Miracle-dz/UniFAR-FTUs huggingface dataset:
Kraken: An Efficient Engine with a Uniform Dataflow for Deep Neural Networks

Deep neural networks (DNNs) have been successfully employed in a multitude of applications with remarkable performance. As such performance is achieved at a significant computational cost, several embedded applications demand fast and efficient hardware accelerators for DNNs. Previously proposed application specific integrated circuit (ASIC) architectures strive to utilize arrays of hundreds of processing elements (PEs) and reduce power-hungry DRAM accesses using multiple dataflows requiring complex PE architectures. These consume significant area and reduce the maximum clock frequency. This paper introduces the Kraken architecture, which optimally processes the convolutional layers, fully-connected layers, and matrix products of any DNN through a hardware-friendly uniform dataflow. This enables maximal data reuse of weights, inputs, and outputs, with a bare-bones PE design and on-the-fly dynamic reconfiguration. Kraken, implemented in 65-nm CMOS technology at 400 MHz, packs 672 PEs in 7.3 mm2, with a peak performance of 537.6 Gops. Kraken processes the convolutional layers of AlexNet, VGG-16, and ResNet-50 at 336.6, 17.5, and 64.2 frames/s, respectively, hence outperforming the state-of-the-art ASIC architectures in terms of overall performance efficiency, DRAM accesses, arithmetic intensity, and throughput, with 5.8x more Gops/mm2 and 1.6x more Gops/W.
Simulations of Stochastic Long-term Variability in Leptonic Models for External-Compton and Synchrotron Self-Compton Dominated Blazars

In this work we investigate the nature of multiwavelength variability of blazars from a purely numerical approach. We use a time-dependent one-zone leptonic blazar emission model to simulate multiwavelength variability by introducing stochastic parameter variations in the emission region. These stochastic parameter variations are generated by Monte Carlo methods and have a characteristic power-law index of α = −2 in their power spectral densities. We include representative blazar test cases for a flat spectrum radio quasar and a high-synchrotron peaked BL Lacertae object for which the high-energy component of the spectral energy distribution is dominated by external-Compton and synchrotron self-Compton emission, respectively. The simulated variability is analyzed in order to characterize the distinctions between the two blazar cases and the physical parameters driving the variability. We show that the variability’s power spectrum is closely related to underlying stochastic parameter variations for both cases. Distinct differences between the different progenitor variations are present in the multiwavelength cross-correlation functions.
Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing

Currently, BIO-based and tuple-based approaches perform quite well on the span-based semantic role labeling (SRL) task. However, the BIO-based approach usually needs to encode a sentence once for each predicate when predicting its arguments, and the tuple-based approach has to deal with a huge search space of O ( n 3 ) , greatly reducing the training and inference efﬁciency. The parsing speed is less than 50 sentences per second. Moreover, both BIO-based and tuple-based approaches usually consider only local structural information when making prediction. This paper proposes to cast end-to-end span-based SRL as a graph parsing task. Based on a novel graph representation schema, we present a fast and accurate SRL parser on the shoulder of recent works on high-order semantic dependency graph parsing. Moreover, we propose a constrained Viterbi procedure to ensure the le-gality of the output graph. Experiments on English CoNLL05 and CoNLL12 datasets show that our model achieves new state-of-the-art re-sults under both settings of without and with pre-trained language models, and can parse over 600 sentences per second.
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. Moreover, they need to acquire a diverse repertoire of general-purpose skills that allow composing long-horizon tasks by following unconstrained language instructions. In this letter, we present Composing Actions from Language and Vision (CALVIN) (Composing Actions from Language and Vision), an open-source simulated benchmark to learn long-horizon language-conditioned tasks. Our aim is to make it possible to develop agents that can solve many robotic manipulation tasks over a long horizon, from onboard sensors, and specified only via human language. CALVIN tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets and supports flexible specification of sensor suites. We evaluate the agents in zero-shot to novel language instructions and to novel environments. We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.
Consistent Training and Decoding for End-to-End Speech Recognition Using Lattice-Free MMI

Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to introduce LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1% / 4.4% on Aishell-1 dev/test set; significant error reduction is also achieved on Aishell-2 and Librispeech datasets over strong baselines. Code is released1.
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning

Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an ML model, risk violating membership privacy. ML model builders need a principled definition of a metric to quantify the membership privacy risk of (a) individual training data records, (b) computed independently of specific MIAs, (c) which assesses susceptibility to different MIAs, (d) can be used for different applications, and (e) efficiently. None of the prior membership privacy risk metrics simultaneously meet all these requirements. We present SHAPr, a membership privacy metric based on Shapley values which is a leave-one-out (LOO) technique, originally intended to measure the contribution of a training data record on model utility. We conjecture that contribution to model utility can act as a proxy for memorization, and hence represent membership privacy risk. Using ten benchmark datasets, we show that SHAPr is indeed effective in estimating susceptibility of training data records to MIAs. We also show that, unlike prior work, SHAPr is significantly better in estimating susceptibility to newer, and more effective MIA. We apply SHAPr to evaluate the efficacy of several defenses against MIAs: using regularization and removing high risk training data records. Moreover, SHAPr is versatile: it can be used for estimating vulnerability of different subgroups to MIAs, and inherits applications of Shapley values (e.g., data valuation). We show that SHAPr has an acceptable computational cost (compared to naive LOO), varying from a few minutes for the smallest dataset to ~92 minutes for the largest dataset.
Self‐supervised graph learning for occasional group recommendation

As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold‐start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high‐quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high‐order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high‐order neighbors with few interactions. Motivated by the self‐supervised learning technique, which is able to find the correlations within the data itself, we propose a self‐supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold‐start users/items/groups. To explicitly enhance the high‐order cold‐start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self‐attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state‐of‐the‐art methods.
Value at Risk Models in Finance

The main objective of this paper is to survey and evaluate the performance of the most popular univariate VaR methodologies, paying particular attention to their underlying assumptions and to their logical flaws. In the process, we show that the Historical Simulation method and its variants can be considered as special cases of the CAViaR framework developed by Engle and Manganelli (1999). We also provide two original methodological contributions. The first one introduces the extreme value theory into the CAViaR model. The second one concerns the estimation of the expected shortfall (the expected loss, given that the return exceeded the VaR) using a regression technique. The performance of the models surveyed in the paper is evaluated using a Monte Carlo simulation. We generate data using GARCH processes with different distributions and compare the estimated quantiles to the true ones. The results show that CAViaR models perform best with heavy-tailed DGP. JEL Classification: C22, G22
Disaster week: A case study immersing first year engineering students in a disaster context to measure communication skills

Context: Communication is vitally important in any engineering role, a fact which students studying engineering do not tend to appreciate until near the end of their diploma or degree. This paper will focus on a first year module, Technical Literacy which is composed of 50 % communication and 50 % drawing. Because Technical Literacy is internally assessed students place more emphasis on the drawing, losing focus on the communication aspects. This paper will discuss the immersion of first year New Zealand Diploma of Engineering students (NZDE) in a five day intensive disaster scenario (modelled from Saemundsdottir, Matthiasdottir, Audunsson, and Saevarsdottir, (2012)) to actively measure an individual student's communication skills. Purpose or goal: The purpose of the change in practice was twofold; to enhance the relevance of engineering communication with the students, as well as creating an immersed context to encourage completion of course material. Approach: Both quantitative and qualitative data will be used to analyse the change in teaching practice. Qualitative data in the form of student questionnaires during the disaster week and individual reflections once the week was completed will be used to summarise student perceptions of the scenario to enhance communication skills. Quantitative data in the form of student completions will be compared with previous years. Final (communication) marks will also be compared though this is incidental to the research. Actual or anticipated outcomes: Student completions of key objectives within communication rose (final statistics yet to be given) Communication tutors who help run the course were satisfied that the quality of the work was at least on a par if not better than previous years (final marks yet to be determined) The importance and self-awareness of communication skills within engineering students has increased as evidenced in student reflections. Summary: Students enjoyed the disaster week scenario, being placed in a highly intense environment with students they do not normally work with. Students successfully managed the scenario submitting components for assessment as the week passed. Completions of assessment were very good, as well as increased student perceptions of the role of communication within an engineering environment. Disaster week was counted as a success by all involved (engineering/communication tutors & students) and will run again next year.
Deep Brain Stimulation with a Computational Model for the Cortex-Thalamus-Basal-Ganglia System and Network Dynamics of Neurological Disorders

