OpenRaiser/Intern-Atlas huggingface dataset:
Joint Power Allocation and Load Balancing Optimization for Energy-Efficient Cell-Free Massive MIMO Networks
Venue: arXiv cs.IT 2020
Year: 2020

Large-scale distributed antenna systems with many access points (APs) that serve the users by coherent joint transmission is being considered for 5G-and-beyond networks. The technology is called Cell-free Massive MIMO and can provide a more uniform service level to the users than a conventional cellular topology. For a given user set, only a subset of the APs is likely needed to satisfy the users' performance demands, particularly outside the peak traffic hours. To find achieve an energy-efficient load balancing, we minimize the total downlink power consumption at the APs, considering both the transmit powers and hardware dissipation. APs can be temporarily turned off to reduce the latter part. The formulated optimization problem is non-convex but, nevertheless, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. Since the computational complexity is prohibitive for real-time implementation, we also propose two low-complexity algorithms that exploit the inherent group-sparsity and the optimized transmit powers in the problem formulation. Numerical results manifest that our optimization algorithms can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers. Moreover, the low-complexity algorithms can effectively handle the power allocation and AP activation for large-scale networks.
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks
Venue: arXiv cs.NI 2020
Year: 2020

One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series.
A workload-adaptive mechanism for linear queries under local differential privacy
Venue: arXiv cs.DB 2020
Year: 2020

We propose a new mechanism to accurately answer a user-provided set of linear counting queries under local differential privacy (LDP). Given a set of linear counting queries (the workload) our mechanism automatically adapts to provide accuracy on the workload queries. We define a parametric class of mechanisms that produce unbiased estimates of the workload, and formulate a constrained optimization problem to select a mechanism from this class that minimizes expected total squared error. We solve this optimization problem numerically using projected gradient descent and provide an efficient implementation that scales to large workloads. We demonstrate the effectiveness of our optimization-based approach in a wide variety of settings, showing that it outperforms many competitors, even outperforming existing mechanisms on the workloads for which they were intended.
Illumination adaptive person reid based on teacher-student model and adversarial training
Venue: arXiv cs.CV 2020
Year: 2020

Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation. However, the changes in the illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Its innovations are threefold: (1) A discriminative entropy loss to ensure the ReID features contain no lighting information. (2) A ReID Teacher model trained by images under "neutral" lighting conditions to guide ReID classification. (3) An illumination Teacher model trained by the differences between the illumination-adjusted and original images to guide illumination classification. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.
Convergence analysis of particle swarm optimization using stochastic Lyapunov functions and quantifier elimination
Venue: arXiv cs.NE 2020
Year: 2020

This paper adds to the discussion about theoretical aspects of particle swarm stability by proposing to employ stochastic Lyapunov functions and to determine the convergence set by quantifier elimination. We present a computational procedure and show that this approach leads to reevaluation and extension of previously know stability regions for PSO using a Lyapunov approach under stagnation assumptions.
Rate-Splitting Multiple Access for Multibeam Satellite Communications
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the beamforming design problem to achieve max-min fairness (MMF) in multibeam satellite communications. Contrary to the conventional linear precoding (NoRS) that relies on fully treating any residual interference as noise, we consider a novel multibeam multicast beamforming strategy based on Rate-Splitting Multiple Access (RSMA). RSMA relies on linearly precoded ratesplitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at receivers to enable a flexible framework for non-orthogonal transmission and robust interbeam interference management. Aiming at achieving MMF among multiple co-channel multicast beams, a per-feed available power constrained optimization problem is formulated with different quality of channel state information at the transmitter (CSIT). The superiority of RS for multigroup multicast and multibeam satellite communication systems compared with conventional scheme (NoRS) is demonstrated via simulations.
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
Venue: arXiv cs.SE 2020
Year: 2020

Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, leading to a large amount of issues and problems. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: 1) the impact of the big data attributes of volume, velocity, and variety on the quality of big data applications; 2) the quality attributes that determine the quality for big data applications include correctness, performance, availability, scalability, reliability and so on; 3) the existing QA technologies, including analysis, specification, model-driven architecture (MDA), verification, fault tolerance, testing, monitoring and fault & failure prediction; 4) existing strengths and limitations of each kind of QA technology; 5) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications. However, many challenges of big data applications regarding quality still remain.
Patterns for Name Analysis and Type Analysis with JastAdd
Venue: arXiv cs.PL 2020
Year: 2020

