Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Bandwidth Variation-Adapted State Transfer for Geo-Replicated State Machines and its Application to Dynamic Replica Replacement

This paper proposes a new state transfer method for geographic state machine replication (SMR) that dynamically allocates the state to be transferred among replicas according to changes in communication bandwidths. SMR improves fault tolerance by replicating a service to multiple replicas. When a replica is newly added or recovered from a failure, the other replicas transfer the current state of the service to it. However, in geographic SMR, the communication bandwidths of replicas are different and constantly changing. Therefore, existing state transfer methods cannot fully utilize the available bandwidth, and their state transfer time increases. To overcome this problem, our method divides the state into multiple chunks and assigns them to replicas based on each replica's bandwidth so that the broader a replica's bandwidth is, the more chunks it transfers. The proposed method also updates the chunk assignment of each replica dynamically based on the currently estimated bandwidth. The performance evaluation on Amazon EC2 shows that the proposed method reduces the state transfer time by up to 47% compared to the existing one. In addition, we apply the proposed method to dynamic replacement of replicas, which can mitigate latency degradation caused by network trouble, and evaluate how fast the method can relocate a replica.
Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency

In the last decade, information diﬀusion (also known as information cascade) on social networks has been massively investigated due to its application values in many ﬁelds. In recent years, many sequential models including those models based on recurrent neural networks have been broadly employed to predict information cascade. However, the user dependencies in a cascade sequence captured by sequential models are generally unidirectional and inconsistent with diﬀusion trees. For example, the true trigger of a successor may be a non-immediate predecessor rather than the immediate predecessor in the sequence. To capture user dependencies more suﬃciently which are crucial to precise cascade modeling, we propose a non-sequential information cascade model named as TAN-DRUD ( T opology-aware A ttention N etworks with D ual R ole U ser D ependency). TAN-DRUD obtains satisfactory performance on information cascade modeling through capturing the dual role user dependencies of information sender and receiver, which is inspired by the classic communication theory. Furthermore, TAN-DRUD incorporates social topology into two-level attention networks for enhanced information diffusion prediction. Our extensive experiments on three cascade datasets demonstrate that our model is not only superior to the state-of-the-art cascade models, but also capable of exploiting topology information and inferring diﬀusion trees.
Feature Structure Distillation for BERT Transferring

—Knowledge distillation is an approach to transfer information on representations from a teacher to a student by reducing their difference. A challenge of this approach is to reduce the ﬂexibility of the student’s representations inducing inaccurate learning of the teacher’s knowledge. To resolve it in BERT transferring, we investigate distillation of structures of representations speciﬁed to three types: intra-feature, local inter-feature, global inter-feature structures. To transfer them, we introduce feature structure distillation methods based on the Centered Kernel Alignment, which assigns a consistent value to similar features structures and reveals more informative relations. In particular, a memory-augmented transfer method with clustering is implemented for the global structures. In the experiments on the nine tasks for language understanding of the GLUE dataset, the proposed methods effectively transfer the three types of structures and improve performance compared to state-of-the-art distillation methods. Indeed, the code for the methods is available in https://github.com/maroo-sky/FSD.
A qualitative investigation of optical flow algorithms for video denoising

A good optical flow estimation is crucial in many video analysis and restoration algorithms employed in application fields like media industry, industrial inspection and automotive. In this work, we investigate how well optical flow algorithms perform qualitatively when integrated into a state of the art video denoising algorithm. Both classic optical flow algorithms (e.g. TV-L1) as well as recent deep learning based algorithm (like RAFT or BMBC) will be taken into account. For the qualitative investigation, we will employ realistic content with challenging characteristic (noisy content, large motion etc.) instead of the standard images used in most publications.
SmartSales: Sales Script Extraction and Analysis from Sales Chatlog

