Miracle-dz/UniFAR-FTUs huggingface dataset:
Search-Based Serving Architecture of Embeddings-Based Recommendations

Over the past 10 years, many recommendation techniques have been based on embedding users and items in latent vector spaces, where the inner product of a (user,item) pair of vectors represents the predicted affinity of the user to the item. A wealth of literature has focused on the various modeling approaches that result in embeddings, and has compared their quality metrics, learning complexity, etc. However, much less attention has been devoted to the issues surrounding productization of an embeddings-based high throughput, low latency recommender system. In particular, how the system might keep up with the changing embeddings as new models are learnt. This paper describes a reference architecture of a high-throughput, large scale recommendation service which leverages a search engine as its runtime core. We describe how the search index and the query builder adapt to changes in the embeddings, which often happen at a different cadence than index builds. We provide solutions for both id-based and feature-based embeddings, as well as for batch indexing and incremental indexing setups. The described system is at the core of a Web content discovery service that serves tens of billions recommendations per day in response to billions of user requests.
Variation of mass accretion rate on to the white dwarf in the dwarf nova VW Hyi in quiescence

We have analysed a series of Suzaku data and one data set of XMM–Newton of the SU UMa type dwarf nova VW Hyi in optical quiescence. The observed spectra in the 0.2–10 keV band are moderately well represented by multitemperature thermal plasma emission models with a maximum temperature of 5–9 keV and bolometric luminosity of (2.4–5.2) × 1030 erg s−1. The mass accretion rate derived from the hard X-ray spectra does not show any clear trend as a function of time since the last superoutburst, in contradiction to theoretical predictions of the disc behaviour of an SU UMa type dwarf nova. The mass accretion rate, on the other hand, shows a clear declining trend with time since the last outburst (including the superoutburst). The rate of decline is of the same order as that evaluated from the hard X-ray light curves of the other two dwarf novae SS Cyg and SU UMa. The standard disc instability model, on the other hand, predicts that the mass accretion rate should increase throughout the optically quiescent phase. We need further observation and theoretical consideration to resolve this discrepancy.
Point-Voxel CNN for Efficient 3D Deep Learning

We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the irregular data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to largely reduce the irregular data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-art point-based models with 7x measured speedup on average. Remarkably, narrower version of PVCNN achieves 2x speedup over PointNet (an extremely efficient model) on part and scene segmentation benchmarks with much higher accuracy. We validate the general effectiveness of our PVCNN on 3D object detection: by replacing the primitives in Frustrum PointNet with PVConv, it outperforms Frustrum PointNet++ by 2.4% mAP on average with 1.5x measured speedup and GPU memory reduction.
Placement Delivery Arrays from Combinations of Strong Edge Colorings

It has recently been pointed out in both of the works [C. Shanguan, Y. Zhang, and G. Ge, IEEE Trans. Inform. Theory, 64(8):5755–5766 (2018)] and [Q. Yan, X. Tang, Q. Chen, and M. Cheng, IEEE Commun. Lett. , 22(2):236–239 (2018)] that placement delivery arrays (PDAs), as coined in [Q. Yan, M. Cheng, X. Tang, and Q. Chen, IEEE Trans. Inform. Theory, 63(9):5821–5833 (2017)], are equivalent to strong edge colorings of bipartite graphs. In this paper we consider various methods of combining two or more edge colorings of bipartite graphs to obtain new ones, and therefore new PDA’s. We find that combining PDAs in certain ways also gives a framework for obtaining PDA’s with more robust and flexible parameters. We investigate how the parameters of certain strong edge colorings change after being combined with another, and we compare the parameters of the resulting PDA’s with those of known ones.
Datapath error detection using hybrid detection approach for high-performance microprocessors

Error detection plays an important role in fault-tolerant computer systems. Two primary parameters concerned for error detection are the coverage and latency. In this paper, a new, hybrid error-detection approach offering a very high coverage with zero detection latency is proposed to protect the data paths of high-performance microprocessors. The feature of zero detection latency is essential to real-time error recovery. The hybrid error-detection approach is to combine the duplication with comparison, triple modular redundancy (TMR) and self-checking mechanisms to construct a formal framework, which allows the error-detection schemes of varying hardware complexity and performance to be incorporated. An experimental 32-bit VLIW core was employed to demonstrate the concept of hybrid detection approach. The hardware implementations in VHDL and simulated fault injection experiments were performed to measure the interesting design metrics, such as hardware overhead, performance degradation and error-detection coverage.
Pseudo random number generators: attention for a newly proposed generator

