Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extended Kalman Filter for Distance Estimation and Power Control in Mobile Molecular Communication

In this paper, we consider a mobile molecular communication (MC) system consisting of two mobile nanomachines, a transmitter and a receiver, propelled by a positive drift velocity and Brownian motion in a realistic blood-vessel-type flow regime. Considering the nonlinear movement of the nanomachines, an extended Kalman filter is employed to estimate the distance from the transmitter. Furthermore, based on the predicted distance, to keep the number of received molecules for bit 1 at a stable level, we employ power control on the number of transmitted molecules based on the distance between the transmitter and the receiver and the residual molecules in the channel from the previous transmission. Finally, the optimal detection threshold is obtained by minimizing the error probability. It is verified that a fixed optimal detection threshold can be effective for the power control scheme in the mobile MC. The bit error rate (BER) performance of our scheme is verified via simulation results.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers

We introduce a new simulation benchmark “Han-doverSim” for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasping of objects. We create training and evaluation environments for the receiver with standardized protocols and metrics. We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.11Code is open sourced at https://handover-sim.github. io.
Homophily and Incentive Effects in Use of Algorithms

As algorithmic tools increasingly aid experts in making conse- quential decisions, the need to understand the precise factors that mediate their inﬂuence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily— do people defer more to models that tend to agree with them? —by manip- ulating the agreement during training between participants and the algorithmic tool. Second, we considered incentives— how do people incorporate a (known) cost structure in the hybrid decision-making setting? —by varying rewards associated with true positives vs. true negatives. Surprisingly, we found limited inﬂuence of either homophily and no evidence of incen- tive effects, despite participants performing similarly to previous studies. Higher levels of agreement between the partici- pant and the AI tool yielded more conﬁdent predictions, but only when outcome feedback was absent. These results high- light the complexity of characterizing human-algorithm interactions, and suggest that ﬁndings from social psychology may require re-examination when humans interact with algorithms.
Causal Inference from Small High-dimensional Datasets

Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another high-dimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.
The Compton/Schwarzschild duality, black hole entropy and quantum information

A new kind of duality has been proposed by Carr related to the quantum description of black holes, the so-called Compton/Schwarzschild duality \cite{Carr:2015nqa}. In this context, a new form for a Generalized Uncertainty Principle has arisen, which must bring us an interesting new route to a quantum description of spacetime. In the present paper, we shall investigate the consequences of the Compton/Schwarzschild duality to black hole entropy. The results found out reinforce an interesting perspective on the relationship between black holes and quantum information theory that has been recently proposed in the literature: that black hole entropy can assume negative values at the final stage of black hole evaporation. Consequently, in the context of the quantum corrections to gravity proposed by the Compton/Schwarschild duality, the final state of a black hole might correspond to a quantum entangled state, in the place of a remnant.
Hybrid Intelligent Testing in Simulation-Based Verification

Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contribute to coverage progress, yet they consume verification resources. In this paper, we propose a hybrid intelligent testing approach combining two methods that have previously been treated separately, namely Coverage-Directed Test Selection and Novelty-Driven Verification. Coverage-Directed Test Selection learns from coverage feedback to bias testing toward the most effective tests. Novelty-Driven Verification learns to identify and simulate stimuli that differ from previous stimuli, thereby reducing the number of simulations and increasing testing efficiency. We discuss the strengths and limitations of each method, and we show how our approach addresses each method’s limitations, leading to hardware testing that is both efficient and effective.
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages

Large pretrained multilingual models, trained on dozens of languages, have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. Further adapting these models to specific languages, especially ones unseen during pre-training, is an important goal toward expanding the coverage of language technologies. In this study, we show how we can use language phylogenetic information to improve cross-lingual transfer leveraging closely related languages in a structured, linguistically-informed manner. We perform adapter-based training on languages from diverse language families (Germanic, Uralic, Tupian, Uto-Aztecan) and evaluate on both syntactic and semantic tasks, obtaining more than 20% relative performance improvements over strong commonly used baselines, especially on languages unseen during pre-training.
The AI Mechanic: Acoustic Vehicle Characterization Neural Networks

