OpenRaiser/Intern-Atlas huggingface dataset:
Counter-Propagating Core Assignment in Multi-Core Fiber Optical Networks to Reduce Inter-Core Crosstalk and Capacity Wastage
Venue: arXiv cs.NI 2018
Year: 2018

Inter-core crosstalk is one of the most serious impairments for signal transmission in a multi-core fiber (MCF) optical network. On the other hand, because of wide deployment of data centers (DCs), we are seeing an increasing bidirectional traffic demand asymmetry, which leads to significant capacity wastage in designing and operating an optical transport network. To alleviate these effects, for an MCF optical network, we propose to assign fiber cores in an MCF in an asymmetric and counter-propagating manner. This can not only significantly reduce inter-core crosstalk between counter-propagating fiber cores but also flexibly assign different numbers of fiber cores in the opposite directions of a fiber link, thereby overcoming network capacity wastage due to the bidirectional traffic demand asymmetry. To evaluate the benefits of the proposed strategy, we consider the routing, spectrum, and core assignment (RSCA) problem for the MCF optical network. An integer linear programming (ILP) model and an auxiliary graph (AG) based heuristic algorithm are developed to optimize network spectrum resource utilization. Simulation studies show the effectiveness of the proposed core counter-propagation strategy, which can significantly outperform its counterpart, i.e., the co-propagation scheme, in terms of the total number of MCFs used and average inter-core crosstalk. In addition, the proposed RSCA heuristic algorithm is efficient to perform close to the ILP model, which can minimize the number of MCFs used and crosstalk between neighboring cores.
Knowledge Distillation with Feature Maps for Image Classification
Venue: arXiv cs.LG 2018
Year: 2018

The model reduction problem that eases the computation costs and latency of complex deep learning architectures has received an increasing number of investigations owing to its importance in model deployment. One promising method is knowledge distillation (KD), which creates a fast-to-execute student model to mimic a large teacher network. In this paper, we propose a method, called KDFM (Knowledge Distillation with Feature Maps), which improves the effectiveness of KD by learning the feature maps from the teacher network. Two major techniques used in KDFM are shared classifier and generative adversarial network. Experimental results show that KDFM can use a four layers CNN to mimic DenseNet-40 and use MobileNet to mimic DenseNet-100. Both student networks have less than 1\% accuracy loss comparing to their teacher models for CIFAR-100 datasets. The student networks are 2-6 times faster than their teacher models for inference, and the model size of MobileNet is less than half of DenseNet-100's.
Building Sequential Inference Models for End-to-End Response Selection
Venue: arXiv cs.CL 2018
Year: 2018

This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7). This task focuses on selecting the correct next utterance from a set of candidates given a partial conversation. We propose an end-to-end neural network based on enhanced sequential inference model (ESIM) for this task. Our proposed model differs from the original ESIM model in the following four aspects. First, a new word representation method which combines the general pre-trained word embeddings with those estimated on the task-specific training set is adopted in order to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE) is designed which is capable to encode sentences hierarchically and generate more descriptive representations by aggregation. Third, a new pooling method which combines multi-dimensional pooling and last-state pooling is used instead of the simple combination of max pooling and average pooling in the original ESIM. Last, a modification layer is added before the softmax layer to emphasize the importance of the last utterance in the context for response selection. In the released evaluation results of DSTC7, our proposed method ranked second on the Ubuntu dataset and third on the Advising dataset in subtask 1 of Track 1.
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
Venue: arXiv cs.LG 2018
Year: 2018

Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict. Our study utilized regression models and attention-based recurrent neural network (RNN) algorithms and a multi-clinical information system large-scale database to build models that can predict the successful response to FBT among hypotensive patients in ICUs. We investigated both time-aggregated modeling using logistic regression algorithms with regularization and time-series modeling using the long short term memory network (LSTM) and the gated recurrent units network (GRU) with the attention mechanism for clinical interpretability. Among all modeling strategies, the stacked LSTM with the attention mechanism yielded the most predictable model with the highest accuracy of 0.852 and area under the curve (AUC) value of 0.925. The study results may help identify hypotensive patients in ICUs who will have sufficient blood pressure recovery after FBT.
EnsNet: Ensconce Text in the Wild
Venue: arXiv cs.CV 2018
Year: 2018

A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SMBNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.
Nose, eyes and ears: Head pose estimation by locating facial keypoints
Venue: arXiv cs.CV 2018
Year: 2018

Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression function directly on the input images fail to do so. To this end, we propose to use a higher level representation to regress the head pose while using deep learning architectures. More specifically, we use the uncertainty maps in the form of 2D soft localization heatmap images over five facial keypoints, namely left ear, right ear, left eye, right eye and nose, and pass them through an convolutional neural network to regress the head-pose. We show head pose estimation results on two challenging benchmarks BIWI and AFLW and our approach surpasses the state of the art on both the datasets.
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
Venue: arXiv cs.CV 2018
Year: 2018

