Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Label Inference for Crowdsourcing

When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class multi-label annotation. To improve the quality of labels, one task is independently completed by a group of heterogeneous crowdsourced workers. Then, the true values of the multiple labels of each task are inferred from these repeated noisy labels. In this paper, we propose a novel probabilistic method, which includes a multi-class multi-label dependency (MCMLD) model, to address this problem. The proposed method assumes that the label-correlation exists in both unknown true labels and noisy crowdsourced labels. Thus, it introduces a mixture of multiple independently multinoulli distributions to capture the correlation among the labels. Finally, the unknown true values of the multiple labels of each task, together with a set of confusion matrices modeling the reliability of the workers, can be jointly inferred through an EM algorithm. Experiments with three simulated typical crowdsourcing scenarios and a real-world dataset consistently show that our proposed MCMLD method significantly outperforms several competitive alternatives. Furthermore, if the labels are strongly correlated, the advantage of MCMLD will be more remarkable.
Slow Links, Fast Links, and the Cost of Gossip

Consider the classical problem of information dissemination: one (or more) nodes in a network have some information that they want to distribute to the remainder of the network. In this paper, we study the cost of information dissemination in networks where edges have latencies, i.e., sending a message from one node to another takes some amount of time. We first generalize the idea of conductance to weighted graphs by defining ϕ_* to be the "critical conductance" and ℓ_* to be the "critical latency". One goal of this paper is to argue that ϕ_* characterizes the connectivity of a weighted graph with latencies in much the same way that conductance characterizes the connectivity of unweighted graphs. We give near tight lower and upper bounds on the problem of information dissemination, up to polylogarithmic factors. Specifically, we show that in a graph with (weighted) diameter d (with latencies as weights) and maximum degree Δ, any information dissemination algorithm requires at least Δ(min(D+Δ, ℓ_*/ϕ_*)) time in the worst case. We show several variants of the lower bound (e.g., for graphs with small diameter, graphs with small max-degree, etc.) by reduction to a simple combinatorial game. We then give nearly matching algorithms, showing that information dissemination can be solved in O(min((D+Δ)log^3 n, (ℓ_*/ϕ;_*)\log n) time. This is achieved by combining two cases. We show that the classical push-pull algorithm is (near) optimal when the diameter or the maximum degree is large. For the case where the diameter and the maximum degree are small, we give an alternative strategy in which we first discover the latencies and then use an algorithm for known latencies based on a weighted spanner construction. (Our algorithms are within polylogarithmic factors of being tight both for known and unknown latencies.) While it is easiest to express our bounds in terms of ϕ_* and ℓ_*, in some cases they do not provide the most convenient definition of conductance in weighted graphs. Therefore, we give a second (nearly) equivalent characterization, namely the average conductance ϕ_avg.
Retrieving 2-D Leaf Angle Distributions for Deciduous Trees From Terrestrial Laser Scanner Data

Terrestrial laser scanning is a promising tool for estimating leaf angle (including leaf inclination and azimuthal angles) distribution (LAD). However, previous studies focus on the retrieval of leaf inclination angle distribution, very few studies have considered the distribution of leaf azimuthal angle due to the restriction of measurement techniques. In this paper, we developed a new method to obtain more accurate leaf inclination and azimuthal angle estimations based on leaf point cloud segmentation and filtration and then fit LAD functions using two-parameter Beta-distribution model. In addition, we constructed a new projection coefficient model with two parameters <inline-formula> <tex-math notation="LaTeX">$G(\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\varphi$ </tex-math></inline-formula>) using Nilson’s algorithm based on the accurate retrieval of LAD. To assess the influence of leaf numbers on leaf inclination and azimuthal angle estimations, we modeled 160 individual leaves and 10 trees with different leaf numbers. In addition, to validate the final results, we also sampled three magnolia trees with different leaf numbers and manually measured leaf inclination and azimuthal angles of all their leaves using an angle measurement device. All results showed that the method proposed in this paper can provide accurate leaf inclination and azimuthal angles (leaf inclination angle: <inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.98$ </tex-math></inline-formula>, RMSE = 24° and leaf azimuthal angle: <inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.99$ </tex-math></inline-formula>, RMSE = 3.44°). The simulated LAD and <inline-formula> <tex-math notation="LaTeX">$G(\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\varphi$ </tex-math></inline-formula>) estimations based on these leaf inclination and azimuthal angles were strongly correlated with those obtained from ground truth measurements (<inline-formula> <tex-math notation="LaTeX">$P >0.05$ </tex-math></inline-formula>).
A Fully Onchip Binarized Convolutional Neural Network FPGA Impelmentation with Accurate Inference

