OpenRaiser/Intern-Atlas huggingface dataset:
Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning for Cross-City Property Appraisal Framework
Venue: arXiv cs.LG 2018
Year: 2018

Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.
Oriented Colourings of Graphs with Maximum Degree Three and Four
Venue: arXiv cs.DM 2018
Year: 2018

We show that any orientation of a graph with maximum degree three has an oriented 9-colouring, and that any orientation of a graph with maximum degree four has an oriented 69-colouring. These results improve the best known upper bounds of 11 and 80, respectively.
Pied Piper: Rethinking Internet Data Delivery
Venue: arXiv cs.NI 2018
Year: 2018

We contend that, analogously to the transition from resource-limited on-prem computing to resource-abundant cloud computing, Internet data delivery should also be adapted to a reality in which the cloud offers a virtually unlimited resource, i.e., network capacity, and virtualization enables delegating local tasks, such as routing and congestion control, to the cloud. This necessitates rethinking the traditional roles of inter- and intra-domain routing and conventional end-to-end congestion control. We introduce Optimized Cloudified Delivery (OCD), a holistic approach for optimizing joint Internet/cloud data delivery, and evaluate OCD through hundreds of thousands of file downloads from multiple locations. We start by examining an OCD baseline approach: traffic from a source A to a destination B successively passes through two cloud virtual machines operating as relays - nearest to A and B; and the two cloud relays employ TCP split. We show that even this naive strategy can outperform recently proposed improved end-to-end congestion control paradigms (BBR and PCC) by an order of magnitude. Next, we present a protocol-free, ideal pipe model of data transmission, and identify where today's Internet data delivery mechanisms diverge from this model. We then design and implement OCD Pied Piper. Pied Piper leverages various techniques, including novel kernel-based transport-layer accelerations, to improve the Internet-Cloud interface so as to approximately match the ideal network pipe model.
A Probe Towards Understanding GAN and VAE Models
Venue: arXiv cs.LG 2018
Year: 2018

This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an introduction to generative models and provide some personal interpretations supported by empirical evidence. Both generative adversarial network models and variational autoencoders have been widely used to approximate probability distributions of data sets. Although they both use parametrized distributions to approximate the underlying data distribution, whose exact inference is intractable, their behaviors are very different. We summarize our experiment results that compare these two categories of models in terms of fidelity and mode collapse. We provide a hypothesis to explain their different behaviors and propose a new model based on this hypothesis. We further tested our proposed model on MNIST dataset and CelebA dataset.
Training Set Camouflage
Venue: arXiv cs.CR 2018
Year: 2018

We introduce a form of steganography in the domain of machine learning which we call training set camouflage. Imagine Alice has a training set on an illicit machine learning classification task. Alice wants Bob (a machine learning system) to learn the task. However, sending either the training set or the trained model to Bob can raise suspicion if the communication is monitored. Training set camouflage allows Alice to compute a second training set on a completely different -- and seemingly benign -- classification task. By construction, sending the second training set will not raise suspicion. When Bob applies his standard (public) learning algorithm to the second training set, he approximately recovers the classifier on the original task. Training set camouflage is a novel form of steganography in machine learning. We formulate training set camouflage as a combinatorial bilevel optimization problem and propose solvers based on nonlinear programming and local search. Experiments on real classification tasks demonstrate the feasibility of such camouflage.
A Commute in Data: The comma2k19 Dataset
Venue: arXiv cs.RO 2018
Year: 2018

comma.ai presents comma2k19, a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma grey panda. Laika, an open-source GNSS processing library, is also introduced here. Laika produces 40% more accurate positions than the GNSS module used to collect the raw data. This dataset includes pose (position + orientation) estimates in a global reference frame of the recording camera. These poses were computed with a tightly coupled INS/GNSS/Vision optimizer that relies on data processed by Laika. comma2k19 is ideal for development and validation of tightly coupled GNSS algorithms and mapping algorithms that work with commodity sensors.
The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective
Venue: arXiv cs.AI 2018
Year: 2018

The recently released AlphaZero algorithm achieves superhuman performance in the games of chess, shogi and Go, which raises two open questions. Firstly, as there is a finite number of possibilities in the game, is there a quantifiable intelligence measurement for evaluating intelligent systems, e.g. AlphaZero? Secondly, AlphaZero introduces sophisticated reinforcement learning and self-play to efficiently encode the possible states, is there a simple information-theoretic model to represent the learning process and offer more insights in fostering strong AI systems? This paper explores the above two questions by proposing a simple variance of Shannon's communication model, the concept of intelligence entropy and the Unified Intelligence-Communication Model is proposed, which provide an information-theoretic metric for investigating the intelligence level and also provide an bound for intelligent agents in the form of Shannon's capacity, namely, the intelligence capacity. This paper then applies the concept and model to AlphaZero as a case study and explains the learning process of intelligent agent as turbo-like iterative decoding, so that the learning performance of AlphaZero may be quantitatively evaluated. Finally, conclusions are provided along with theoretical and practical remarks.
Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation. Traditional automated solutions often fail in relieving experts from the labor-intensive manual labeling. In this paper, we propose a deep neural network based solution for automated left atrium segmentation in gadolinium-enhanced MR volumes with promising performance. We firstly argue that, for this volumetric segmentation task, networks in 2D fashion can present great superiorities in time efficiency and segmentation accuracy than networks with 3D fashion. Considering the highly varying shape of atrium and the branchy structure of associated pulmonary veins, we propose to adopt a pyramid module to collect semantic cues in feature maps from multiple scales for fine-grained segmentation. Also, to promote our network in classifying the hard examples, we propose an Online Hard Negative Example Mining strategy to identify voxels in slices with low classification certainties and penalize the wrong predictions on them. Finally, we devise a competitive training scheme to further boost the generalization ability of networks. Extensively verified on 20 testing volumes, our proposed framework achieves an average Dice of 92.83% in segmenting the left atria and pulmonary veins.
Partial Function Extension with Applications to Learning and Property Testing
Venue: arXiv cs.DS 2018
Year: 2018

