit systems.
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling

We give a quantum algorithm for computing an $\epsilon$-approximate Nash equilibrium of a zero-sum game in a $m \times n$ payoff matrix with bounded entries. Given a standard quantum oracle for accessing the payoff matrix our algorithm runs in time $\widetilde{O}(\sqrt{m + n}\cdot \epsilon^{-2.5} + \epsilon^{-3})$ and outputs a classical representation of the $\epsilon$-approximate Nash equilibrium. This improves upon the best prior quantum runtime of $\widetilde{O}(\sqrt{m + n} \cdot \epsilon^{-3})$ obtained by [vAG19] and the classic $\widetilde{O}((m + n) \cdot \epsilon^{-2})$ runtime due to [GK95] whenever $\epsilon = \Omega((m +n)^{-1})$. We obtain this result by designing new quantum data structures for efficiently sampling from a slowly-changing Gibbs distribution.
High-resolution Power Doppler Using Null Subtraction Imaging

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of 39 um was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.
Effect of Temperature-Dependent Thermophysical Properties on Turbulent Forced Convection Under Constant Heat Flux Boundary Condition

In this study, we performed highly resolved large-eddy simulations (LES) to investigate the influence of variable properties on the forced turbulent convection in a channel. The constant heat flux boundary condition permits wall temperature fluctuations and thus induces variations of fluid properties. Since the effect of viscosity on the flow exhibits $Re_\tau^{-1}$ scaling, we only considered $Re_\tau = 180$ in the present study. Compared to the flow with constant properties, results indicate that the variable properties have trivial effects on the mean velocity and temperature profiles, Reynolds shear stress, wall-normal heat flux, as well as the small-scale turbulence characteristics. However, we also observed that the turbulence intensities, low-speed streaks, burst motions, and budgets for temperature variance and wall-normal heat flux are modified by the variable properties in a perceptible way. In addition, we showed that the classic wall scaling is a good choice for flow with small and moderate variations of fluid properties.
RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration

The increasing interest in TinyML, i.e., near-sensor machine learning on power budgets of a few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to inference only. Current training algorithms, based on various forms of error and gradient backpropagation, rely on floating-point matrix operations to meet the precision and dynamic range requirements. So far, the energy and power cost of these operations has been considered too high for TinyML scenarios. This paper addresses the open challenge of near-sensor training on a few mW power budget and presents RedMulE - Reduced-Precision Matrix Multiplication Engine, a low-power specialized accelerator conceived for multi-precision floating-point General Matrix-Matrix Operations (GEMM-Ops) acceleration, supporting FP16, as well as hybrid FP8 formats, with {sign, exponent, mantissa}=({1,4,3}, {1,5,2}). We integrate RedMule into a Parallel Ultra-Low-Power (PULP) cluster containing eight energy-efficient RISC-V cores sharing a tightly-coupled data memory and implement the resulting system in a 22 nm technology. At its best efficiency point (@ 470 MHz, 0.65 V), the RedMulE-augmented PULP cluster achieves 755 GFLOPS/W and 920 GFLOPS/W during regular General Matrix-Matrix Multiplication (GEMM), and up to 1.19 TFLOPS/W and 1.67 TFLOPS/W when executing GEMM-Ops, respectively, for FP16 and FP8 input/output tensors. In its best performance point (@ 613 MHz, 0.8 V), RedMulE achieves up to 58.5 GFLOPS and 117 GFLOPS for FP16 and FP8, respectively, with 99.4% utilization of the array of Computing Elements and consuming less than 60 mW on average, thus enabling on-device training of deep learning models in TinyML application scenarios while retaining the flexibility to tackle other classes of common linear algebra problems efficiently.
Numerical Simulation of the Radiation Force from Transient Acoustic Fields: Application to Laser-Guided Acoustic Tweezers

Using pulsed acoustic waves could provide a superior selectivity for microscale acoustic tweezers. However, the theory for the radiation force of pulsed acoustic waves has only been recently derived and no numerical implementations are available. In this paper, we present a finite-element implementation of this model to simulate the transient acoustic radiation force on small spheres. We use the model to simulate laser-guided acoustic tweezers and optimize their performance. By enabling numerical simulations of the transient radiation force, this work may accelerate the rational design of pulse-based high-selectivity acoustic tweezers devices.