Deep convolutional neural network has taken an important role in machine learning algorithm which has been widely used in computer vision tasks. However, its enormous model size and massive computation cost have became the main obstacle for deployment of such powerful algorithm in low power and resource limited embedded system, such as FPGA. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binary activation function, can significantly reduce the model size and computation complexity, which paves a new road for energy-efficient FPGA implementation. In this work, we first propose a new BNN algorithm, called Parallel-Convolution BNN (i.e. PC-BNN), which replaces the original binary convolution layer in conventional BNN with two parallel binary convolution layers. PC-BNN achieves ~86% on CIFAR-10 dataset with only 2.3Mb parameter size. We then deploy our proposed PC-BNN into the Xilinx PYNQ Z1 FPGA board with only 4.9Mb on-chip RAM. Since the ultra-small network parameter, it is feasible to store the whole network parameter into on-chip RAM, which could greatly reduce the energy and delay overhead to load network parameter from off-chip memory. Meanwhile, a new data streaming pipeline architecture is proposed in PC-BNN FPGA implementation to further improve throughput. The experiment results show that our PC-BNN based FPGA implementation achieves 930 frames per second, 387.5 FPS/Watt and 396x10-4 FPS/LUT, which are among the best throughput and energy efficiency compared to most recent works.
Robot Imitation Through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments

Continuous Goal-Directed Actions (CGDA)is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories for the execution of actions are not necessarily encoded within the model. This is studied as an optimization problem, and the solution is computed through evolutionary algorithms in simulated environments. Evolutionary algorithms require a large number of evaluations, which had made the use of these algorithms in real world applications very challenging. This paper presents online evolutionary strategies, as a change of paradigm within CGDA execution. Online evolutionary strategies shift and merge motor execution into the planning loop. A concrete online evolutionary strategy, Online Evolved Trajectories (OET), is presented. OET drastically reduces computational times between motor executions, and enables working in real world dynamic environments and/or with human collaboration. Its performance has been measured against Full Trajectory Evolution (FTE)and Incrementally Evolved Trajectories (IET), obtaining the best overall results. Experimental evaluations are performed on the TEO full-sized humanoid robot with “paint” and “iron” actions that together involve vision, kinesthetic and force features.
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

Since the resurgence of CNNs the robotic vision community has developed a range of algorithms that perform classification, semantic segmentation and structure prediction (depths, normals, surface curvature) using neural networks. While some of these models achieve state-of-the art results and super human level performance, deploying these models in a time critical robotic environment remains an ongoing challenge. Real-time frameworks are of paramount importance to build a robotic society where humans and robots integrate seamlessly. To this end, we present a novel real-time structure prediction framework that predicts depth at 30 frames per second on an NVIDIA-TX2. At the time of writing, this is the first piece of work to showcase such a capability on a mobile platform. We also demonstrate with extensive experiments that neural networks with very large model capacities can be leveraged in order to train accurate condensed model architectures in a “from teacher to student” style knowledge transfer.
Finding Predictive EEG Complexity Features for Classification of Epileptic and Psychogenic Nonepileptic Seizures Using Imperialist Competitive Algorithm

In this study, the imperialist competitive algorithm (ICA) is applied for classification of epileptic seizure and psychogenic nonepileptic seizure (PNES). For this purpose, after decomposing the EEG signal into five sub-bands and extracting some complexity features of EEG, the ICA is applied to find the predictive feature subset that maximizes the classification performance in the frequency spectrum. Results show that the spectral entropy and Renyi entropy are the most important EEG features as they are always appeared in the best feature subsets when applying different classifiers. Also, it is observed that the SVM-RBF and SVM-linear models are the best classifiers resulting in highest performance metrics compared to other classifiers. Our study shows that the reported algorithm is able to classify the epileptic seizure and PNES with a very high classification metrics.
TCEP: Traffic Consolidation for Energy-Proportional High-Radix Networks

