Miracle-dz/UniFAR-FTUs huggingface dataset:
Power and Accuracy of Multi-Layer Perceptrons (MLPs) under Reduced-voltage FPGA BRAMs Operation

In this paper, we exploit the aggressive supply voltage underscaling technique in Block RAMs (BRAMs) of Field Programmable Gate Arrays (FPGAs) to improve the energy efficiency of Multi-Layer Perceptrons (MLPs). Additionally, we evaluate and improve the resilience of this accelerator. Through experiments on several representative FPGA fabrics, we observe that until a minimum safe voltage level, i.e., Vmin the MLP accuracy is not affected. This safe region involves a large voltage guardband. Also, it involves a narrower voltage region where faults start to appear in memories due to the increased circuit delay, but these faults are masked by MLP, and thus, its accuracy is not affected. However, further undervolting causes significant accuracy loss as a result of the fast-increasing high fault rates. Based on the characterization of these undervolting faults, we propose fault mitigation techniques that can effectively improve the resilience behavior of such accelerator. Our evaluation is based on four FPGA platforms. On average, we achieve >90% energy saving with a negligible accuracy loss of up to 0.1%.
Empowering Active Learning to Jointly Optimize System and User Demands

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
DeepRacing: Parameterized Trajectories for Autonomous Racing

We consider the challenging problem of high speed autonomous racing in a realistic Formula One environment. DeepRacing is a novel end-to-end framework, and a virtual testbed for training and evaluating algorithms for autonomous racing. The virtual testbed is implemented using the realistic F1 series of video games, developed by Codemasters, which many Formula One drivers use for training. This virtual testbed is released under an open-source license both as a standalone C++ API and as a binding to the popular Robot Operating System 2 (ROS2) framework. This open-source API allows anyone to use the high fidelity physics and photo-realistic capabilities of the F1 game as a simulator, and without hacking any game engine code. We use this framework to evaluate several neural network methodologies for autonomous racing. Specifically, we consider several fully end-to-end models that directly predict steering and acceleration commands for an autonomous race car as well as a model that predicts a list of waypoints to follow in the car's local coordinate system, with the task of selecting a steering/throttle angle left to a classical control algorithm. We also present a novel method of autonomous racing by training a deep neural network to predict a parameterized representation of a trajectory rather than a list of waypoints. We evaluate these models performance in our open-source simulator and show that trajectory prediction far outperforms end-to-end driving. Additionally, we show that open-loop performance for an end-to-end model, i.e. root-mean-square error for a model's predicted control values, does not necessarily correlate with increased driving performance in the closed-loop sense, i.e. actual ability to race around a track. Finally, we show that our proposed model of parameterized trajectory prediction outperforms both end-to-end control and waypoint prediction.
Nonclassical temporal correlations enhance the performance of ticking clocks

We investigate the role of nonclassical temporal correlations in enhancing the performance of ticking clocks in a discrete-time scenario. We show that the problem of optimal models for ticking clocks is related to the violation of Leggett-Garg-type temporal inequalities formulated in terms of, possibly invasive, sequential measurements, but on a system with a bounded memory capacity. Moreover, the idea of ticking clocks inspires the derivation of a new family of temporal inequalities showing a gap between classical and quantum correlations. We present analytical and numerical results showing that quantum ticking-clock models achieving accuracy beyond the classical bound are also those violating Leggett-Garg-type temporal inequalities for finite sequences. We also investigate the continuous limit of our models and its relation with known results in the literature.
Organic matter production of American lobsters (Homarus americanus) during impoundment in Maine, United States

Abstract Lobster impoundments are dammed coastal embayments utilised to hold American lobsters (Homarus americanus) before shipping to market. The impacts of lobster impoundments on the environment have not been previously studied. Here, the digestive functioning of American lobsters was examined to assess the quantity and quality (TVS, total volatile solids) of lobster faeces produced under the temperatures and feeding regimens these animals were subjected to during the impoundment period. Overall it was determined that quantity and quality of faeces did not differ among the experimental temperatures (5, 10, and 15°C), and that animals fed every 1–2 days produced greater quantity and quality of faeces than those fed every 3–18 days, or those animals fed less than every 18 days. As a first estimate of organic matter production in active lobster impoundments, it was calculated that a typical lobster impoundment produced 0.79 ± 0.35 (average ± 1 SD) g TVS m‐2 day‐1 during the impoundment period, equivalent to 3.11 ± 0.60% of the total weight of lobsters stocked into the impoundment. This level of organic matter production is below the level produced by other aquaculture operations, and that at which benthic impacts might be expected.
Cranial strength in relation to estimated biting forces in some mammals