Xorshift128+ is a newly proposed pseudo random number generator (PRNG), which is now the standard PRNG in a number of platforms. We point out some flaws of such generators, propose a little slower replacements tiny Mersenne Twisters (tinyMTs), and argue on some morals on PRNGs.
The turbulent dynamo

Abstract The generation of a magnetic field in an electrically conducting fluid generally involves the complicated nonlinear interaction of flow turbulence, rotation and field. This dynamo process is of great importance in geophysics, planetary science and astrophysics, since magnetic fields are known to play a key role in the dynamics of these systems. This paper gives an introduction to dynamo theory for the fluid dynamicist. It proceeds by laying the groundwork, introducing the equations and techniques that are at the heart of dynamo theory, before presenting some simple dynamo solutions. The problems currently exercising dynamo theorists are then introduced, along with the attempts to make progress. The paper concludes with the argument that progress in dynamo theory will be made in the future by utilising and advancing some of the current breakthroughs in neutral fluid turbulence such as those in transition, self-sustaining processes, turbulence/mean-flow interaction, statistical and data-driven methods and maintenance and loss of balance.
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training

We present improvements in automatic speech recognition (ASR) for Somali, a currently extremely under-resourced language. This forms part of a continuing United Nations (UN) effort to employ ASR-based keyword spotting systems to support humanitarian relief programmes in rural Africa. Using just 1.57 hours of annotated speech data as a seed corpus, we increase the pool of training data by applying semi-supervised training to 17.55 hours of untranscribed speech. We make use of factorised time-delay neural networks (TDNN-F) for acoustic modelling, since these have recently been shown to be effective in resource-scarce situations. Three semi-supervised training passes were performed, where the decoded output from each pass was used for acoustic model training in the subsequent pass. The automatic transcriptions from the best performing pass were used for language model augmentation. To ensure the quality of automatic transcriptions, decoder confidence is used as a threshold. The acoustic and language models obtained from the semi-supervised approach show significant improvement in terms of WER and perplexity compared to the baseline. Incorporating the automatically generated transcriptions yields a 6.55\% improvement in language model perplexity. The use of 17.55 hour of Somali acoustic data in semi-supervised training shows an improvement of 7.74\% relative over the baseline.
Platypus: a Partially Synchronous Offchain Protocol for Blockchains

Offchain protocols aim at bypassing the scalability and privacy limitations of classic blockchains by allowing a subset of participants to execute multiple transactions outside the blockchain. While existing solutions like payment networks and factories depend on a complex routing protocol, other solutions simply require participants to build a \emph{childchain}, a secondary blockchain where their transactions are privately executed. Unfortunately, all childchain solutions assume either synchrony or a trusted execution environment. In this paper, we present Platypus a childchain that requires neither synchrony nor a trusted execution environment. Relieving the need for a trusted execution environment allows Platypus to ensure privacy without trusting a central authority, like Intel, that manufactures dedicated hardware chipset, like SGX. Relieving the need for synchrony means that no attacker can steal coins by leveraging clock drifts or message delays to lure timelocks. In order to prove our algorithm correct, we formalize the chilchain problem as a Byzantine variant of the classic Atomic Commit problem, where closing a childchain is equivalent to committing the whole set of payments previously recorded on the childchain ``atomically'' on the main chain. Platypus is resilience optimal and we explain how to generalize it to crosschain payments.
Entanglement criteria for two coupled atomic ensembles exhibiting Dicke-like quantum phase transition

Two interacting atomic ensembles display a Dicke-like quantum phase transition~(QPT) above a critical coupling strength. We show that an ensemble-ensemble entanglement accompanies the QPT. Inseparability onsets exactly at the critical coupling for the QPT and increases continuously with the coupling strength. Since the nature of entanglement at QPTs is extremely different than the one for spin-squeezed like inseparability, we derive three new criteria for the ensemble-ensemble entanglement, which witnesses the entanglement successfully. The advantage of this system for generating entanglement, compared to a superradiant QPT, is that it does not include photons which do leak in a cavity.
General framework for projection structures