In a world increasingly dependent on road-based transportation, it is essential to understand vehicles. We introduce the AI mechanic, an acoustic vehicle characterization deep learning system, as an integrated approach using sound captured from mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel cascading architectures for vehicle understanding, which we define as sequential, conditional, multi-level networks that process raw audio to extract highly-granular insights. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio and achieve>92% validation set accuracy on attributes (fuel type, engine configuration, cylinder count and aspiration type). Our cascading architecture additionally achieved 93.6% validation and 86.8% test set accuracy on misfire fault prediction, demonstrating margins of 16.4% / 7.8% and 4.2% / 1.5% improvement over na\"ive and parallel baselines. We explore experimental studies focused on acoustic features, data augmentation, feature fusion, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.
Continuously Tempered PDMP samplers

New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However, these methods can struggle to sample from multi-modal or heavy-tailed distributions. We show how tempering ideas can improve the mixing of PDMPs in such cases. We introduce an extended distribution defined over the state of the posterior distribution and an inverse temperature, which interpolates between a tractable distribution when the inverse temperature is 0 and the posterior when the inverse temperature is 1. The marginal distribution of the inverse temperature is a mixture of a continuous distribution on [0,1) and a point mass at 1: which means that we obtain samples when the inverse temperature is 1, and these are draws from the posterior, but sampling algorithms will also explore distributions at lower temperatures which will improve mixing. We show how PDMPs, and particularly the Zig-Zag sampler, can be implemented to sample from such an extended distribution. The resulting algorithm is easy to implement and we show empirically that it can outperform existing PDMP-based samplers on challenging multimodal posteriors.
Dexterous robotic manipulation using deep reinforcement learning and knowledge transfer for complex sparse reward‐based tasks

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate a cube along a specified positional trajectory, but with no requirement for the cube to have any specific orientation. We used a relatively simple reward function, a combination of a goal‐based sparse reward and a distance reward, in conjunction with Hindsight Experience Replay (HER) to guide the learning of the DRL agent (Deep Deterministic Policy Gradient [DDPG]). Our approach allowed our agents to acquire dexterous robotic manipulation strategies in simulation. These strategies were then deployed on the real robot and outperformed all other competition submissions, including those using more traditional robotic control techniques, in the final evaluation stage of the RRC. Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory. The requirement to also orient the cube makes the agent less able to learn the task through blind exploration due to increased problem complexity. To circumvent this issue, we make novel use of a Knowledge Transfer (KT) technique that allows the strategies learned by the agent in the original task (which was agnostic to cube orientation) to be transferred to this task (where orientation matters). KT allowed the agent to learn and perform the extended task in the simulator, which improved the average positional deviation from 0.134 to 0.02 m, and average orientation deviation from 142° to 76° during evaluation. This KT concept shows good generalization properties and could be applied to any actor‐critic learning algorithm.
Gauge symmetry breaking in flux compactification with a Wilson-line scalar condensate

We discuss the gauge symmetry breaking of six dimensional theories in flux compactification with a magnetic flux background and a constant vacuum expectation value (VEV) for the scalar fields, which are zero modes of extra spatial components of the gauge field. Although the effective potential for the scalar fields are known not to be generated classically and radiatively in a magnetic flux background only, the one-loop effective potential is shown to be generated by the effects of the non-zero constant VEV. As illustrations, we calculate the one-loop effective potential in SU(2) and SU(3) Yang-Mills theories. In both cases, we find that the potential minimum is located at non-zero VEV and the gauge symmetry breaking takes place.
Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms

We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale images of 30,222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion. The training set totally consists of 27,222 images, and the test set contains 300 images per class. Oracle-MNIST shares the same data format with the original MNIST dataset, allowing for direct compatibility with all existing classifiers and systems, but it constitutes a more challenging classification task than MNIST. The images of ancient characters suffer from 1) extremely serious and unique noises caused by three-thousand years of burial and aging and 2) dramatically variant writing styles by ancient Chinese, which all make them realistic for machine learning research. The dataset is freely available at https://github.com/wm-bupt/oracle-mnist.
Defending Against Adversarial Attacks by Energy Storage Facility