The mammalian skull has proven to be remarkably plastic during ontogeny and phylogeny in response to the demands of mastication. I examine whether the bending strength of the skull in some mammals correlates with the maximal loads imposed through the masticatory apparatus. The approach is analytical, using the methods of beam theory. Cranial strength is estimated from the second moment of area and other geometrical measurements made from 20–30 transverse CT scans through the skulls of 20 opossums (Didelphis virginiana), and through single skulls of five felid and five canid genera of different sizes. Maximal biting forces were first estimated from areas on the dried skulls bounding the spaces filled in life by the jaw-adducting muscles. These estimates were then adjusted with reference to forces recorded in vivo or, for other specimens, to estimates based on dissections of the jaw muscles. Stress distribution in the face, and peak stresses, were calculated for each animal. Stress levels are low (5–35 MPa).
Vehicle Re-Identification Based on Complementary Features

In this work, we present our solution to the vehicle reidentification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle’s orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle ReIdentification demonstrated the advantage of our methods, we got 5-th place in the vehicle Re-ID track of AIC2020.
On increasing and invariant parking sequences

The notion of parking sequences is a new generalization of parking functions introduced by Ehrenborg and Happ. In the parking process defining the classical parking functions, instead of each car only taking one parking space, we allow the cars to have different sizes and each takes up a number of adjacent parking spaces after a trailer $T$ parked on the first $z-1$ spots. A preference sequence in which all the cars are able to park is called a parking sequence. In this paper, we study increasing parking sequences and count them via bijections to lattice paths with right boundaries. Then we study two notions of invariance in parking sequences and present various characterizations and enumerative results.
Inflation of migrated hot Jupiters

The observed low densities of gas giant planets with a high equilibrium temperature (hot Jupiters) can be simulated in models when a fraction of the surface radiation is deposited deeper in the interior. Meanwhile, migration theories suggest that hot Jupiters formed further away from their host star and migrated inward. We incorporate disc migration in simulations of the evolving interior of hot Jupiters to determine whether migration has a long-lasting effect on the inflation of planets. We quantify the difference between the radius of a migrated planet and the radius of a planet that formed in situ as the radius discrepancy. We remain agnostic about the physical mechanism behind interior heating, but assume it scales with the received stellar flux by a certain fraction. We find that the change in irradiation received from the host star while the planet is migrating can affect the inflation and final radius of the planet. Models with a high fraction of energy deposited in the interior (>5 per cent) show a significant radius discrepancy when the deposit is at higher pressures than $P=1 \, \mathrm{bar}$. For a smaller fraction of 1 per cent, there is no radius discrepancy for any deposit depth. We show that a uniform heating mechanism can cause different rates of inflation, depending on the migration history. If the forthcoming observations on mean densities and atmospheres of gas giants give a better indication of a potential heating mechanism, this could help to constrain the prior migration of such planets.
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN are concatenated with historical records and then fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.
Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.
Maximum Size Intersecting Families of Bounded Minimum Positive Co-degree

Let $\mathcal{H}$ be an $r$-uniform hypergraph. The \emph{minimum positive co-degree} of $\mathcal{H}$, denoted $\delta_{r-1}^+(\mathcal{H})$, is the minimum $k$ such that if $S$ is an $(r-1)$-set contained in a hyperedge of $\mathcal{H}$, then $S$ is contained in at least $k$ distinct hyperedges of $\mathcal{H}$. We determine the maximum possible size of an intersecting $r$-uniform $n$-vertex hypergraph with minimum positive co-degree $\delta_{r-1}^+(\mathcal{H}) \geq k$ and characterize the unique hypergraph attaining this maximum, for $n$ sufficiently large. Our proof is based on the delta-system method.
Estimates of adult survival rate for three colonies of Sooty Shearwater (Puffinus griseus) in New Zealand

Abstract The Sooty Shearwater (Puffinus griseus), also known in New Zealand as tītī or muttonbird, is an abundant, long-lived, trans-equatorial migrant that breeds in colonies in the southern hemisphere during the austral summer and migrates to and forages in the North Pacific Ocean during the boreal summer. That populations of Sooty Shearwaters have been declining has been indicated by falling oceanic counts, and reduced burrow density and declining rates of chick harvest of Rakiura Māori muttonbirders. Accurate estimation of adult survival rate of Sooty Shearwaters is vital if we wish to use a demographic model to help understand the decline and manage the population. Mark—recapture studies were carried out at three breeding colonies: Taiaroa Head, Whenua Hou and The Snares, all in southern New Zealand. Analysis of the data generally involved the use of models in which both transience and trap-dependence were allowed for. In addition, birds were split into two groups, according to whether they had been caught on the surface or under an inspection hatch that was placed over a breeding chamber. Our estimates of the level of transience suggest that a large portion of the population at each colony is composed of juvenile and pre-breeding birds. Model-averaged estimates of the survival rate for resident birds were 0.917 (95% C.I.: 0.860–0.973) at Taiaroa Head, 0.966 (0.933–0.997) at Whenua Hou, and 0.961 (0.706–0.996) at The Snares. A combined estimate for The Snares and Whenua Hou is 0.952 (0.896–0.979). Our estimates are higher than earlier estimates of survival for Sooty Shearwaters and congeners, presumably because transience has been more adequately accounted for than in earlier mark—recapture models.
Effects of Controller Heterogeneity on Autonomous Vehicle Traffic

