OpenRaiser/Intern-Atlas huggingface dataset:
An improved spectral clustering method for mixed membership community detection
Venue: arXiv cs.SI 2020
Year: 2020

Community detection has been well studied recent years, but the more realistic case of mixed membership community detection remains a challenge. Here, we develop an efficient spectral algorithm Mixed-ISC based on applying more than K eigenvectors for clustering given K communities for estimating the community memberships under the degree-corrected mixed membership (DCMM) model. We show that the algorithm is asymptotically consistent. Numerical experiments on both simulated networks and many empirical networks demonstrate that Mixed-ISC performs well compared to a number of benchmark methods for mixed membership community detection. Especially, Mixed-ISC provides satisfactory performances on weak signal networks.
LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness
Venue: arXiv cs.LG 2020
Year: 2020

Since the dawn of mankind's introduction to powered flights, there have been multiple incidents which can be attributed to aircraft stalls. Most modern-day aircraft are equipped with advanced warning systems to warn the pilots about a potential stall, so that pilots may adopt the necessary recovery measures. But these warnings often have a short window before the aircraft actually enters a stall and require the pilots to act promptly to prevent it. In this paper, we propose a deep learning based approach to predict an Impending stall, well in advance, even before the stall-warning is triggered. We leverage the capabilities of long short-term memory (LSTM) recurrent neural networks (RNN) and propose a novel approach to predict potential stalls from the sequential in-flight sensor data. Three different neural network architectures were explored. The neural network models, trained on 26400 seconds of simulator flight data are able to predict a potential stall with > 95% accuracy, approximately 10 seconds in advance of the stall-warning trigger. This can significantly augment the Pilot's preparedness to handle an unexpected stall and will add an additional layer of safety to the traditional stall warning systems.
Improving Gradient Flow with Unrolled Highway Expectation Maximization
Venue: arXiv cs.LG 2020
Year: 2020

Integrating model-based machine learning methods into deep neural architectures allows one to leverage both the expressive power of deep neural nets and the ability of model-based methods to incorporate domain-specific knowledge. In particular, many works have employed the expectation maximization (EM) algorithm in the form of an unrolled layer-wise structure that is jointly trained with a backbone neural network. However, it is difficult to discriminatively train the backbone network by backpropagating through the EM iterations as they are prone to the vanishing gradient problem. To address this issue, we propose Highway Expectation Maximization Networks (HEMNet), which is comprised of unrolled iterations of the generalized EM (GEM) algorithm based on the Newton-Rahpson method. HEMNet features scaled skip connections, or highways, along the depths of the unrolled architecture, resulting in improved gradient flow during backpropagation while incurring negligible additional computation and memory costs compared to standard unrolled EM. Furthermore, HEMNet preserves the underlying EM procedure, thereby fully retaining the convergence properties of the original EM algorithm. We achieve significant improvement in performance on several semantic segmentation benchmarks and empirically show that HEMNet effectively alleviates gradient decay.
On Knowledge Distillation for Direct Speech Translation
Venue: arXiv cs.CL 2020
Year: 2020

Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT). For MT, one of the most promising techniques to transfer knowledge is knowledge distillation. In this paper, we compare the different solutions to distill knowledge in a sequence-to-sequence task like ST. Moreover, we analyze eventual drawbacks of this approach and how to alleviate them maintaining the benefits in terms of translation quality.
Driving Behavior Explanation with Multi-level Fusion
Venue: arXiv cs.LG 2020
Year: 2020

In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions. In this work, we focus on generating high-level driving explanations as the vehicle drives. We present BEEF, for BEhavior Explanation with Fusion, a deep architecture which explains the behavior of a trajectory prediction model. Supervised by annotations of human driving decisions justifications, BEEF learns to fuse features from multiple levels. Leveraging recent advances in the multi-modal fusion literature, BEEF is carefully designed to model the correlations between high-level decisions features and mid-level perceptual features. The flexibility and efficiency of our approach are validated with extensive experiments on the HDD and BDD-X datasets.
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing
Venue: arXiv cs.CL 2020
Year: 2020

The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which normally contains multiple table schemas. Previous studies on this task usually utilized contextual information to enrich utterance representations and to further influence the decoding process. While they ignored to describe and track the interaction states which are determined by history SQL queries and are related with the intent of current utterance. In this paper, two kinds of interaction states are defined based on schema items and SQL keywords separately. A relational graph neural network and a non-linear layer are designed to update the representations of these two states respectively. The dynamic schema-state and SQL-state representations are then utilized to decode the SQL query corresponding to current utterance. Experimental results on the challenging CoSQL dataset demonstrate the effectiveness of our proposed method, which achieves better performance than other published methods on the task leaderboard.
Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
Venue: arXiv cs.CV 2020
Year: 2020

Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process.
Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data
Venue: arXiv cs.LG 2020
Year: 2020

Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial mathematics and many others. Despite the existence of algorithms that learn the dynamics from trajectorial measurements there are few attempts to infer the dynamical system straight from population data. In this work, we deduce and then computationally estimate the Fokker-Planck equation which describes the evolution of the population's probability density, based on stochastic differential equations. Then, following the USDL approach, we project the Fokker-Planck equation to a proper set of test functions, transforming it into a linear system of equations. Finally, we apply sparse inference methods to solve the latter system and thus induce the driving forces of the dynamical system. Our approach is illustrated in both synthetic and real data including non-linear, multimodal stochastic differential equations, biochemical reaction networks as well as mass cytometry biological measurements.
Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing
Venue: arXiv cs.DC 2020
Year: 2020

The development of mobile services has impacted a variety of computation-intensive and time-sensitive applications, such as recommendation systems and daily payment methods. However, computing task competition involving limited resources increases the task processing latency and energy consumption of mobile devices, as well as time constraints. Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading. On the one hand, they focus on independent tasks rather than dependent tasks. The challenges of task dependency in the real world, especially task segmentation and integration, remain to be addressed. On the other hand, the multiuser scenarios related to resource allocation and the mutex access problem must be considered. In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance. First, we discuss the task dependency model, task priority model, energy consumption model, and average latency from the perspective of server clusters and multidependence on mobile tasks. Our method based on these models is introduced to formalize communication behavior among multiple agents; then, reinforcement learning is executed as an offloading strategy to obtain the results. Because of the incomplete state information, long short-term memory (LSTM) is employed as a decision-making tool to assess the internal state. Moreover, to optimize and support effective action, we consider using a bidirectional recurrent neural network (BRNN) to learn and enhance features obtained from agents' communication. Finally, we simulate experiments on the Alibaba cluster dataset. The results show that our method is better than other baselines in terms of energy consumption, load status and latency.
Shrinkage of Decision Lists and DNF Formulas
Venue: arXiv cs.CC 2020
Year: 2020

We establish nearly tight bounds on the expected shrinkage of decision lists and DNF formulas under the $p$-random restriction $\mathbf R_p$ for all values of $p \in [0,1]$. For a function $f$ with domain $\{0,1\}^n$, let $\mathrm{DL}(f)$ denote the minimum size of a decision list that computes $f$. We show that \[ \mathbb E[\ \mathrm{DL}(f{\upharpoonright}\mathbf R_p)\ ] \le \mathrm{DL}(f)^{\log_{2/(1-p)}(\frac{1+p}{1-p})}. \] For example, this bound is $\sqrt{\mathrm{DL}(f)}$ when $p = \sqrt{5}-2 \approx 0.24$. For Boolean functions $f$, we obtain the same shrinkage bound with respect to DNF formula size plus $1$ (i.e., replacing $\mathrm{DL}(\cdot)$ with $\mathrm{DNF}(\cdot)+1$ on both sides of the inequality).
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks
Venue: arXiv cs.LG 2020
Year: 2020

With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide the optimal scheduling policy that maximizes the convergence rate. We also study the effect of local computation steps and communication steps on the convergence of the proposed algorithm. We prove, in practice, federated learning algorithms may solve a different problem than the one that they have been employed for if the unreliability of wireless channels is neglected. Finally, through numerous experiments on real and synthetic datasets, we demonstrate the convergence of our proposed algorithm.
EMRs with Blockchain : A distributed democratised Electronic Medical Record sharing platform
Venue: arXiv cs.CR 2020
Year: 2020

Medical data sharing needs to be done with the utmost respect for privacy and security. It contains intimate data of the patient and any access to it must be highly regulated. With the emergence of vertical solutions in healthcare institutions, interoperability across organisations has been hindered. The authors of this paper propose a blockchain based medical-data sharing solution, utilising Hyperledger Fabric to regulate access to medical data, and using the InterPlanatory File System for its storage. We believe that the combination of these two distributed solutions can enable patients to access their medical records across healthcare institutions while ensuring non-repudiation, immutability and providing data-ownership. It would enable healthcare practitioners to access all previous medical records in a single location, empowering them with the data required for the effective diagnosis and treatment of patients. Making it safe and straightforward, it would also enable patients to share medical data with research institutions, leading to the creation of reliable data sets, laying the groundwork required for the creation of personalised medicine.
COMPACT: Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique
Venue: arXiv cs.CG 2020
Year: 2020

