Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.
Two-loop renormalization and mixing of gluon and quark energy-momentum tensor operators

In this paper, we present one- and two-loop results for the renormalization of the gluon and quark gauge-invariant operators which appear in the definition of the QCD energy-momentum tensor, in dimensional regularization. To this end, we consider a variety of Green's functions with different incoming momenta. We identify the set of twist-2 symmetric traceless and flavor singlet operators which mix among themselves and we calculate the corresponding mixing coefficients for the nondiagonal components. We also provide results for some appropriate regularization-independent (${\mathrm{RI}}^{\ensuremath{'}}$)-like schemes, which address this mixing, and we discuss their application to nonperturbative studies via lattice simulations. Finally, we extract the one- and two-loop expressions of the conversion factors between the proposed ${\mathrm{RI}}^{\ensuremath{'}}$ and the $\overline{\mathrm{MS}}$ schemes. From our results regarding the $\overline{\mathrm{MS}}$-renormalized Green's functions, one can easily derive conversion factors relating numerous variants of ${\mathrm{RI}}^{\ensuremath{'}}$-like schemes to $\overline{\mathrm{MS}}$. To make our results easily accessible, we also provide them in the form of a Mathematica input file and, also as Supplemental Material, an equivalent text file.
Predicting traffic overflows on private peering

Large content providers and content distribution network operators usually connect with large Internet service providers (eyeball networks) through dedicated private peering. The capacity of these private network interconnects is provisioned to match the volume of the real content demand by the users. Unfortunately, in case of a surge in traffic demand, for example due to a content trending in a certain country, the capacity of the private interconnect may deplete and the content provider/distributor would have to reroute the excess traffic through transit providers. Although, such overflow events are rare, they have significant negative impacts on content providers, Internet service providers, and end-users. These include unexpected delays and disruptions reducing the user experience quality, as well as direct costs paid by the Internet service provider to the transit providers. If the traffic overflow events could be predicted, the Internet service providers would be able to influence the routes chosen for the excess traffic to reduce the costs and increase user experience quality. In this article we propose a method based on an ensemble of deep learning models to predict overflow events over a short term horizon of 2-6 hours and predict the specific interconnections that will ingress the overflow traffic. The method was evaluated with 2.5 years' traffic measurement data from a large European Internet service provider resulting in a true-positive rate of 0.8 while maintaining a 0.05 false-positive rate. The lockdown imposed by the COVID-19 pandemic reduced the overflow prediction accuracy. Nevertheless, starting from the end of April 2020 with the gradual lockdown release, the old models trained before the pandemic perform equally well.
DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis

Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns. However, since financial datasets have very low signal-to-noise ratio and are non-stationary, complex models are often very prone to overfitting and suffer from instability issues. Moreover, as various machine learning and data mining tools become more widely used in quantitative trading, many trading firms have been producing an increasing number of features (aka factors). Therefore, how to automatically select effective features becomes an imminent problem. To address these issues, we propose DoubleEnsemble, an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection. Specifically, we identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. Our model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction. We conduct extensive experiments, including price prediction for cryptocurrencies and stock trading, using both DNN and gradient boosting decision tree as base models. Our experiment results demonstrate that DoubleEnsemble achieves a superior performance compared with several baseline methods.
Dust Attenuation Curves at z ∼ 0.8 from LEGA-C: Precise Constraints on the Slope and 2175Å Bump Strength

We present a novel approach to measure the attenuation curves of 485 individual star-forming galaxies with > 1010 M⊙ based on deep optical spectra from the VLT/VIMOS LEGA-C survey and multiband photometry in the COSMOS field. Most importantly, we find that the attenuation curves in the rest-frame 3000–4500 Å range are typically almost twice as steep as the Milky Way, LMC, SMC, and Calzetti attenuation curves, which is in agreement with recent studies of the integrated light of present-day galaxies. The attenuation at 4500 Å and the slope strongly correlate with the galaxy inclination: face-on galaxies show less attenuation and steeper curves compared to edge-on galaxies, suggesting that geometric effects dominate observed variations in attenuation. Our new method produces 2175 Å UV bump detections for 260 individual galaxies. Even though obvious correlations between UV bump strength and global galaxy properties are absent, strong UV bumps are most often seen in face-on, lower-mass galaxies (10 < log10(M*/M⊙) < 10.5) with low overall attenuation. Finally, we produce a typical attenuation curve for star-forming galaxies at z ∼ 0.8; this prescription represents the effect of dust on the integrated spectral energy distributions of high-redshift galaxies more accurately than commonly used attenuation laws.
INFLUENCE OF MATERNAL ENVIRONMENT ON SALINITY AND DROUGHT TOLERANCE OF ANNUAL SOWTHISTLE (SONCHUS OLERACEUS L.) AT GERMINATION AND EMERGENCE STAGE