Interactions between road users are both highly non-linear and profoundly complex, and there is no reason to expect that interactions between autonomous vehicles will be any different. Given the recent rapid development of autonomous vehicle technologies, we need to understand how these interactions are likely to present themselves, and what their implications might be. This paper looks into the impact of autonomous vehicles with differing controllers, focusing specifically on the effects of changing the mean and heterogeneity of controller parameters on three key performance metrics: throughput, passenger safety and comfort. Towards this end, we develop a method for systematically sampling vehicle controllers as a function of parameter heterogeneity. In addition to evaluating the impact of heterogeneity on performance, we quantity the relative impacts of controller input parameters on the output performance metrics by means of sensitivity analyses. The MovSim traffic simulator was used to simulate a realistic traffic system, whilst recording maximum throughput, as well as lane change frequencies and mean absolute accelerations as proxies for safety and comfort. Our results reveal that traffic performance is primarily affected by the heterogeneity of vehicle target velocities, as well as by the mean values of a very small subset of the parameters, of which the target velocity is by far the most significant.
Sustained in vivo cardiac protection by a rationally designed peptide that causes ɛ protein kinase C translocation

Brief periods of cardiac ischemia trigger protection from subsequent prolonged ischemia (preconditioning). ɛ Protein kinase C (ɛPKC) has been suggested to mediate preconditioning. Here, we describe an ɛPKC-selective agonist octapeptide, ψɛ receptor for activated C-kinase (ψɛRACK), derived from an ɛPKC sequence homologous to its anchoring protein, ɛRACK. Introduction of ψɛRACK into isolated cardiomyocytes, or its postnatal expression as a transgene in mouse hearts, increased ɛPKC translocation and caused cardio-protection from ischemia without any deleterious effects. Our data demonstrate that ɛPKC activation is required for protection from ischemic insult and suggest that small molecules that mimic this ɛPKC agonist octapeptide provide a powerful therapeutic approach to protect hearts at risk for ischemia.
Enhancing LGMD’s Looming Selectivity for UAV With Spatial–Temporal Distributed Presynaptic Connections

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect’s visual neuron, LGMD is considered to be an ideal basis for building UAV’s collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial–temporal synaptic interactions, which is inspired by recent findings in locusts’ synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial–temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.
Optofluidic microdevice for algae classification: a comparison of results from discriminant analysis and neural network pattern recognition

The early detection of changes in the level and composition of algae is essential for tracking water quality and environmental changes. Current approaches require the collection of a specimen which is later analyzed in a laboratory: this slow and expensive approach prevents the rapid identification of changes in algae species dynamics and hinders a quick response to potential outbreaks. In a recent work, we presented a microfluidic chip for classifying and quantifying algae species in water. Here, we study the device performance and specifically compare the difference in results obtained by using a discriminant analysis classification approach and a neural network pattern recognition approach. Using both of these methods, we demonstrate the classification of algae by species, of microspheres by size, and of a detritus/cyanobacteria mixture by type. In each of the demonstrations here, the neural network outperforms the discriminant analysis method.
Improved Algorithms for Solving Polynomial Systems over GF(2) by Multiple Parity-Counting

We consider the problem of finding a solution to a multivariate polynomial equation system of degree $d$ in $n$ variables over $\mathbb{F}_2$. For $d=2$, the best-known algorithm for the problem is by Bardet et al. [J. Complexity, 2013] and was shown to run in time $O(2^{0.792n})$ under assumptions that were experimentally found to hold for random equation systems. The best-known worst-case algorithm for the problem is due to Bjorklund et al. [ICALP'19]. It runs in time $O(2^{0.804n})$ for $d = 2$ and $O(2^{(1 - 1/(2.7d))n})$ for $d > 2$. In this paper, we devise a worst-case algorithm that improves the one by Bjorklund et al. It runs in time $O(2^{0.6943n})$ (or $O(1.6181^n)$) for $d = 2$ and $O(2^{(1 - 1/(2d))n})$ for $d > 2$. Our algorithm thus outperforms all known worst-case algorithms, as well as ones analyzed for random equation systems. We also devise a second algorithm that outputs all solutions to a polynomial system and has similar complexity to the first (provided that the number of solutions is not too large). A central idea in the work of Bjorklund et al. was to reduce the problem of finding a solution to a polynomial system over $\mathbb{F}_2$ to the problem of counting the parity of all solutions. A parity-counting instance was then reduced to many smaller parity-counting instances. Our main observation is that these smaller instances are related and can be solved more efficiently by a new algorithm to a problem which we call multiple parity-counting.