In partial function extension, we are given a partial function consisting of $n$ points from a domain and a function value at each point. Our objective is to determine if this partial function can be extended to a function defined on the domain, that additionally satisfies a given property, such as convexity. This basic problem underlies research questions in many areas, such as learning, property testing, and game theory. We formally study the problem of extending partial functions to satisfy fundamental properties in combinatorial optimization, focusing on upper and lower bounds for extension and applications to learning and property testing. (1) For subadditive functions, we show the extension problem is coNP-complete, and we give tight bounds on the approximability. We also give an improved lower bound for learning subadditive functions, and give the first nontrivial testers for subadditive and XOS functions. (2) For submodular functions, we show that if a partial function can be extended to a submodular function on the lattice closure (the minimal set that contains the partial function and is closed under union and intersection) of the partial function, it can be extended to a submodular function on the entire domain. We obtain algorithms for determining extendibility in a number of cases, including if $n$ is a constant, or the points are nearly the same size. The complexity of extendibility is in general unresolved. (3) Lastly, for convex functions in $\mathbb{R}^m$, we show an interesting juxtaposition: while we can determine the existence of an extension efficiently, computing the value of a widely-studied convex extension at a given point is strongly NP-hard.
Smart Humans... WannaDie?
Venue: arXiv cs.CY 2018
Year: 2018

It won't be long until our prostheses, ECG personal monitors, subcutaneous insulin infusors, glasses, etc. become devices of the Internet of Things (IoT), always connected for monitoring, maintenance, charging and tracking. This will be the dawn of the Smart Human, not just a user of the IoT but a Thing in the Internet. How long would it then take for hackers to attack us like they have been attacking IoT devices? What would happen if hackers were able to blackmail us threatening our IoT body parts? Smart Humans may become victims of the devastating attack of WannaDie, a new ransomware that could provide the plot-line for a possible future episode of the Black Mirror TV series.
A Study on Optimal Beam Patterns for Single User Massive MIMO Transmissions
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes optimal beam patterns of analog beamforming for SU (Single User) massive MIMO (Multi-Input Multi-Output) transmission systems. For hybrid beamforming in SU massive MIMO systems, there are several design parameters such as beam patterns, the number of beams (streams), the shape of array antennas, and so on. In conventional hybrid beamforming, rectangular patch array antennas implemented on a planar surface with linear phase shift beam patterns have been used widely. However, it remains unclear whether existing configurations are optimal or not. Therefore, we propose a method using OBPB (Optimal Beam Projection Beamforming) for designing configuration parameters of the hybrid beamforming. By using the method, the optimal beam patterns are derived first, and are projected on the assumed surface to calculate the achievable number of streams and the resulting channel capacity. The results indicate OBPB with a spherical surface yields at least 3.5 times higher channel capacity than conventional configurations.
An Empirical Evaluation of Allgatherv on Multi-GPU Systems
Venue: arXiv cs.DC 2018
Year: 2018

Applications for deep learning and big data analytics have compute and memory requirements that exceed the limits of a single GPU. However, effectively scaling out an application to multiple GPUs is challenging due to the complexities of communication between the GPUs, particularly for collective communication with irregular message sizes. In this work, we provide a performance evaluation of the Allgatherv routine on multi-GPU systems, focusing on GPU network topology and the communication library used. We present results from the OSU-micro benchmark as well as conduct a case study for sparse tensor factorization, one application that uses Allgatherv with highly irregular message sizes. We extend our existing tensor factorization tool to run on systems with different node counts and varying number of GPUs per node. We then evaluate the communication performance of our tool when using traditional MPI, CUDA-aware MVAPICH and NCCL across a suite of real-world data sets on three different systems: a 16-node cluster with one GPU per node, NVIDIA's DGX-1 with 8 GPUs and Cray's CS-Storm with 16 GPUs. Our results show that irregularity in the tensor data sets produce trends that contradict those in the OSU micro-benchmark, as well as trends that are absent from the benchmark.
Advanced Super-Resolution using Lossless Pooling Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a novel deep learning-based approach for still image super-resolution, that unlike the mainstream models does not rely solely on the input low resolution image for high quality upsampling, and takes advantage of a set of artificially created auxiliary self-replicas of the input image that are incorporated in the neural network to create an enhanced and accurate upscaling scheme. Inclusion of the proposed lossless pooling layers, and the fusion of the input self-replicas enable the model to exploit the high correlation between multiple instances of the same content, and eventually result in significant improvements in the quality of the super-resolution, which is confirmed by extensive evaluations.
Inferring the size of the causal universe: features and fusion of causal attribution networks
Venue: arXiv cs.SI 2018
Year: 2018