Deep brain stimulation (DBS) can alleviate the movement disorders like Parkinson’s disease (PD). Indeed, it is known that aberrant beta (13-30 Hz) oscillations and the loss of dopaminergic neurons in the basal ganglia-thalamus (BGTH) and cortex characterize the akinesia symptoms of PD. However, the relevant biophysical mechanism behind this process still remains unclear. Based on the prior striatal inhibitory model, we propose an extended BGTH model incorporating medium spine neurons (MSNs) and fast-spiking interneurons (FSIs) along with the effect of DBS. We are focusing in this paper on an open-loop DBS mode, where the stimulation parameters stay constant independent of variations in the disease state, and modifications of parameters rely mainly on trial and error of medical experts. Additionally, we propose a novel combined model of the cerebellar-basal-ganglia thalamocortical network, MSNs, and FSIs and show new results that indicate that Parkinsonian oscillations in the beta-band frequency range emerge from the dynamics of such a network. Our model predicts that DBS can be used to suppress beta oscillations in globus pallidus pars interna (GPi) neurons. This research will help our better understanding of the changes in the brain activity caused by DBS, providing new insight for studying PD in the future.
HTMOT: Hierarchical Topic Modelling over Time

Topic models provide an efficient way of extracting insights from text and supporting decision-making. Recently, novel methods have been proposed to model topic hierarchy or temporality. Modeling temporality provides more precise topics by separating topics that are characterized by similar words but located over distinct time periods. Conversely, modeling hierarchy provides a more detailed view of the content of a corpus by providing topics and sub-topics. However, no models have been proposed to incorporate both hierarchy and temporality which could be beneficial for applications such as environment scanning. Therefore, we propose a novel method to perform Hierarchical Topic Modelling Over Time (HTMOT). We evaluate the performance of our approach on a corpus of news articles using the Word Intrusion task. Results demonstrate that our model produces topics that elegantly combine a hierarchical structure and a temporal aspect. Furthermore, our proposed Gibbs sampling implementation shows competitive performance compared to previous state-of-the-art methods.
MODAL ANALYSIS OF ACOUSTIC EMISSION SIGNALS

This report shows the results of a continuing study to determine the type of waves present in plates of different thickness created by breaking pencil leads in-plane (IP) and out-of-plane (OOP) on the plates and by detecting the resulting stress waves with a high fidelity acoustic-emission transducer mounted on the edge and surface of the plates. The results show that the extensional velocity over the range of plate thickness from 3.13 to 12.5 mm does not change, and the flexure wave is dispersive in accordance with plate wave theory. The procedure in this work split the signal into two frequency ranges, a high frequency range with a 100 kHz high-pass filter and a low frequency range with a 20-70 kHz bandpass filter. This frequency split provided an easy method of measuring the velocity of the low frequency flexure wave. A bulk shear wave, not predicted by plate wave theory was observed along with the extensional wave in the high frequency channel. In order to confirm the presence of the shear wave, two shear plates, one with particle motion vertical to the plane of the plate and the other parallel to the plane of the plate were attached to the end of one of the bars. These transducers were used as receivers for IP and OOP pencil-lead breaks, and also as transmitters excited by a 150-V spike pulse. Both the Sv and Sh waves, created with pulsing, could be detected with a transducer mounted on the surface with petroleum jelly. The Sh wave had a much higher amplitude than the Sv wave. The results show that mode conversion of the Sv wave to a flexure wave occurs, as it propagates down the bar, which partially accounts for its loss of amplitude. From this study, it is suggested that the crack growth signal detected by most AE tests on large thin-wall structures utilizing 100 kHz high-pass filtering, resonant transducers, and large transducer spacing, is the Sh wave created by crack propagation.
Traversing Time with Multi-Resolution Gaussian Process State-Space Models

Gaussian Process state-space models capture complex temporal dependencies in a principled manner by placing a Gaussian Process prior on the transition function. These models have a natural interpretation as discretized stochastic differential equations, but inference for long sequences with fast and slow transitions is difficult. Fast transitions need tight discretizations whereas slow transitions require backpropagating the gradients over long subtrajectories. We propose a novel Gaussian process state-space architecture composed of multiple components, each trained on a different resolution, to model effects on different timescales. The combined model allows traversing time on adaptive scales, providing efficient inference for arbitrarily long sequences with complex dynamics. We benchmark our novel method on semi-synthetic data and on an engine modeling task. In both experiments, our approach compares favorably against its state-of-the-art alternatives that operate on a single time-scale only.
A study of the factors affecting NO(x) emissions in lean premixed turbine combustors