هدیکچ : هلحرم رد یکشخ و يروش شنت هب تمواقم رب يردام هایگ طیحم ریثات یسررب روظنم هب هناوج ي زبـس و ینز هدوت ود روذب ،کغیتریش زره فلع ندش هقطنم ود زا کغیتریش ي ًلاماـک یمیلقا تایصوصخ اب دنجریب و زاوها ي عمج توافتم يروآ ب هناگادج شیامزآ ود و هدش ه اب یفداصت لآماک حرط بلاق رد لیروتکاف تروص 3 رد رارـکت دیدرگ ارجا دنجریب هاگشناد يزرواشک هدکشناد یتاقیقحت هاگشیامزآ . تظلغ لماش يروش حوطس ياه 0 ، 10 ، 20 ، 40 ، 80 ، 160 ، 240 و 320 یلیم رلک رلاوم دی لیـسناتپ لماـش یکشخ حوطس و میدس ياـه يزمـسا 0 ، 1 / 0 ، 2 / 0 ، 4 / 0 ، 6 / 0 ، 8 / 0 و 1 دوب لاگساپاگم . جیاتن ناشن قیقحت نیا داد شنت رد هک تافص یکشخو يروش دیدش ياه لماش یسررب دروم : هناوج دصرد هناوج تعرس ،ینز هشیر لوط ،ینز هقاس لوط ،هچ هینب و هچ شهاـک هچهایگ ي ینعم رایسب يراد ) 01 / 0 (P≤ دندرک ادیپ . لدم ساسا رب و کـمن زا یتظلغ ،هدش هداد شزارب کیتسجل نیـنچمه تعنامم يارب هک یکشخ شنت زا یحطس 50 هناوج رثکادح يدصرد هدوت رد ینز بـتارم هـب ،دوب مزلا زاوها ي هدوت زا رتشیب دوب دنجریب ي . هدوت رتشیب تمواقم زا یکاح شیامزآ نیا جیاتن یکـشخ و يروـش شنت هب زاوها ي هدوت هب تبسن جریب ِي هـظحلام لـباق رـثا يردام هایگ طیحم هک دیدرگ صخشم اذل و دوب دن تایـصوصخ رـب يا هناوج همانرب رد یتسیاب و هتشاد کغیتریش روذب ینز يزیر فلع نیا لرتنک و تیریدم ياه دریگب رارق رظن دم زره . هژاو يدیلک ياه : هناوج تعرس هناوج دصرد ،ینز کیتسجل لدم ،ینز .
Gaia DR2 data and the evolutionary status of eight high-velocity hot post-AGB candidates

From Gaia DR 2 data of eight high velocity hot post-AGB candidates LS 3593, LSE 148, LS 5107, HD 172324, HD 214539, LS IV -12 111, LS III +52 24, and LS 3099, we found that six of them have accurate parallaxes which made it possible to derive their distances, absolute visual magnitudes (M_V) and luminosity (log L/L_sun). Except LS 5107 all the remaining seven stars have accurate effective temperature (T_eff) in the literature. Some of these stars are metal-poor and some of them do not have circumstellar dust shells. In the past the distances of some stars were estimated to be 6~kpc which we find it to be incorrect. The accurate Gaia DR2 parallaxes show that they are relatively nearby post-AGB stars. When compared with post-AGB evolutionary tracks we find their initial masses in the range of 1M_sun to 2M_sun. We find the luminosity of LSE 148 to be significantly lower than that of post-AGB stars, suggesting that this is a post-horizontal branch star or post-early-AGB star. LS 3593 and LS 5107 are new high velocity hot post-AGB stars from Gaia DR2.
Long Time Influence of Small Perturbations and Motion on the Simplex of Invariant Probability Measures

A general approach to a broad class of asymptotic problems related to long-time influence of small perturbations, of both deterministic and stochastic type, is presented in the paper. The main characteristic of this influence is a limiting motion on the simplex of invariant probability measures of the non-perturbed system in an appropriate time scale. The main tools we use in the paper are limit theorems for large deviations, modified averaging principle, and diffusion approximation.
Exploiting Unsupervised Data for Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task. Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playing their maximum effect. In this paper, we propose a novel approach to leverage unsupervised conversation data, which is more accessible. Specifically, we propose the Conversation Completion (ConvCom) task, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation. Then, we Pre-train a basic COntext-Dependent Encoder (Pre-CODE) on the ConvCom task. Finally, we fine-tune the Pre-CODE on the datasets of ERC. Experimental results demonstrate that pre-training on unsupervised data achieves significant improvement of performance on the ERC datasets, particularly on the minority emotion classes.
Control of the exciton valley dynamics in van der Waals heterostructures

The exciton valley dynamics in van der Waals heterostructures with transition metal dichalcogenides monolayers is driven by the long-range exchange interaction between the electron and the hole in the exciton. It couples the states active in the opposite circular polarizations resulting in the longitudinal-transverse splitting of excitons propagating in the monolayer plane. Here we study theoretically the effect of the dielectric environment on the long-range exchange interaction and demonstrate how the encapsulation in the hexagonal boron nitride modifies the exciton longitudinal-transverse splitting. We calculate the exciton spin/valley polarization relaxation due to the long-range exchange interaction and demonstrate that the variation of the monolayer environment results in significant, up to five-fold, enhancement of the exciton valley polarization lifetime.
Towards a Cognitive Computational Neuroscience of Auditory Phantom Perceptions.