Cause-and-effect reasoning, the attribution of effects to causes, is one of the most powerful and unique skills humans possess. Multiple surveys are mapping out causal attributions as networks, but it is unclear how well these efforts can be combined. Further, the total size of the collective causal attribution network held by humans is currently unknown, making it challenging to assess the progress of these surveys. Here we study three causal attribution networks to determine how well they can be combined into a single network. Combining these networks requires dealing with ambiguous nodes, as nodes represent written descriptions of causes and effects and different descriptions may exist for the same concept. We introduce NetFUSES, a method for combining networks with ambiguous nodes. Crucially, treating the different causal attributions networks as independent samples allows us to use their overlap to estimate the total size of the collective causal attribution network. We find that existing surveys capture 5.77% $\pm$ 0.781% of the $\approx$293 000 causes and effects estimated to exist, and 0.198% $\pm$ 0.174% of the $\approx$10 200 000 attributed cause-effect relationships.
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
Venue: arXiv cs.CL 2018
Year: 2018

State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes. To exploit these benefits in a more sophisticated way, we propose a novel deep neural multi-task learning framework with explicit feedback strategies to jointly model recognition and normalization. On one hand, our method benefits from the general representations of both tasks provided by multi-task learning. On the other hand, our method successfully converts hierarchical tasks into a parallel multi-task setting while maintaining the mutual supports between tasks. Both of these aspects improve the model performance. Experimental results demonstrate that our method performs significantly better than state-of-the-art approaches on two publicly available medical literature datasets.
Humanoid Robot-Application and Influence
Venue: arXiv cs.RO 2018
Year: 2018

Application of humanoid robots has been common in the field of healthcare and education. It has been recurrently used to improve social behavior and mollify distress level among children with autism, cancer and cerebral palsy. This article discusses the same from a human factors perspective. It shows how people of different age and gender have a different opinion towards the application and acceptance of humanoid robots. Additionally, this article highlights the influence of cerebral condition and social interaction on a user behavior and attitude towards humanoid robots. Our study performed a literature review and found that (a) children and elderly individuals prefer humanoid robots due to inactive social interaction, (b) The deterministic behavior of humanoid robots can be acknowledged to improve social behavior of autistic children, (c) Trust on humanoid robots is highly driven by its application and a user age, gender, and social life.
Individual decision making in task-oriented groups
Venue: arXiv cs.MA 2018
Year: 2018

The strategies adopted by individuals to select relevant information to pass on are central to understanding problem solving by groups. Here we use agent-based simulations to revisit a cooperative problem-solving scenario where the task is to find the common card in decks distributed to the group members. The agents can display only a sample of their cards and we explore different strategies to select those samples based on the confidences assigned to the cards. An agent's confidence that a particular card is the correct one is given by the number of times it observed that card in the decks of the other agents. We use a Gibbs distribution to select the card samples with the temperature measuring the strength of a noise that prevents the agents to correctly rank the cards. The group is guaranteed to find the common card in all runs solely in the infinite temperature limit, where the cards are sampled regardless of their confidences. In this case, we obtain the scaling form of the time constant that characterizes the asymptotic exponential decay of the failure probability. For finite time, however, a finite temperature yields a probability of failure that is several orders of magnitude lower than in the infinite temperature limit. The available experimental results are consistent with the decision-making model for finite temperature only.
Bootstrapping Conversational Agents With Weak Supervision
Venue: arXiv cs.AI 2018
Year: 2018

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.
Causal Identification under Markov Equivalence
Venue: arXiv cs.AI 2018
Year: 2018

Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph. In many practical settings, however, the knowledge available for the researcher is not strong enough so as to specify a unique causal graph. Another line of investigation attempts to use observational data to learn a qualitative description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of observed features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, represented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced subgraphs. We then develop an algorithm to compute the effect of an arbitrary set of variables on an arbitrary outcome set. We show that the algorithm is strictly more powerful than the current state of the art found in the literature.
A Survey of Privacy Infrastructures and Their Vulnerabilities
Venue: arXiv cs.CR 2018
Year: 2018

Over the last two decades, the scale and complexity of Anonymous networks and its associated technologies grows exponentially as privacy has become a major concern of individuals. Also, some cyber attackers make use of privacy infrastructures including botnets and Tor to do illegal activities like drug, contraband or DDoS attack. However, anonymous networks are not perfect, there are some methods could exploit the vulnerabilities and track user information. In this paper, we analyze few of privacy infrastructures and their vulnerabilities.