High-radix topologies in large-scale networks provide low network diameter and high path diversity, but the idle power from high-speed links results in energy inefficiency, especially at low traffic load. In this work, we exploit the high path diversity and non-minimal adaptive routing in high-radix topologies to consolidate traffic to a smaller number of links to enable more network channels to be power-gated. In particular, we propose TCEP (Traffic Consolidation for Energy-Proportional high-radix networks), a distributed, proactive power management mechanism for large-scale networks that achieves energy-proportionality by proactively power-gating network channels through traffic consolidation. Instead of naively power-gating the least utilized link, TCEP differentiates links with the type of traffic (i.e., minimally vs. non-minimally routed traffic) on them since the performance impact of power-gating on minimal traffic is greater than non-minimal traffic. The performance degradation from the reduced number of channels is minimized by concentrating available links to a small number of routers, instead of distributing them across the network, to maximize path diversity. TCEP introduces a shadow link to quickly reactivate an inactive link and Power-Aware progressive Load-balanced (PAL) routing algorithm that incorporates the link power states in load-balancing the network. Our evaluations show that TCEP achieves significantly higher throughput across various traffic patterns while providing comparable energy savings for real workloads, compared to a prior approach proposed for the flattened butterfly topology.
Why Is Implementation Science Important for Intervention Design and Evaluation Within Educational Settings?

The current challenging economic climate demands, more than ever, value for money in service delivery. Every service is required to maximise positive outcomes in the most cost-effective ways. To date, a smorgasbord of interventions have been designed to benefit society. Those worthy of attention have solid foundations in empirical research, offering service providers reassurance that positive outcomes are assured. Many of these programmes lie within the field of education and everyday school practice, yet often even these highly supported programmes yield poor results. Therefore, teachers and school staff cast these valuable materials aside, claiming they are ineffective. The question is why. Why do these interventions, grounded in theory and research, fail to translate into positive outcomes? The answer rests within the study of implementation science. Following a brief definition of key terms and theories, this article will go on to discuss why implementation is not a straightforward process. To do so, this article will draw upon examples of poorly implemented, but evidence-based, school programmes. Having acknowledged how poor implementation affects sustainability, we will then look at the growing amount of frameworks for practice within this field. One such framework, The Core Components Model, (Fixsen, Blase, Naoom, & Wallace, 2009) will be used to facilitate discussion around the processes of successful design and evaluation. This article will continue by illustrating how the quality of implementation has directly affected sustainability of The Incredible Years programmes (Webser-Stratton, 2012) and the Promoting Alternative Thinking Strategies (PATHS) curriculum (Greenberg & Kusche, 1996). Then by analysing implementation science, some of the challenges currently faced within this field will be highlighted and areas for further research discussed. This will then link to the implications for educational psychologists (EPs) and will conclude that implementation science is crucial to the design and evaluation of interventions and the EP is in an ideal position to support sustainable positive change.
Patch-based Evaluation of Dense Image Matching Quality

Airborne laser scanning and photogrammetry are two main techniques to obtain 3D data representing the object surface. Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data. We present a framework to evaluate point clouds from dense image matching and derived Digital Surface Models (DSM) based on automatically extracted sample patches. Dense matching error and noise level are evaluated quantitatively at both the local level and whole block level. Experiments show that the optimal vertical accuracy achieved by dense matching is as follows: the mean offset to the reference data is 0.1 Ground Sampling Distance (GSD); the maximum offset goes up to 1.0 GSD. When additional oblique images are used in dense matching, the mean deviation, the variation of mean deviation and the level of random noise all get improved. We also detect a bias between the point cloud and DSM from a single photogrammetric workflow. This framework also allows to reveal inhomogeneity in the distribution of the dense matching errors due to over-fitted BBA network. Meanwhile, suggestions are given on the photogrammetric quality control.
SDREGION: Fast Spotting of Changing Communities in Biological Networks

Given a large, dynamic graph, how can we trace the activities of groups of vertices over time? Given a dynamic biological graph modeling a given disease progression, which genes interact closely at the early stage of the disease, and their interactions are being disrupted in the latter stage of the disease? Which genes interact sparsely at the early stage of the disease, and their interactions increase as the disease progresses? Knowing the answers to these questions is important as they give insights to the underlying molecular mechanism to disease progression, and potential treatments that target these mechanisms can be developed. There are three main contributions to this paper. First, we designed a novel algorithm, SDREGION, that identifies subgraphs that decrease or increase in density monotonically over time, referred to as d-regions or i-regions, respectively. We introduced the objective function, -density, for identifying d-(i-)regions. Second, SDREGION is a generic algorithm, applicable across several real datasets. In this manuscript, we showed its effectiveness, and made observations in the modeling of the progression of lung cancer. In particular, we observed that SDREGION identified d-(i-)regions that capture mechanisms that align with literature. Importantly, findings that were identified but were not retrospectively validated by literature may provide novel mechanisms in tumor progression that will guide future biological experiments. Third, SDREGION is scalable with a time complexity of O(mlogn + nlogn) where m is the number of edges, and n is the number of vertices in a given dynamic graph.
Learning Coexistence Discriminative Features for Multi-Class Object Detection

