Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesoscopic modeling of hidden spiking neurons

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous latent variable models, neuLVM can be explicitly mapped to a recurrent, multi-population SNN, giving it a transparent biological interpretation. We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking photo-stimulation.
Quantum error correction in a time-dependent transverse-field Ising model

We describe a simple quantum error correcting code built out of a time-dependent transverse ﬁeld Ising model. The code is similar to a repetition code, but has two advantages: an N -qubit code can be implemented with a ﬁnite-depth spatially local unitary circuit, and it can subsequently protect against both X and Z errors if N ≥ 10 is even. We propose an implementation of this code with 10 ultracold Rydberg atoms in optical tweezers, along with further generalizations of the code.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

Multi-behavior recommendation exploits multiple types of user-item interactions, such as view and cart, to learn user preferences and has demonstrated to be an effective solution to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits their personal preferences. Those interaction behaviors often obey a certain order, and more importantly, different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model named Cascading Residual Graph Convolutional Network (CRGCN for short) for multi-behavior recommendation, which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters (with comparison to the single-behavior based recommendation model). In particular, we design a cascading residual graph convolutional network (GCN) structure, which enables our model to learn user preferences by continuously refining the embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on three real-world benchmark datasets show that CRGCN can substantially outperform the state-of-the-art methods, achieving 24.76%, 27.28%, and 25.10% relative gains on average in terms of HR@K (K = {10,20,50,80}) over the best baseline across the three datasets. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.
Shift invariance of half space integrable models

We formulate and establish symmetries of certain integrable half space models, analogous to recent results on symmetries for models in a full space. Our starting point is the colored stochastic six vertex model in a half space, from which we obtain results on the asymmetric simple exclusion process, as well as for the beta polymer through a fusion procedure which may be of independent interest. As an application, we establish a distributional identity between the absorption time in a type B analogue of the oriented swap process and last passage times in a half space, establishing the Baik–Ben Arous–Péché phase transition for the absorption time. The proof uses Hecke algebras and integrability of the six vertex model through the Yang–Baxter and reﬂection equations.
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search

Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner (SPL) machine to discover the mathematical structure of nonlinear dynamics. The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search (MCTS) agent to explore optimal expression trees based on measurement data. The MCTS agent obtains an optimistic selection policy through the traversal of expression trees, featuring the one that maps to the arithmetic expression of underlying physics. Salient features of the proposed framework include search flexibility and enforcement of parsimony for discovered equations. The efficacy and superiority of the SPL machine are demonstrated by numerical examples, compared with state-of-the-art baselines.
Continual evaluation for lifelong learning: Identifying the stability gap

Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery. We refer to this intriguing but potentially problematic phenomenon as the stability gap. The stability gap had likely remained under the radar due to standard practice in the field of evaluating continual learning models only after each task. Instead, we establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance. Empirically we show that experience replay, constraint-based replay, knowledge-distillation, and parameter regularization methods are all prone to the stability gap; and that the stability gap can be observed in class-, task-, and domain-incremental learning benchmarks. Additionally, a controlled experiment shows that the stability gap increases when tasks are more dissimilar. Finally, by disentangling gradients into plasticity and stability components, we propose a conceptual explanation for the stability gap.
Constraints on the onset of color transparency from quasi-elastic 12 C ( e, e (cid:48) p ) up to

Quasi-elastic scattering on 12 C( e, e (cid:48) p ) was measured in Hall C at Jeﬀerson Lab for space-like 4-momentum transfer squared Q 2 in the range of 8–14.2 (GeV/ c ) 2 with proton momenta up to 8.3 GeV/ c . The experiment was carried out in the upgraded Hall C at Jeﬀerson Lab. It used the existing high momentum spectrometer and the new super high momentum spectrometer to detect the scattered electrons and protons in coincidence. The nuclear transparency was extracted as the ratio of the measured yield to the yield calculated in the plane wave impulse approximation. Additionally, the transparency of the 1 s 1 / 2 and 1 p 3 / 2 shell protons in 12 C was extracted, and the asymmetry of the missing momentum distribution was examined for hints of the quantum chromodynamics prediction of Color Transparency. All of these results were found to be consistent with traditional nuclear physics and inconsistent with the onset of Color Transparency.
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality

As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantization levels, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements (PE) into the accelerator design space while having accurate and fast power, performance, and area models. In this work, we present QADAM, a highly parameterized quantization-aware power, performance, and area modeling framework for DNN accelerators. Our framework can facilitate future research on design space exploration and Pareto-efficiency of DNN accelerators for various design choices such as bit precision, PE type, scratchpad sizes of PEs, global buffer size, number of total PEs, and DNN configurations. Our results show that different bit precisions and PE types lead to significant differences in terms of performance per area and energy. Specifically, our framework identifies a wide range of design points where performance per area and energy varies more than 5x and 35x, respectively. We also show that the proposed lightweight processing elements (LightPEs) consistently achieve Pareto-optimal results in terms of accuracy and hardware-efficiency. With the proposed framework, we show that LightPEs achieve on par accuracy results and up to 5.7x more performance per area and energy improvement when compared to the best INT16 based design.
THE EFFECTIVENESS OF MINDFULNESS TRAINING IN IMPROVING THE SELF-CONTROL OF SPEEDBOAT CREW IN BALI, INDONESIA

This study aims to test empirically the effectiveness of mindfulness training on improving the self-control of the crew of speedboat Company X in Bali, Indonesia. The experimental subjects in the study are 22 people who are in the sea transportation unit. They are divided into two groups, 12 of whom are in the experimental group and 10 in the control group. The data were collected by using a questionnaire with the Likert scale, consisting of 26 items with the reliability level of 0.889. The data processing used different tests. The test results of the average score of Mann-Withney of the experimental group is 16.50, which is higher than the average score of the Mann-Withney of the control group, which is 5.50, and this means that the average score of the self-control of the speedboat crew members who were treated with mindfulness training is higher than that of those who were not given mindfulness treatment. The result of U = 0.000, Z = -3.974, p (value), Asymp Sig/ significance coefficient <0.05 shows that there is a significant difference in the increase of self-control between the experimental group and the control group.
AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks

Benford's law describes the distribution of the first digit of numbers appearing in a wide variety of numerical data, including tax records, and election outcomes, and has been used to raise "red flags" about potential anomalies in the data such as tax evasion. In this work, we ask the following novel question: Given a large transaction or financial graph, how do we find a set of nodes that perform many transactions among each other that also deviate significantly from Benford's law? We propose the AntiBenford subgraph framework that is founded on well-established statistical principles. Furthermore, we design an efficient algorithm that finds AntiBenford subgraphs in near-linear time on real data. We evaluate our framework on both real and synthetic data against a variety of competitors. We show empirically that our proposed framework enables the detection of anomalous subgraphs in cryptocurrency transaction networks that go undetected by state-of-the-art graph-based anomaly detection methods. Our empirical findings show that our AntiBenford framework is able to mine anomalous subgraphs, and provide novel insights into financial transaction data. The code and the datasets are available at https://github.com/tsourakakis-lab/antibenford-subgraphs.
Understanding Metrics for Paraphrasing

Paraphrase generation is a difficult problem. This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is. Metrics for evaluation of paraphrasing quality is an on going research problem. Most of the existing metrics in use having been borrowed from other tasks do not capture the complete essence of a good paraphrase, and often fail at borderline-cases. In this work, we propose a novel metric $ROUGE_P$ to measure the quality of paraphrases along the dimensions of adequacy, novelty and fluency. We also provide empirical evidence to show that the current natural language generation metrics are insufficient to measure these desired properties of a good paraphrase. We look at paraphrase model fine-tuning and generation from the lens of metrics to gain a deeper understanding of what it takes to generate and evaluate a good paraphrase.
X-ray emission of contact binary variables within 1 kpc

By assembling the largest sample to date of X-ray emitting EW-type binaries (EWXs), we carried out correlation analyses for the X-ray luminosity log$L_{\textrm{X}}$, and X-ray activity level log($L_{\textrm{X}}$/$L_{\textrm{bol}}$) versus the orbital period $P$ and effective temperature $T_{\rm eff}$. We find strong $P$-log$L_{\textrm{X}}$ and $P$-log($L_{\textrm{X}}$/$L_{\textrm{bol}}$) correlations for EWXs with $P$<0.44 days and we provide the linear parametrizations for these relations, on the basis of which the orbital period can be treated as a good predictor for log$L_{\textrm{X}}$ and log($L_{\textrm{X}}$/$L_{\textrm{bol}}$). The aforementioned binary stellar parameters are all correlated with log$L_{\textrm{X}}$, while only $T_{\rm eff}$ exhibits a strong correlation with log($L_{\textrm{X}}$/$L_{\textrm{bol}}$). Then, EWXs with higher temperature show lower X-ray activity level, which could indicate the thinning of the convective area related to the magnetic dynamo mechanism. The total X-ray luminosity of an EWX is essentially consistent with that of an X-ray saturated main sequence star with the same mass as its primary, which may imply that the primary star dominates the X-ray emission. The monotonically decreasing $P$-log($L_{\textrm{X}}$/$L_{\textrm{bol}}$) relation and the short orbital periods indicate that EWXs could all be in the X-ray saturated state, and they may inherit the changing trend of the saturated X-ray luminosities along with the mass shown by single stars. For EWXs, the orbital period, mass, and effective temperature increase in concordance. We demonstrate that the period $P=0.44$ days corresponds to the primary mass of $\sim1.1 \rm M_\odot$, beyond which the saturated X-ray luminosity of single stars will not continue to increase with mass. This explains the break in the positive $P$-log$L_{\textrm{X}}$ relation for EWXs with $P>0.44$ days.
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks

The last few years have witnessed an exponential rise in the propagation of offensive text on social media. Identification of this text with high precision is crucial for the well-being of society. Most of the existing approaches tend to give high toxicity scores to innocuous statements (e.g., “I am a gay man”). These false positives result from over-generalization on the training data where specific terms in the statement may have been used in a pejorative sense (e.g., “gay”). Emphasis on such words alone can lead to discrimination against the classes these systems are designed to protect. In this paper, we address the problem of offensive language detection on Twitter, while also detecting the type and the target of the offense. We propose a novel approach called SyLSTM, which integrates syntactic features in the form of the dependency parse tree of a sentence and semantic features in the form of word embeddings into a deep learning architecture using a Graph Convolutional Network. Results show that the proposed approach significantly outperforms the state-of-the-art BERT model with orders of magnitude fewer number of parameters.
More Recent Advances in (Hyper)Graph Partitioning

In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the past decade in practical algorithms for balanced (hyper)graph partitioning together with future research directions. Our work serves as an update to a previous survey on the topic [29]. In particular, the survey extends the previous survey by also covering hypergraph partitioning and has an additional focus on parallel algorithms.
Periodic structure with electrostatic forces: Interactions beyond the nearest neighbor

Periodic structures are a type of metamaterial in which the physical properties depend not only on the details of the unit cell but also on how unit cells are arranged and interact with each other. In conventional engineering structures, each unit cell interacts with adjacent cells. Methods developed for vibrational and wave propagation analysis in periodic engineering structures consider only nearest-neighbor interactions. The dispersion curves of such systems, in which only adjacent cells interact, have been extensively studied. Metamaterial properties depend on the interactions of a unit cell with other cells. Further interactions, and specifically, interactions beyond the closest neighbors, imply a more complex band structure and wave behavior. In this paper, an example class of such structures, in which electrostatic forces are the driving force, has been investigated. In this paper, properties affecting these periodic structures, such as elastic forces, have been investigated. An attractive property of such structures is that the band structures of such metamaterials can be tuned by changing electric voltages.
Two-Torsion Subgroups of some Modular Jacobians

We give a practical method to compute the 2-torsion subgroup of the Jacobian of a non-hyperelliptic curve of genus $3$, $4$ or $5$. The method is based on the correspondence between the 2-torsion subgroup and the theta hyperplanes to the curve. The correspondence is used to explicitly write down a zero-dimensional scheme whose points correspond to elements of the $2$-torsion subgroup. Using $p$-adic or complex approximations (obtained via Hensel lifting or homotopy continuation and Newton-Raphson) and lattice reduction we are then able to determine the points of our zero-dimensional scheme and hence the $2$-torsion points. We demonstrate the practicality of our method by computing the $2$-torsion of the modular Jacobians $J_{0}\left( N \right)$ for $N = 42, 55, 63, 72, 75$. As a result of this we are able to verify the generalised Ogg conjecture for these values.
Interpretation of Latent Codes in InfoGAN with SAR Images

Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect.
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection

Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven level-crossing ADC (LC-ADC) is exploited in the recording system, which utilizes the sparsity of ECG signal to enable compressive recording and save ADC energy during the silent signal period. Meanwhile, the proposed spiking convolutional neural network (SCNN) based neuromorphic arrhythmia detection method is inherently compatible with the spike-based output of LC-ADC, hence realizing accurate detection and low energy consumption at system level. Simulation results show that the proposed system with 5-bit LC-ADC achieves 88.6% reduction of sampled data points compared with Nyquist sampling in the MIT-BIH dataset, and 93.59% arrhythmia detection accuracy with SCNN, demonstrating the compression ability of LC-ADC and the effectiveness of system level co-design with SCNN.
SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/mask pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.