Adversarial attacks on data-driven algorithms applied in power system will be a new type of threat on grid security. Literature has demonstrated the adversarial attack on deep-neural network can significantly misleading the load forecast of a power system. However, it is unclear how the new type of attack impact on the operation of grid system. In this research, we point out that adversarial attacks on the algorithms are distinct from traditional attacks. Rather than causing system collapse, the adversarial attacks camouflaged as normal forecast bias and thus is able to continuously cause the economic loss in a system. We manifest that the adversarial algorithm attack induces a significant cost-increase risk which will be exacerbated by the growing penetration of intermittent renewable energy. In Texas, a 5% adversarial attack can increase the total generation cost by 17% in a quarter, which account for around $\$ 2\times 10^{7}$. When wind-energy penetration increases to over 40%, the 5% adversarial attack will inflate the generation cost by 23%. Our research discovers a novel approach of defending against the adversarial attack: investing on energy-storage system. All current literature focuses on developing algorithm to defending against adversarial attack. We are the first research revealing the capability of using facility in physical system to defending against the adversarial algorithm attack in a system of Internet of Thing, such as smart grid system.
Fermi-LAT Detection of Extended Gamma-Ray Emission in the Vicinity of SNR G045.7-00.4: Evidence of Escaping Cosmic Rays Interacting with the Surrounding Molecular Clouds

We present an analysis of Fermi Large Area Telescope data of the gamma-ray emission in the vicinity of a radio supernova remnant (SNR), G045.7-00.4. To study the origin of the gamma-ray emission, we also make use of the CO survey data of Milky Way Imaging Scroll Painting to study the massive molecular gas complex that surrounds the SNR. The whole size of the gigaelectronvolt emission is significantly larger than that of the radio morphology. Above 3 GeV, the gigaelectronvolt emission is resolved into two sources: one is spatially consistent with the position of the SNR with a size comparable to that of the radio emission, and the other is located outside of the western boundary of the SNR and spatially coincident with the densest region of the surrounding molecular cloud. We suggest that the gigaelectronvolt emission of the western source may arise from cosmic rays (CRs) that have escaped the SNR and illuminated the surrounding molecular cloud. We find that the gamma-ray spectra of the western source can be consistently explained by this scenario with a total energy of ∼1050 erg in escaping CRs assuming the escape is isotropic.
Strong coupling quantum Otto cycle

We propose a quantum Otto cycle model without the weak coupling assumption that the interaction between the system and reservoirs is negligible. Our model replaces the thermalization process in the weak coupling model with a process comprising thermalization and decoupling. We analytically calculate the eﬃciency of our model and show that in the weak interaction limit, when the contribution of the interaction terms is neglected, the eﬃciency reduces to that of the earlier model. We derive the suﬃcient condition for the eﬃciency of our model not to surpass that of the weak coupling model and ﬁnd that the condition is satisﬁed when the decoupling processes have a positive cost. Using a simple two-level system, we numerically examine the relation between the interaction strength and the eﬃciency of our model, and demonstrate that our model does not exceed the eﬃciency of the weak coupling model.
A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks

Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for continuous-time networks of relational events, using a latent space representation for nodes. We model relational events between nodes using mutually exciting Hawkes processes with baseline intensities dependent upon the distances between the nodes in the latent space and sender and receiver specific effects. We demonstrate that our proposed LSH model can replicate many features observed in real temporal networks including reciprocity and transitivity, while also achieving superior prediction accuracy and providing more interpretable fits than existing models.
Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental Changes

Developments in reinforcement learning (RL) have allowed algorithms to achieve impressive performance in highly complex, but largely static problems. In contrast, biological learning seems to value efficiency of adaptation to a constantly-changing world. Here we build on a recently-proposed neuronal learning rule that assumes each neuron can optimize its energy balance by predicting its own future activity. That assumption leads to a neuronal learning rule that uses presynaptic input to modulate prediction error. We argue that an analogous RL rule would use action probability to modulate reward prediction error. This modulation makes the agent more sensitive to negative experiences, and more careful in forming preferences. We embed the proposed rule in both tabular and deep-Q-network RL algorithms, and find that it outperforms conventional algorithms in simple, but highly-dynamic tasks. We suggest that the new rule encapsulates a core principle of biological intelligence; an important component for allowing algorithms to adapt to change in a human-like way.
Disc instability and bar formation: View from the IllustrisTNG simulations