The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare. However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions. Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences and beyond.
Evaluation of flashing stimuli shape and colour heterogeneity using a P300 brain-computer interface speller
Venue: arXiv cs.HC 2018
Year: 2018

Objective: Previous works using a visual P300-based speller have reported an improvement modifying the shape or colour of the presented stimulus. However, the effects of both blended factors have not been yet studied. Thus, the aim of the present work was to study both factors and assess the interaction between them. Method: Fifteen naïve participants tested four different spellers in a calibration and online task. All spellers were similar except the employed illumination of the target stimulus: white letters, white blocks, coloured letters, and coloured blocks. Results: The block-shaped conditions offered an improvement versus the letter-shaped conditions in the calibration (accuracy) and online (accuracy and correct commands per minute) tasks. Analysis of the P300 waveform showed a larger difference between target and no target stimulus waveforms for the block-shaped conditions versus the letter-shaped. The hypothesis regarding the colour heterogeneity of the stimuli was not found at any level of the analysis. Conclusion: The use of block-shaped illumination demonstrated a better performance than the standard letter-shaped flashing stimuli in classification performance, correct commands per minute, and P300 waveform.
Integral Geometric Dual Distributions of Multilinear Models
Venue: arXiv cs.CV 2018
Year: 2018

We propose an integral geometric approach for computing dual distributions for the parameter distributions of multilinear models. The dual distributions can be computed from, for example, the parameter distributions of conics, multiple view tensors, homographies, or as simple entities as points, lines, and planes. The dual distributions have analytical forms that follow from the asymptotic normality property of the maximum likelihood estimator and an application of integral transforms, fundamentally the generalised Radon transforms, on the probability density of the parameters. The approach allows us, for instance, to look at the uncertainty distributions in feature distributions, which are essentially tied to the distribution of training data, and helps us to derive conditional distributions for interesting variables and characterise confidence intervals of the estimates.
AI Matrix - Synthetic Benchmarks for DNN
Venue: arXiv cs.CV 2018
Year: 2018

Deep neural network (DNN) architectures, such as convolutional neural networks (CNN), involve heavy computation and require hardware, such as CPU, GPU, and AI accelerators, to provide the massive computing power. With the many varieties of AI hardware prevailing on the market, it is often hard to decide which one is the best to use. Thus, benchmarking AI hardware effectively becomes important and is of great help to select and optimize AI hardware. Unfortunately, there are few AI benchmarks available in both academia and industry. Examples are BenchNN[1], DeepBench[2], and Dawn Bench[3], which are usually a collection of typical real DNN applications. While these benchmarks provide performance comparison across different AI hardware, they suffer from a number of drawbacks. First, they cannot adapt to the emerging changes of DNN algorithms and are fixed once selected. Second, they contain tens to hundreds of applications and take very long time to finish running. Third, they are mainly selected from open sources, which are restricted by copyright and are not representable to proprietary applications. In this work, a synthetic benchmarks framework is firstly proposed to address the above drawbacks of AI benchmarks. Instead of pre-selecting a set of open-sourced benchmarks and running all of them, the synthetic approach generates only a one or few benchmarks that best represent a broad range of applications using profiled workload characteristics data of these applications. Thus, it can adapt to emerging changes of new DNN algorithms by re-profiling new applications and updating itself, greatly reduce benchmark count and running time, and strongly represent DNN applications of interests. The generated benchmarks are called AI Matrix, serving as a performance benchmarks matching the statistical workload characteristics of a combination of applications of interests.
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Venue: arXiv cs.CV 2018
Year: 2018

Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level Adaptation Transform (SPLAT) approach to detector adaptation that efficiently generates cross-domain image pairs. Our model uses aligned-pair and/or pseudo-label losses to adapt an object detector to the target domain, and can learn transformations with or without densely labeled data in the source (e.g. semantic segmentation annotations). Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment. Otherwise, when dense labels are available we introduce a more efficient cycle-free method, which exploits pixel-level semantic labels to condition the training of the transformation network. The end task is then trained using detection box labels from the source, potentially including labels inferred on unlabeled source data. We show both that pixel-level transforms outperform prior approaches to detector domain adaptation, and that our cycle-free method outperforms prior models for unconstrained cycle-based learning of generic transformations while running 3.8 times faster. Our combined model improves on prior detection baselines by 12.5 mAP adapting from Sim 10K to Cityscapes, recovering over 50% of the missing performance between the unadapted baseline and the labeled-target upper bound.
TxProbe: Discovering Bitcoin's Network Topology Using Orphan Transactions
Venue: arXiv cs.CR 2018
Year: 2018