Packing optimization is a prevalent problem that necessitates robust and efficient algorithms that are also simple to implement. One group of approaches is the raster methods, which rely on approximating the objects with pixelated representations. Although they are versatile in treating irregular geometries, the raster methods received limited attention in solving problems involving rotatable objects, where available studies generally analyze only right-angled rotations. In addition, raster approximation allows the use of unique performance metrics and indirect consideration of constraints, which have not been exploited in the literature. This study presents the new Concurrent or Ordered Matrix-based Packing Arrangement Computation Technique (COMPACT). The method relies on raster representations of the objects that can be rotated by arbitrary angles, unlike the right-angled rotation restrictions imposed in many existing packing optimization studies based on raster methods. The raster approximations are obtained through loop-free operations that improve efficiency. Besides, a novel performance metric is introduced, which favors efficient filling of the available space by maximizing the internal contact between the objects as well as the contact between the objects and domain boundaries. Moreover, the objective functions are exploited to discard overlap and overflow constraints and enable the use of unconstrained optimization methods. Several test problems involving concurrent and ordered packing of multiple rectangular and circular objects into square bins are investigated. The results show that the proposed technique performs effectively in determining the packing arrangements.
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present an approach to tactile pose estimation from the first touch for known objects. First, we create an object-agnostic map from real tactile observations to contact shapes. Next, for a new object with known geometry, we learn a tailored perception model completely in simulation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor output, we match it against the pre-computed set using the object-specific embedding learned purely in simulation using contrastive learning. This results in a perception model that can localize objects from a single tactile observation. It also allows reasoning over pose distributions and including additional pose constraints coming from other perception systems or multiple contacts. We provide quantitative results for four objects. Our approach provides high accuracy pose estimations from distinctive tactile observations while regressing pose distributions to account for those contact shapes that could result from different object poses. We further extend and test our approach in multi-contact scenarios where several tactile sensors are simultaneously in contact with the object. Website: http://mcube.mit.edu/research/tactile_loc_first_touch.html
On the Binding Problem in Artificial Neural Networks
Venue: arXiv cs.NE 2020
Year: 2020

Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences. In this paper, we argue that the underlying cause for this shortcoming is their inability to dynamically and flexibly bind information that is distributed throughout the network. This binding problem affects their capacity to acquire a compositional understanding of the world in terms of symbol-like entities (like objects), which is crucial for generalizing in predictable and systematic ways. To address this issue, we propose a unifying framework that revolves around forming meaningful entities from unstructured sensory inputs (segregation), maintaining this separation of information at a representational level (representation), and using these entities to construct new inferences, predictions, and behaviors (composition). Our analysis draws inspiration from a wealth of research in neuroscience and cognitive psychology, and surveys relevant mechanisms from the machine learning literature, to help identify a combination of inductive biases that allow symbolic information processing to emerge naturally in neural networks. We believe that a compositional approach to AI, in terms of grounded symbol-like representations, is of fundamental importance for realizing human-level generalization, and we hope that this paper may contribute towards that goal as a reference and inspiration.
On the Lattice of Conceptual Measurements
Venue: arXiv cs.AI 2020
Year: 2020

We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
Wi-Fi Based Indoor Positioning System For Mobile Robots By Using Particle Filter
Venue: arXiv cs.RO 2020
Year: 2020

Mobile robots have the capability to work in real-time autonomously. Autonomous behavior is strictly dependent on knowing the position of the mobile robot. The positioning of a mobile robot in an indoor area is a difficult task for only one sensor information is used. We proposed a system and method to locate the mobile robot via fusing signals from WIFI and odometer data via particle filter. In this study, the Particle filter is a well-known filter that is used for indoor positioning of mobile robots. The proposed system includes two parts that are RFKON system and evarobot for data collection and experiments. The Received Signal Strength (RSS) measurements of the WiFi access points that are located in any environment are used to locate a stationary mobile robot in one floor area via SIS Particle Filter. RSS measurements from the RFKON database are used and the average location error is 0.7606 and 0.1495 m for 300 and 1000 particles respectively.
Topological Planning with Transformers for Vision-and-Language Navigation
Venue: arXiv cs.RO 2020
Year: 2020

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. forward, rotate) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.
Data-Driven Intersection Management Solutions for Mixed Traffic of Human-Driven and Connected and Automated Vehicles
Venue: arXiv cs.LG 2020
Year: 2020

This dissertation proposes two solutions for urban traffic control in the presence of connected and automated vehicles. First a centralized platoon-based controller is proposed for the cooperative intersection management problem that takes advantage of the platooning systems and V2I communication to generate fast and smooth traffic flow at a single intersection. Second, a data-driven approach is proposed for adaptive signal control in the presence of connected vehicles. The proposed system relies on a data-driven method for optimal signal timing and a data-driven heuristic method for estimating routing decisions. It requires no additional sensors to be installed at the intersection, reducing the installation costs compared to typical settings of state-of-the-practice adaptive signal controllers. The proposed traffic controller contains an optimal signal timing module and a traffic state estimator. The signal timing module is a neural network model trained on microscopic simulation data to achieve optimal results according to a given performance metric such as vehicular delay or average queue length. The traffic state estimator relies on connected vehicles' information to estimate the traffic's routing decisions. A heuristic method is proposed to minimize the estimation error. With sufficient parameter tuning, the estimation error decreases as the market penetration rate (MPR) of connected vehicles grows. Estimation error is below 30% for an MPR of 10% and it shrinks below 20% when MPR grows larger than 30%. Simulations showed that the proposed traffic controller outperforms Highway Capacity Manual's methodology and given proper offline parameter tuning, it can decrease average vehicular delay by up to 25%.