We make use of z = 0 samples of strongly barred and unbarred disc galaxies from the TNG100 and TNG50 cosmological hydrodynamical simulations to assess the performance of the simple disc instability criterion proposed by Efstathiou, Lake & Negroponte (1982) (ELN-criterion). We find that strongly barred galaxies generally assemble earlier, are more star-dominated in their central regions, and have more massive and more compact discs than unbarred galaxies. The ELN-criterion successfully identifies ${\sim }\, 75{{\ \rm per\ cent}}$ and ${\sim }\, 80{{\ \rm per\ cent}}$ of the strongly barred and the unbarred galaxies, respectively. Strongly barred galaxies that the criterion fails to identify tend to have more extended discs, higher spin values and bars that assembled later than is typical for the bulk of the barred population. The bars in many of these cases appear to be produced by an interaction with a close neighbour (i.e. to be externally triggered) rather than to result from secular growth in the disc. On the other hand, we find that unbarred galaxies misclassified as barred by the ELN-criterion typically have stellar discs similar to those of barred galaxies, although more extended in the vertical direction and less star-dominated in their central regions, possibly reflecting later formation times. In addition, the bulge component of these galaxies is significantly more prominent at early times than in the strongly barred sample. Thus, the ELN-criterion robustly identifies secular bar instabilities in most simulated disc galaxies, but additional environmental criteria are needed to account for interaction-induced bar formation.
Online parametrierbare Traktor-Gerätemodelle

In order to optimize fuel consumption of tractors holistically, implement models are necessary which are able to describe the demand of draft and torque power at given operating parameters. Typically, existing models aren’t able to fulfil parameterization online, i. e. during field operation, because implement and soil parameters are unknown and soil parameters vary strongly. In this article a cluster-based approach is proposed. To recognize different soils and implements, the continous collection of situation-based measurement data and their assignment to a cluster is used. The cluster center is used for a quantification of the average situation to parameterize the models. Therefore simplifying model assumptions have to be done which are also presented. The implements mentioned at DLG-PowerMix are considered.
Effects of Formula Fertilization by Soil Testing on Rice

Compared with the regular fertilization,the increasing effect of formula fertilization by soil testing was investigated.The results showed that in comparison with control and regular plots,rice yield in plots where formula fertilization by soil testing was applied,increased 178 kg/667m2 and 30.2 kg/667m2,corresponding to 50%,6.3% of increasing rate, and the net income was enhanced 231.15 yuan/667m2,39.90 yuan/667m2,respectively.Through determinating analysis,we found that the dependent rate of rice on nutrient was 70.7%. The rice yield obtained at harvest was lower 13.8 kg than that of expected yield,the identical degree reached 97.5%.
EUROPEAN JOURNAL OF ENTOMOLOGY EUROPEAN JOURNAL OF ENTOMOLOGY

Rising concerns about the negative eﬀ ects of chemical compounds in pest control have made it increasingly necessary to ﬁ nd new environmentally friendly compounds to combat insect pests. In recent years, the use of plant derivatives, such as, essential oils have proved very promising. Despite the many studies on essential oils, there are very few studies on the eﬀ ect of these oils on induction of Heat shock proteins (HSPs) and antioxidant enzymes. Therefore, it was hypothesized that the exposure of insects to essential oils induce stress, which it is likely induces the production of heat shock proteins and antioxidant enzymes. Thus, Ectomyelois ceratoniae Clark (Lepidoptera: Pyralidae) was used as a model insect to study the lethal and sublethal eﬀ ect of the fumigant toxicity of the essential oils extracted from the plants Thymus daenensis Celak and Satureja khuzistanica Jamzad. Gas Chromatography/Mass Spectrometry indicated that thymol and carvacrol were the major component of T. daenensis and S. khuzistanica extracts, respectively. Exposing E. ceratoniae to sublethal doses of the essential oils (LC 30 ) and using RT-qPCR indicated that the expression levels of HSP70 and HSP90 were signiﬁ cantly increased. Activities of antioxidant enzymes including SOD, CA, POX, GST and the concentration of MDA increased following exposure to sublethal doses of both essential oils. These ﬁ ndings clearly show that in addition to the reported eﬀ ects of essential oils, oxidative stress and induction of free radical production could be another mode of the action of essential oils on insects.