Bitcoin relies on a peer-to-peer overlay network to broadcast transactions and blocks. From the viewpoint of network measurement, we would like to observe this topology so we can characterize its performance, fairness and robustness. However, this is difficult because Bitcoin is deliberately designed to hide its topology from onlookers. Knowledge of the topology is not in itself a vulnerability, although it could conceivably help an attacker performing targeted eclipse attacks or to deanonymize transaction senders. In this paper we present TxProbe, a novel technique for reconstructing the Bitcoin network topology. TxProbe makes use of peculiarities in how Bitcoin processes out of order, or "orphaned" transactions. We conducted experiments on Bitcoin testnet that suggest our technique reconstructs topology with precision and recall surpassing 90%. We also used TxProbe to take a snapshot of the Bitcoin testnet in just a few hours. TxProbe may be useful for future measurement campaigns of Bitcoin or other cryptocurrency networks.
Ann: A domain-specific language for the effective design and validation of Java annotations
Venue: arXiv cs.PL 2018
Year: 2018

This paper describes a new modelling language for the effective design and validation of Java annotations. Since their inclusion in the 5th edition of Java, annotations have grown from a useful tool for the addition of meta-data to play a central role in many popular software projects. Usually they are not conceived in isolation, but in groups, with dependency and integrity constraints between them. However, the native support provided by Java for expressing this design is very limited. To overcome its deficiencies and make explicit the rich conceptual model which lies behind a set of annotations, we propose a domain-specific modelling language. The proposal has been implemented as an Eclipse plug-in, including an editor and an integrated code generator that synthesises annotation processors. The environment also integrates a model finder, able to detect unsatisfiable constraints between different annotations, and to provide examples of correct annotation usages for validation. The language has been tested using a real set of annotations from the Java Persistence API (JPA). Within this subset we have found enough rich semantics expressible with Ann and omitted nowadays by the Java language, which shows the benefits of Ann in a relevant field of application.
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research
Venue: arXiv cs.DL 2018
Year: 2018

The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to use for a particular application, and to be confident that one has compared against all relevant related work when developing new ideas. However, such a rapidly growing body of research literature is a problem that other fields have already faced - in particular, medicine and epidemiology. In those fields, systematic reviews and meta-analyses have been used exactly for dealing with these issues and it is not uncommon for entire journals to be dedicated to such analyses. Here, we suggest the field of machine learning might similarly benefit from meta-analysis and systematic review, and we encourage further discussion and development along this direction.
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Deep learning models have been used extensively to solve real-world problems in recent years. The performance of such models relies heavily on large amounts of labeled data for training. While the advances of data collection technology have enabled the acquisition of a massive volume of data, labeling the data remains an expensive and time-consuming task. Active learning techniques are being progressively adopted to accelerate the development of machine learning solutions by allowing the model to query the data they learn from. In this paper, we introduce a real-world problem, the recognition of parking signs, and present a framework that combines active learning techniques with a transfer learning approach and crowd-sourcing tools to create and train a machine learning solution to the problem. We discuss how such a framework contributes to building an accurate model in a cost-effective and fast way to solve the parking sign recognition problem in spite of the unevenness of the data associated with the fact that street-level images (such as parking signs) vary in shape, color, orientation and scale, and often appear on top of different types of background.
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network
Venue: arXiv cs.CV 2018
Year: 2018

We explore a solution for learning disease signatures from weakly, yet easily obtainable, annotated volumetric medical imaging data by analyzing 3D volumes as a sequence of 2D images. We demonstrate the performance of our solution in the detection of emphysema in lung cancer screening low-dose CT images. Our approach utilizes convolutional long short-term memory (LSTM) to "scan" sequentially through an imaging volume for the presence of disease in a portion of scanned region. This framework allowed effective learning given only volumetric images and binary disease labels, thus enabling training from a large dataset of 6,631 un-annotated image volumes from 4,486 patients. When evaluated in a testing set of 2,163 volumes from 2,163 patients, our model distinguished emphysema with area under the receiver operating characteristic curve (AUC) of .83. This approach was found to outperform 2D convolutional neural networks (CNN) implemented with various multiple-instance learning schemes (AUC=0.69-0.76) and a 3D CNN (AUC=.77).
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
Venue: arXiv cs.LG 2018
Year: 2018

Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology. Established methods in the field, such as homology modeling and fragment assembly, appeared to have reached their limit. However, this year saw the emergence of promising new approaches: end-to-end protein structure and dynamics models, as well as reinforcement learning applied to protein folding. For these approaches to be investigated on a larger scale, an efficient implementation of their key computational primitives is required. In this paper we present a library of differentiable mappings from two standard dihedral-angle representations of protein structure (full-atom representation "$ϕ,ψ,ω,χ$" and backbone-only representation "$ϕ,ψ,ω$") to atomic Cartesian coordinates. The source code and documentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.