Data from a Solar Turbines Mars premixer are analyzed to identify the effect of combustor inlet pressure, inlet temperature, equivalence ratio, residence time, and unmixedness on lean premixed (LP) NOX emissions. Examination of these and additional data available in the open literature result in identification of the characteristic times associated with the chemical kinetic and fluid mechanic subprocesses of NO formation in lean premixed combustors. The times are combined into a semi-empirical characteristic time model that can correlate NOX emissions from a given lean premixed combustor with negligible fuel/air unmixedness. The utility and limitations of such a model are demonstrated through the analysis of data from several lean premixed combustion devices. Based on these results, it is evident that resolution of issues pertaining to the definition of a characteristic length scale in the LP NO X characteristic time model and the quantification of "negligible" awaits additional testing.
Shallow trimers of two identical fermions and one particle in resonant regimes

We consider two identical fermions interacting in the p-wave channel. Each fermion also interacts with another particle in the vicinity of an s-wave resonance. We find that in addition to the Kartavtsev-Malykh universal trimer states resulting from the s-wave particle-fermion interaction, the fermion-fermion p-wave interaction induces one or two shallow trimers in a large domain of the control parameters, including a borromean regime where the ground-state trimer exists in the absence of dimer at any mass ratio between the fermions and the particle. A generic picture of the trimer spectrum emerges from this work in terms of the low-energy parameters of the interactions.
Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation

Speech-driven 3D facial animation with accurate lip synchronization has been widely studied. However, synthesizing realistic motions for the entire face during speech has rarely been explored. In this work, we present a joint audio-text model to capture the contextual information for expressive speech-driven 3D facial animation. The existing datasets are collected to cover as many different phonemes as possible instead of sentences, thus limiting the capability of the audio-based model to learn more diverse contexts. To address this, we propose to leverage the contextual text embeddings extracted from the powerful pre-trained language model that has learned rich contextual representations from large-scale text data. Our hypothesis is that the text features can disambiguate the variations in upper face expressions, which are not strongly correlated with the audio. In contrast to prior approaches which learn phoneme-level features from the text, we investigate the high-level contextual text features for speech-driven 3D facial animation. We show that the combined acoustic and textual modalities can synthesize realistic facial expressions while maintaining audio-lip synchronization. We conduct the quantitative and qualitative evaluations as well as the perceptual user study. The results demonstrate the superior performance of our model against existing state-of-the-art approaches.
CBI-time-changed Lévy processes for multi-currency modeling

We develop a stochastic volatility framework for modeling multiple currencies based on CBI-time-changed Lévy processes. The proposed framework captures the typical risk characteristics of FX markets and is coherent with the symmetries of FX rates. Moreover, due to the self-exciting behavior of CBI processes, the volatilities of FX rates exhibit self-exciting dynamics. By relying on the theory of affine processes, we show that our approach is analytically tractable and that the model structure is invariant under a suitable class of risk-neutral measures. A semi-closed pricing formula for currency options is obtained by Fourier methods. We propose two calibration methods, also by relying on deep-learning techniques, and show that a simple specification of the model can achieve a good fit to market data on a currency triangle.
Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification

State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited to small closed vocabularies which are far from enough for natural communication. In addition, most of the high-performing approaches require data from invasive devices (e.g., ECoG). In this paper, we extend the problem to open vocabulary Electroencephalography(EEG)-To-Text Sequence-To-Sequence decoding and zero-shot sentence sentiment classification on natural reading tasks. We hypothesis that the human brain functions as a special text encoder and propose a novel framework leveraging pre-trained language models (e.g., BART). Our model achieves a 40.1% BLEU-1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines. Furthermore, we show that our proposed model can handle data from various subjects and sources, showing great potential for a high-performance open vocabulary brain-to-text system once sufficient data is available. The code is made publicly available for research purpose at https://github.com/MikeWangWZHL/EEG-To-Text.