In modern sales applications, automatic script extraction and management greatly decrease the need for human labor to collect the winning sales scripts, which largely boost the success rate for sales and can be shared across the sales teams. In this work, we present the SmartSales system to serve both the sales representatives and managers to attain the sales insights from the large-scale sales chatlog. SmartSales consists of three modules: 1) Customer frequently asked questions (FAQ) extraction aims to enrich the FAQ knowledge base by harvesting high quality customer question-answer pairs from the chatlog. 2) Customer objection response assists the salespeople to figure out the typical customer objections and corresponding winning sales scripts, as well as search for proper sales responses for a certain customer objection. 3) Sales manager dashboard helps sales managers to monitor whether a specific sales representative or team follows the sales standard operating procedures (SOP). The proposed prototype system is empowered by the state-of-the-art conversational intelligence techniques and has been running on the Tencent Cloud to serve the sales teams from several different areas.
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly"AMRized"to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on different benchmarks especially in topology-related scores.
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis

In the realm of unsupervised learning, Bayesian nonparametric mixture models, exempliﬁed by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting the complexity of the model to the data. Such models are particularly useful in clustering tasks where the number of clusters is unknown. Despite their potential and mathematical elegance, however, DPMMs have yet to become a mainstream tool widely adopted by practitioners. This is arguably due to a misconception that these models scale poorly as well as the lack of high-performance (and user-friendly) software tools that can handle large datasets efﬁciently. In this paper we bridge this practical gap by proposing a new, easy-to-use, statistical software package for scalable DPMM inference. More concretely, we provide efﬁcient and easily-modiﬁable implementations for high-performance distributed sampling-based inference in DPMMs where the user is free to choose between either a multiple-machine, multiple-core, CPU implementation (written in Julia ) and a multiple-stream GPU implementation (written in CUDA/C++ ). Both the CPU and GPU implementations come with a common (and optional) python wrapper, providing the user with a single point of entry with the same interface. On the algorithmic side, our implementations leverage a leading DPMM sampler from [1]. While Chang and Fisher III’s implementation (written in MATLAB/C++ ) used only CPU and was designed for a single multi-core machine, the packages we proposed here distribute the computations efﬁciently across either multiple multi-core machines or across mutiple GPU streams. This leads to speedups, alleviates memory and storage limitations, and lets us ﬁt DPMMs to signiﬁcantly larger datasets and of higher dimensionality than was possible previously by either [1] or other DPMM methods. Our open-source code (GPLv2 licensed) is publicly available on github.com .
Magnon heat transport in a two-dimensional Mott insulator

Whether or not anomalies in the thermal conductivity in insulating cuprates can be attributed to antiferromagnetic order and magnons in a 2D Mott insulator remains an intriguing open question. To shed light on this issue, we investigate the thermal conductivity κ and its relationship with the speciﬁc heat c v in the half-ﬁlled 2D single-band Hubbard model, using the numerically exact determinant quantum Monte Carlo algorithm and maximum entropy analytic continuation. At low temperatures where the charge degrees of freedom are gapped-out and c v exhibits a clear magnon peak, we observe that thermal conductivity κ also tends to form a peak at similar temperatures. Reducing temperature further produces a sharp upturn in κ , associated with an increasing mean-free path. We identify this as the high-temperature side of the anomalous peak in insulating cuprates, where the mean-free path eventually is cut-oﬀ by other scattering eﬀects, including phonons, disor-der, and physical size. Diﬀerent scattering eﬀects in our model are identiﬁed and analyzed in the thermal diﬀusivity. and L x is the linear size of the lattice along the x direction. The correlation function is position independent due to translation symmetry. The values at r x = 0 and r x = 1 are not included in the ﬁtting, in order to rule out short-range eﬀects. The term ∝ e −| L x − ∆ r x | /ξ is added to the ﬁtting function, in consideration of periodic boundary conditions [23]. Imaginary time discretization dτ = 1 / (20 t ).
Medical image segmentation using 3D MRI data