Existing methods on object detection have the ability to learn the discriminative features of local regions for object recognition; however, the coexistence relation between the multi-class objects could also benefit recognition. In this paper, we propose to learn the coexistence discriminative features for multi-class object detection. Given an image with multiple class objects, the strong supervision of the region-based annotations are first used as the image-level label to learn the independent discriminative features for each class. Then, the coexistence relation is fused as coexistence feature based on the attention mechanism. By combining the independent discriminative features and coexistence feature, the classification performance of multi-class object proposals can be consistently improved. Experimental results prove that the proposed end-to-end network outperforms the state-of-the-art object detection approaches, and the learned discriminative features can effectively capture the coexistence relations to improve classification performance of multi-class objects in the object detection task.
Optimal Regimens and Cutoff Evaluation of Tildipirosin Against Pasteurella multocida

Pasteurella multocida (PM) can invade the upper respiratory tract of the body and cause death and high morbidity. Tildipirosin, a new 16-membered-ring macrolide antimicrobial, has been recommended for the treatment of respiratory diseases. The objective of this research was to improve the dose regimes of tildipirosin to PM for reducing the macrolides resistance development with the pharmacokinetic/pharmacodynamic (PK/PD) modeling approach and to establish an alternate cutoff for tildipirosin against PM. A single dose (4 mg/kg body weight) of tildipirosin was administered via intramuscular (i.m.) and intravenous (i.v.) injection to the pigs. The minimum inhibitory concentration (MIC) values of clinical isolates (112) were measured in the range of 0.0625–32 μg/ml, and the MIC50 and MIC90 values were 0.5 and 2 μg/ml, respectively. The MIC of the selected PM04 was 2 and 0.5 μg/ml in the tryptic soy broth (TSB) and serum, respectively. The main pharmacokinetic (PK) parameters including the area under the curve at 24 h (AUC24 h), AUC, terminal half-life (T1/2), the time to peak concentration (Tmax), peak concentration (Cmax), relative total systemic clearance (CLb), and the last mean residence time (MRTlast) were calculated to be 7.10, 7.94 μg∗h/ml, 24.02, NA h, NA μg/ml, 0.46 L/h∗kg, 8.06 h and 3.94, 6.79 μg∗h/ml, 44.04, 0.25 h, 0.98 μg/ml, 0.43 L/h∗kg, 22.85 h after i.v. and i.m. induction, respectively. Moreover, the bioavailability of i.m. route was 85.5%, and the unbinding of tildipirosin to serum protein was 78%. The parameters AUC24 h/MIC in serum for bacteriostatic, bactericidal, and elimination activities were calculated as 18.91, 29.13, and 34.03 h based on the inhibitory sigmoid Emax modeling. According to the Monte Carlo simulation, the optimum doses for bacteriostatic, bactericidal, and elimination activities were 6.10, 9.41, and 10.96 mg/kg for 50% target and 7.86, 12.17, and 14.57 mg/kg for 90% target, respectively. The epidemiological cutoff value (ECV) was calculated to be 4 μg/ml which could cover 95% wild-type clinical isolates distribution. The PK-PD cutoff (COPD) was analyzed to be 0.25 μg/ml in vitro for tildipirosin against PM based on the Monte Carlo simulation. Compared with these two cutoff values, the finial susceptible breakpoint was defined as 4 μg/ml. The data presented now provides the optimal regimens (12.17 mg/kg) and susceptible breakpoint (4 μg/ml) for clinical use, but these predicted data should be validated in the clinical practice.
A Linearity-Improved 8-bit 320-MS/s SAR ADC With Metastability Immunity Technique

This paper presents an 8-bit 320-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) with the linearity-improved technique. A linearity-improved sampling switch with parasitic capacitance compensation, which makes the parasitic capacitance of sampling switch to be almost constant with varied input signal, is proposed. It also improves the matching of the differential sampling switches. Moreover, a metastability immunity technique is provided to suppress the uncertain decision behavior of dynamic comparator at high conversion rate. In addition, a bypass SAR logic that parallels comparator and SAR logic operations is exhibited to reduce the delay of SAR feedback loop. To demonstrate the proposed techniques, a design example of SAR ADC is fabricated in a 55-nm CMOS technology, consuming 1.2 mW at a 1-V power supply. It achieves a signal-to-noise-and-distortion ratio >43.5 dB and spurious free dynamic range >54 dB at 320 MS/s. The ADC core occupies an active area of only 0.02 mm2, and the corresponding figure of merit is 30 fJ/conversion step with Nyquist rate.