In order to gain a mechanistic understanding of how tinnitus emerges in the brain, we must build biologically plausible computational models that mimic both tinnitus development and perception, and test the tentative models with brain and behavioral experiments. With a special focus on tinnitus research, we review recent work at the intersection of artificial intelligence, psychology and neuroscience, indicating a new research agenda that follows the idea that experiments will yield theoretical insight only when employed to test brain-computational models. This view challenges the popular belief, that tinnitus research is primarily data limited, and that producing large, multi-modal, and complex datasets, analyzed with advanced data analysis algorithms, will finally lead to fundamental insights into how tinnitus emerges. However, there is converging evidence that although modern technologies allow assessing neural activity in unprecedentedly rich ways in both, animals and humans, empirical testing one verbally defined hypothesis about tinnitus after another, will never lead to a mechanistic understanding. Instead, hypothesis testing needs to be complemented with the construction of computational models that generate verifiable predictions. We argue, that even though, contemporary artificial intelligence and machine learning approaches largely lack biological plausibility, the models to be constructed will have to draw on concepts from these fields, since they have already proven to do well in modeling brain function. Nevertheless, biological fidelity will have to be increased successively, leading to ever better and fine-grained models, allowing at the end for even testing possible treatment strategies in silico, before application in animal or patient studies.
Discovering Object-centric Petri Nets

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed.
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks

Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a scalable time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, the Hadamard product of the DCT coefficients and a single frequency base, taken in order, is given to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference.
Segment Inequality and Almost Rigidity Structures for Integral Ricci Curvature

We will show the Cheeger–Colding segment inequality for manifolds with integral Ricci curvature bound. By using this segment inequality, the almost rigidity structure results for integral Ricci curvature will be derived by a similar method as in [1]. And the sharp Hölder continuity result of [7] holds in the limit space of manifolds with integral Ricci curvature bound.
Direct Radio Discovery of a Cold Brown Dwarf

Magnetospheric processes seen in gas giants such as aurorae and circularly polarized cyclotron maser radio emission have been detected from some brown dwarfs. However, previous radio observations targeted known brown dwarfs discovered via their infrared emission. Here we report the discovery of BDR J1750+3809, a circularly polarized radio source detected around 144 MHz with the Low-Frequency Array (LOFAR) telescope. Follow-up near-infrared photometry and spectroscopy show that BDR J1750+3809 is a cold methane dwarf of spectral type T6.5 ± 1 at a distance of . The quasi-quiescent radio spectral luminosity of BDR J1750+3809 is ≈5 × 1015 erg s−1 Hz−1, which is over two orders of magnitude larger than that of the known population of comparable spectral type. This could be due to a preferential geometric alignment or an electrodynamic interaction with a close companion. In addition, as the emission is expected to occur close to the electron gyrofrequency, the magnetic field strength at the emitter site in BDR J1750+3809 is B ≳ 25 G, which is comparable to planetary-scale magnetic fields. Our discovery suggests that low-frequency radio surveys can be employed to discover substellar objects that are too cold to be detected in infrared surveys.
Multi-turn Response Selection Using Dialogue Dependency Relations

Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.
Strong Coupling Quantum Thermodynamics with Renormalized Hamiltonian and Temperature.

We develop the strong coupling quantum thermodynamics based on the solution of the exact master equation. We find that both the Hamiltonian and the temperature must be renormalized due to the system-reservoir couplings. With the renormalized Hamiltonian and temperature, the exact steady state of open quantum systems can be expressed as a standard Gibbs state. The exact steady-state particle distributions obey the Bose-Einstein distribution or the Fermi-Dirac distribution only for the renormalized energy and temperature. In this formulation, heat and work are quantum mechanically defined, from which we compute the specific heat and examine the consistency of the theory. Consequently, thermodynamic laws and statistical mechanics emerge naturally and rigorously from quantum evolution of open systems.
CardioXNet: A Novel Lightweight CRNN Framework for Classifying Cardiovascular Diseases from Phonocardiogram Recordings

The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram(PCG) signals has been historically applied in this domain owing to its simplicity and cost-effectiveness. However, insufficiency of expert physicians and human subjectivity affect the applicability of this technique, especially in the low-resource settings. For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse using raw PCG signal. The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning. The first phase mainly focuses on automated feature extraction and it has been implemented in a modular way with three parallel CNN pathways i.e., frequency feature extractor (FFE), pattern extractor (PE) and adaptive feature extractor (AFE). 1D-CNN based FFE and PE respectively learn the coarse and fine-grained features from the PCG while AFE explores the salient features from variable receptive fields involving 2D-CNN based squeezeexpansion. Thus, in the representation learning phase, the network extracts efficient time-invariant features and converges with great rapidity. In the sequential residual learning phase,because of the bidirectional-LSTMs and the skip connection, the network can proficiently extract temporal features. The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.6% accuracy, 99.6% precision, 99.6% recall and 99.4% F1-score on an average while being computationally comparable.