Precise segmentation of three-dimensional (3D) magnetic resonance imaging (MRI) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Accurate automatic extraction a 3D component from images obtained by magnetic resonance imaging (MRI) is a challenging segmentation problem due to the small size objects of interest (e.g., blood vessels, bones) in each 2D MRA slice and complex surrounding anatomical structures. Our objective is to develop a specific segmentation scheme for accurately extracting parts of bones from MRI images. In this paper, we use a segmentation algorithm to extract the parts of bones from Magnetic Resonance Imaging (MRI) data sets based on modified active contour method. As a result, the proposed method demonstrates good accuracy in a comparison between the existing segmentation approaches on real MRI data.
Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Abstract Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.
New device for taking nine-directional ocular photographs: “9Gaze” application

This study compared the time required to produce nine-directional ocular photographs using the conventional method to that using the newly devised 9Gaze application. In total, 20 healthy adults, 10 adult patients with strabismus, and 10 pediatric patients with amblyopia or strabismus had their ocular photographs taken using a digital camera with PowerPoint 2010, and with an iPad, and iPod touch with 9Gaze. Photographs of 10 healthy patients were taken by orthoptists with <1 year of experience, and the other participants had theirs taken by those with >1 year of experience. The required time was compared between the three devices in all patients and the two orthoptist groups in 20 healthy adults (>1 year and <1 year of experience). The required times were significantly different between the devices: 515.5 ± 187.0 sec with the digital camera, 117.4 ± 17.8 sec with the iPad, and 76.3 ± 14.1 sec with the iPod touch. The required time with the digital camera was significantly different between the two orthoptist groups (404.7 ± 150.8 vs. 626.3 ± 154.2 sec, P=0.007). The use of the 9Gaze application shortened the recording time required. Furthermore, 9Gaze can be used without considering the years of experience of the examiner.
Species identification skills predict in-depth knowledge about species

To raise biodiversity awareness effectively, communicators should be aware of knowledge levels in their audiences. Species identification skills have been used in the past as a measure of what people know about species, yet it is not known whether they serve as good indicators. To study the link between species identification and in-depth species knowledge, we presented an animal knowledge test to an online audience of over 7,000 Dutch adults, and used correlation and regression analyses to determine the extent to which species identification predicts in-depth knowledge about species’ origin, habitat, diet, and behavior. We found that in-depth knowledge was higher in those who correctly identified species as compared with those who did not correctly identify species, for all four types of in-depth knowledge. Moreover, as compared to alternative variables (work, age, gender, and educational level), species identification was by far the best predictor for in-depth knowledge about species. However, species identification levels were generally higher than levels of in-depth knowledge, and knowledge gaps and misconceptions were uncovered. The results confirm the value of species identification tests, but also highlight limitations and challenges that should be taken into account when establishing knowledge levels and communicating biodiversity.
SensiMix: Sensitivity-Aware 8-bit index & 1-bit value mixed precision quantization for BERT compression

Given a pre-trained BERT, how can we compress it to a fast and lightweight one while maintaining its accuracy? Pre-training language model, such as BERT, is effective for improving the performance of natural language processing (NLP) tasks. However, heavy models like BERT have problems of large memory cost and long inference time. In this paper, we propose SensiMix (Sensitivity-Aware Mixed Precision Quantization), a novel quantization-based BERT compression method that considers the sensitivity of different modules of BERT. SensiMix effectively applies 8-bit index quantization and 1-bit value quantization to the sensitive and insensitive parts of BERT, maximizing the compression rate while minimizing the accuracy drop. We also propose three novel 1-bit training methods to minimize the accuracy drop: Absolute Binary Weight Regularization, Prioritized Training, and Inverse Layer-wise Fine-tuning. Moreover, for fast inference, we apply FP16 general matrix multiplication (GEMM) and XNOR-Count GEMM for 8-bit and 1-bit quantization parts of the model, respectively. Experiments on four GLUE downstream tasks show that SensiMix compresses the original BERT model to an equally effective but lightweight one, reducing the model size by a factor of 8× and shrinking the inference time by around 80% without noticeable accuracy drop.
Elevated baseline circulating platelet-to-lymphocyte ratio and survival in initial stage Ⅳ gastric cancer patients: A meta-analysis