In the first part, we develop a general framework for projection structures and study several inference problems within this framework. We propose procedures based on data dependent measures (DDM) and make connections with empirical Bayes and penalization methods. The main inference problem is the uncertainty quantification (UQ), but on the way we solve the estimation, DDM-contraction problems, and a weak version of the structure recovery problem. The approach is local in that the quality of the inference procedures is measured by the local quantity, the oracle rate, which is the best trade-off between the approximation error by a projection structure and the complexity of that approximating projection structure. Like in statistical learning settings, we develop distribution-free theory as no particular model is imposed, we only assume certain mild condition on the stochastic part of the projection predictor. We introduce the excessive bias restriction (EBR) under which we establish the local confidence optimality of the constructed confidence ball. The proposed general framework unifies a very broad class of high-dimensional models and structures, interesting and important on their own right. In the second part, we apply the developed theory and demonstrate how the general results deliver a whole avenue of local and global minimax results (many new ones, some known results from the literature are improved) for particular models and structures as consequences, including white noise model and density estimation with smoothness structure, linear regression and dictionary learning with sparsity structures, biclustering and stochastic block models with clustering structure, covariance matrix estimation with banding and sparsity structures, and many others. Various adaptive minimax results over various scales follow also from our local results.
Coexistence in a random growth model with competition

We consider a variation of the Hastings-Levitov model HL(0) for random growth in which the growing cluster consists of two competing regions. We allow the size of successive particles to depend both on the region in which the particle is attached, and the harmonic measure carried by that region. We identify conditions under which one can ensure coexistence of both regions. In particular, we consider whether it is possible for the process giving the relative harmonic measures of the regions to converge to a non-trivial ergodic limit.
Performance Comparison of Musical Instrument Family Classification Using Soft Set

Nowadays, it appears essential to design automatic and efficacious classification algorithm for the musical instruments. Automatic classification of musical instruments is made by extracting relevant features from the audio samples, afterward classification algorithm is used (using these extracted features) to identify into which of a set of classes, the sound sample is possible to fit. The aim of this paper is to demonstrate the viability of soft set for audio signal classification. A dataset of 104 (single monophonic notes) pieces of Traditional Pakistani musical instruments were designed. Feature extraction is done using two feature sets namely perception based and mel-frequency cepstral coefficients (MFCCs). In a while, two different classification techniques are applied for classification task, which are soft set (comparison table) and fuzzy soft set (similarity measurement). Experimental results show that both classifiers can perform well on numerical data. However, soft set achieved accuracy up to 94.26% with best generated dataset. Consequently, these promising results provide new possibilities for soft set in classifying musical instrument sounds. Based on the analysis of the results, this study offers a new view on automatic instrument classification.
A comparison of methods for supply current analysis

It is demonstrated that AC supply current analysis can be an effective method for testing complex digital devices in a technology other than CMOS. Experiments using a microprocessor demonstrate the effectiveness of this approach and its potential for reliability analysis and mixed-signal testing. Results from two different methods of waveform analysis are presented. The application of statistical signal detection methods is shown to provide superior fault detection, compared to AR (autoregressive) modeling. Because the signature is analyzed as a continuous-time signal and no assumptions are made regarding waveform shape or fault effects, it is believed that this method may be applied to the testing of both digital and analog devices, at various levels of integration. An additional benefit may be the capability of detecting minor current aberrations, resulting from activated failure mechanisms prior to functional failure. <<ETX>>
A low noise, low residual offset, chopped amplifier for mixed level applications

This paper describes the principle and the design of a CMOS low noise, low residual offset, chopped amplifier with a class AB output stage for noise and offset reduction in mixed analog digital applications. The operation is based on chopping and dynamic element matching to reduce noise and offset, without excessive increase of the charge injection residual offset. The main goal is to achieve low residual offsets by chopping at high frequencies reducing at the same time the 1/f noise of the amplifier. Measurements on a 0.8 /spl mu/m CMOS realization show reduction of 1/f noise and 18nV//spl radic/Hz residual thermal noise at low frequencies. The residual offset is lower than 100 /spl mu/V up to 8 MHz chopping frequency. Driving a 32 /spl Omega/ load the linearity is better than -80 dB and better than -88 dB for a 1 k/spl Omega/ load at 1 kHz.
NeuroImage: Clinical

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classi ﬁ ers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T 1 -weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n=3602) and an Asian cohort (n=347). For the ADNI-2 cohort, the graph-CNN showed classi ﬁ cation accuracy of controls (CN) vs. AD at 85.8% and early MCI (EMCI) vs. AD at 79.2%, followed by CN vs. late MCI (LMCI) (69.3%), LMCI vs. AD (65.2%), EMCI vs. LMCI (60.9%), and CN vs. EMCI (51.8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T 1 -weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The ﬁ nd-tuned graph-CNN further provided a promising CN vs. AD classi ﬁ cation accuracy of 89.4% on the ADNI-1 cohort and>90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classi ﬁ ers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classi ﬁ cation performance.
Practical use of Partially Homomorphic Cryptography

Researchers and practitioners for decades assumed that encrypted data cannot be processed. However, recently with the emergence of cloud computing and Gentry’s work on homomorphic encryption a great interest appeared on the use of partial homomorphic functions to process encrypted data. The paper presents MorphicLib, a new partial homomorphic cryptography library written in Java that can be used to implement a wide-range of applications. The paper shows the usefulness of the library with two services. HomomorphicSpace is coordination service, that is a tuple space that stores encrypted tuples but still supports operations like returning tuples with values withing a certain range. HomoFuse is a remote file system for a Linux machine, that keeps cloud data and metadata encrypted with cryptographic schemes with homomorphic properties. Thanks to these properties it is possible to perform a set of file search operations, at server side, without any prior decoding. The paper presents an experimental evaluation of the library, the coordination service, and the remote filesystem service. We observed a negligible overhead when homomorphic operations were used.
Influence of Aerodynamics on Quadrotor Dynamics

The aim of this work is to study the aerodynamics effect of the propellers on the dynamic behaviour of a quadrotor helicopter system. Here an inverse dynamics analysis and a standard PID linear controller for feedback control are considered for a simplified model of UAV represented by a linear under-actuated system. Aerodynamic forces are modeled with the blade element theory to consider the variation of the thrust coefficients with the different flight conditions, in order to calculate the aerodynamic effect of drag and thrust on the rotation of the propellers. This aerodynamic analysis is very useful to predict the dynamic behaviour of the system in presence of wind fields and gives us indications about the type of propellers and motor characteristics to be installed on the UAV. The proposed dynamical model and its control strategies can be source for future works. Key–Words: UAV, Quadcopter, PID Control, Under-Actuated System.
Mutual entrainment based human-robot communication field-paradigm shift from "human interface" to "communication field"

A mutual entrainment based communication field is proposed as a new communication paradigm to realize a human-robot collaboration system. The most interesting ability of this field is to spontaneously generate a control information field for autonomous coordination including both human and robotic dynamics. Applying it to the coordinative walking process between human and robotic system as an example, the authors show that the control information field indicating functional relationship between human and robot is self-organized in the communication field. It is encoded on the spatial phase pattern generated by the mutual entrainment between walking rhythms. Interpreting this information, each subsystem would be able to coordinately behave as one whole system. This result suggests that the essence of communication is not in any definite and separated order as is seen in conventional human interfaces but in the ability to self-organize flexible and integrated order in the communication field. <<ETX>>
Curative effect of Danhong injection on the clinical symptoms , adverse reactions and electrocardiogram of angina pectoris of coronary heart disease

Objective: To investigate the improvement effect of Danhong injection on the clinical symptoms, cardiac function, adverse reactions and electrocardiogram of patients with angina pectoris of coronary heart disease. Methods: One hundred and sixty patients with angina pectoris of coronary heart disease treated in our hospital from January 2013 to December 2016 were selected as the subjects. The patients were divided into treatment group and control group randomly, 80 cases in each group. The control group received routine comprehensive anti-anginal therapy. Danhong injection was added to the treatment group and other medications were the same as the control group. The treatment effect of angina pectoris, improvement of cardiac function and the occurrence of adverse reactions were analyzed and compared between the two groups. Results: Compared with the control group, the duration and seizure frequency of angina pectoris were significantly reduced in the treatment group (P=0.000). Besides, the total effective rate in the treatment group was 93.75%, significantly higher than that in the control group (77.5%) with statistically significant difference (P=0.012). In addition, the total effective rate of electrocardiogram improvement in the treatment group (85%) was also significantly higher than that in the control group (65%) (P=0.016). And the improvement of cardiac function in the treatment group was obviously better than that in the control group with statistically significant difference (P=0.000). However, there was no significant difference in the incidence of adverse reactions between the two groups. Conclusion: The clinical effect of Danhong injection was obvious in the treatment of angina pectoris of coronary heart disease, with high safety and without any adverse reactions. And the improvement of ischemic electrocardiogram changes and cardiac function were remarkable. Therefore, Danhong injection was an effective drug for the treatment of angina pectoris of coronary heart disease.