Background Systemic inflammatory response (SIR) plays important roles in initiation, promotion and progression of tumor. However, the prognostic role of baseline circulating platelet–to–lymphocyte ratio (PLR) (known as a marker of SIR) in human initial stage Ⅳ gastric cancer (GC) remains controversial. Hence, we performed this meta-analysis to assess the value of it in prognosis prediction for these patients. Materials and methods We searched PubMed, Embase and EBSCO to identify the studies and computed extracted data with STATA 12.0. Results A total of 3025 patients with initial stage Ⅳ GC from 13 published studies were incorporated into this meta-analysis. We found that elevated baseline circulating PLR was significantly associated with decreased overall survival (OS), but not with progression–free survival (PFS) in stage Ⅳ GC patients. However, in stratified analyses, high PLR was only associated with worse 1-year and 2-year OS, but not with 3-year or 4-year OS; In addition, it was considerably related with reduced 6-month PFS, but not with 1-year or 2-year PFS. Moreover, high PLR markedly correlated with peritoneal metastasis of GC. Conclusion Elevated baseline circulating PLR decreased 1-year OS and 6-month PFS in initial stage Ⅳ GC patients, implicating that it is a valuable prognostic index for these patients and modifying the inflammatory responses may have a potential for effective treatment.
Head-mounted sensory augmentation system for navigation in low visibility environments

Sensory augmentation can be used to assist in some tasks where sensory information is limited or sparse. This thesis focuses on the design and investigation of a head-mounted vibrotactile sensory augmentation interface to assist navigation in low visibility environments such as firefighters’ navigation or travel aids for visually impaired people. A novel head-mounted vibrotactile interface comprising a 1-by-7 vibrotactile display worn on the forehead is developed. A series of psychophysical studies is carried out with this display to (1) determine the vibrotactile absolute threshold, (2) investigate the accuracy of vibrotactile localization, and (3) evaluate the funneling illusion and apparent motion as sensory phenomena that could be used to communicate navigation signals. The results of these studies provide guidelines for the design of head-mounted interfaces. A 2nd generation head-mounted sensory augmentation interface called the Mark-II Tactile Helmet is developed for the application of firefighters’ navigation. It consists of a ring of ultrasound sensors mounted to the outside of a helmet, a microcontroller, two batteries and a refined vibrotactile display composed of seven vibration motors based on the results of the aforementioned psychophysical studies. A ‘tactile language’, that is, a set of distinguishable vibrotactile patterns, is developed for communicating navigation commands to the Mark-II Tactile Helmet. Four possible combinations of two command presentation modes (continuous, discrete) and two command types (recurring, single) are evaluated for their effectiveness in guiding users along a virtual wall in a structured environment. Continuous and discrete presentation modes use spatiotemporal patterns that induce the experience of apparent movement and discrete movement on the forehead, respectively. The recurring command type presents the tactile command repeatedly with an interval between patterns of 500 ms while the single command type presents the tactile command just once when there is a change in the command. The effectiveness of this tactile language is evaluated according to the objective measures of the users’ walking speed and the smoothness of their trajectory parallel to the virtual wall and subjective measures of utility and comfort employing Likert-type rating scales. The Recurring Continuous (RC) commands that exploit the phenomena of apparent motion are most effective in generating efficient routes and fast travel, and are most preferred. Finally, the optimal tactile language (RC) is compared with audio guidance using verbal instructions to investigate effectiveness in delivering navigation commands. The results show that haptic guidance leads to better performance as well as lower cognitive workload compared to auditory feedback. This research demonstrates that a head-mounted sensory augmentation interface can enhance spatial awareness in low visibility environments and could help firefighters’ navigation by providing them with supplementary sensory information.