In the last two decades, tools have been implemented to more formally specify the semantic analysis phase of a compiler instead of relying on handwritten code. In this paper, we introduce patterns and a method to translate a formal definition of a language's type system into a specification for JastAdd, which is one of the aforementioned tools based on Reference Attribute Grammars. This methodological approach will help language designers and compiler engineers to more systematically use such tools for semantic analysis. As an example, we use a simple, yet complete imperative language and provide an outlook on how the method can be extended to cover further language constructs or even type inference.
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
Venue: arXiv cs.IR 2020
Year: 2020

Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank documents. Given the same amount of time, a faster re-ranking model can incorporate more documents than a less efficient one, leading to a higher effectiveness. To utilize this property, we propose TK (Transformer-Kernel): a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of Transformer layers (up to three) to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. TK offers an optimal ratio between effectiveness and efficiency: under realistic time constraints (max. 200 ms per query) TK achieves the highest effectiveness in comparison to BERT and other re-ranking models. We demonstrate this on three large-scale ranking collections: MSMARCO-Passage, MSMARCO-Document, and TREC CAR. In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
Wasserstein Exponential Kernels
Venue: arXiv cs.LG 2020
Year: 2020

In the context of kernel methods, the similarity between data points is encoded by the kernel function which is often defined thanks to the Euclidean distance, a common example being the squared exponential kernel. Recently, other distances relying on optimal transport theory - such as the Wasserstein distance between probability distributions - have shown their practical relevance for different machine learning techniques. In this paper, we study the use of exponential kernels defined thanks to the regularized Wasserstein distance and discuss their positive definiteness. More specifically, we define Wasserstein feature maps and illustrate their interest for supervised learning problems involving shapes and images. Empirically, Wasserstein squared exponential kernels are shown to yield smaller classification errors on small training sets of shapes, compared to analogous classifiers using Euclidean distances.
Polylidar -- Polygons from Triangular Meshes
Venue: arXiv cs.CG 2020
Year: 2020

This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets, including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization. The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular mesh regions representing the shape of the point set. Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented to comparatively evaluate Polylidar speed and accuracy. Results show comparable accuracy and more than four times speedup compared to other concave polygon extraction methods.
FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction
Venue: arXiv cs.CE 2020
Year: 2020

An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.
Hybrid Pass Transistor Logic with Ambipolar Transistors
Venue: arXiv cs.ET 2020
Year: 2020

In comparison to the conventional complementary pull-up and pull-down logic structure, the pass transistor logic (PTL) family reduces the number of transistors required to perform logic functions, thereby reducing both area and power consumption. However, this logic family requires inter-stage inverters to ensure signal integrity in cascaded logic circuits, and inverters must be used to provide each logical input signal in its complementary form. These inverters and complementary signals increase the device count and significantly degrade overall system efficiency. Dual-gate ambipolar field-effect transistors natively provide a single-transistor XNOR operation and permit highly-efficient and compact circuits due to their ambipolar capabilities. Similar to PTL, logic circuits based on ambipolar field-effect transistors require complementary signals. Therefore, numerous inverters are required, with significant energy and area costs. Ambipolar field-effect transistors are a natural match for PTL, as hybrid ambipolar-PTL circuits can simultaneously use these inverters to satisfy their necessity in both PTL and ambipolar circuits. We therefore propose a new hybrid ambipolar-PTL logic family that exploits the compact logic of PTL and the ambipolar capabilities of ambipolar field-effect transistors. Novel hybrid ambipolar-PTL circuits were designed and simulated in SPICE, demonstrating strong signal integrity along with the efficiency advantages of using the required inverters to simultaneously satisfy the requirements of PTL and ambipolar circuits. In comparison to the ambipolar field-effect transistors in the conventional CMOS logic structure, our hybrid full adder circuit can reduce propagation delay by 47%, energy consumption by 88%, energy-delay product by a factor of 9, and area-energy-delay product by a factor of 20.
A Domain Semantics for Higher-Order Recursive Processes
Venue: arXiv cs.PL 2020
Year: 2020

The polarized SILL programming language uniformly integrates functional programming and session-typed message-passing concurrency. It supports general recursion, asynchronous and synchronous communication, and higher-order programs that communicate channels and processes. We give polarized SILL a domain-theoretic semantics---the first denotational semantics for a language with this combination of features. Session types in polarized SILL denote pairs of domains of unidirectional communications. Processes denote continuous functions between these domains, and process composition is interpreted by a trace operator. We illustrate our semantics by validating expected program equivalences.
On Throughput of Compressive Random Access for One Short Message Delivery in IoT
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we study compressive random access (CRA) with two stages for machine-type communication (MTC) in cellular Internet-of-Things (IoT). In particular, we consider the case that each user (IoT device or sensor) has only one short message (of the same length) when it is activated to send data in IoT applications. Two different CRA-based random access schemes are discussed (one is conventional and the other is new based on a simplified handshaking process). Based on the throughput analysis, we show that the CRA-based random access scheme with simplified handshaking process can outperform as its length of payload is adaptively decided depending on the number of active users. Simulation results confirm that the derived throughput expressions agree with them and can be used to design a random access system for MTC with each active device or sensor that has one short message.
Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories
Venue: arXiv cs.LG 2020
Year: 2020

Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution deteriorates the quality of embeddings due to data sparsity, especially in less populated areas. We address this issue by proposing a method that generates fine grained place embeddings, which leverages spatial hierarchical information according to the local density of observed data points. The effectiveness of our fine grained place embeddings are compared to baseline methods via next place prediction tasks using real world trajectory data from 3 cities in Japan. In addition, we demonstrate the value of our fine grained place embeddings for land use classification applications. We believe that our technique of incorporating spatial hierarchical information can complement and reinforce various place embedding generating methods.
Finite Hilbert Transform in Weighted L2 Spaces
Venue: arXiv cs.LG 2020
Year: 2020

Several new properties of weighted Hilbert transform are obtained. If mu is zero, two Plancherel-like equations and the isotropic properties are derived. For mu is real number, a coerciveness is derived and two iterative sequences are constructed to find the inversion. The proposed iterative sequences are applicable to the case of pure imaginary constant mu=i*eta with |eta|<pi/4 . For mu=0.0 and 3.0 , we present the computer simulation results by using the Chebyshev series representation of finite Hilbert transform. The results in this paper are useful to the half scan in several imaging applications.
Minimizing Dynamic Regret and Adaptive Regret Simultaneously
Venue: arXiv cs.LG 2020
Year: 2020

Regret minimization is treated as the golden rule in the traditional study of online learning. However, regret minimization algorithms tend to converge to the static optimum, thus being suboptimal for changing environments. To address this limitation, new performance measures, including dynamic regret and adaptive regret have been proposed to guide the design of online algorithms. The former one aims to minimize the global regret with respect to a sequence of changing comparators, and the latter one attempts to minimize every local regret with respect to a fixed comparator. Existing algorithms for dynamic regret and adaptive regret are developed independently, and only target one performance measure. In this paper, we bridge this gap by proposing novel online algorithms that are able to minimize the dynamic regret and adaptive regret simultaneously. In fact, our theoretical guarantee is even stronger in the sense that one algorithm is able to minimize the dynamic regret over any interval.
Low Overhead Online Data Flow Tracking for Intermittently Powered Non-volatile FPGAs
Venue: arXiv cs.AR 2020
Year: 2020

Energy harvesting is an attractive way to power future IoT devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable. While FPGAs have been widely adopted in various embedded systems, it is hard to survive unstable power since all the memory components in FPGA are based on volatile SRAMs. The emerging non-volatile memory based FPGAs provide promising potentials to keep configuration data on the chip during power outages. Few works have considered implementing efficient runtime intermediate data checkpoint on non-volatile FPGAs. To realize accumulative computation under intermittent power on FPGA, this paper proposes a low-cost design framework, Data-Flow-Tracking FPGA (DFT-FPGA), which utilizes binary counters to track intermediate data flow. Instead of keeping all on-chip intermediate data, DFT-FPGA only targets on necessary data that is labeled by off-line analysis and identified by an online tracking system. The evaluation shows that compared with state-of-the-art techniques, DFT-FPGA can realize accumulative computing with less off-line workload and significantly reduce online roll-back time and resource utilization.
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation
Venue: arXiv cs.CL 2020
Year: 2020

Unstructured Persona-oriented Dialogue Systems (UPDS) has been demonstrated effective in generating persona consistent responses by utilizing predefined natural language user persona descriptions (e.g., "I am a vegan"). However, the predefined user persona descriptions are usually short and limited to only a few descriptive words, which makes it hard to correlate them with the dialogues. As a result, existing methods either fail to use the persona description or use them improperly when generating persona consistent responses. To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses. PEE consists of two main modules: persona exploration and persona exploitation. The former learns to extend the predefined user persona description by mining and correlating with existing dialogue corpus using a variational auto-encoder (VAE) based topic model. The latter learns to generate persona consistent responses by utilizing the predefined and extended user persona description. In order to make persona exploitation learn to utilize user persona description more properly, we also introduce two persona-oriented loss functions: Persona-oriented Matching (P-Match) loss and Persona-oriented Bag-of-Words (P-BoWs) loss which respectively supervise persona selection in encoder and decoder. Experimental results show that our approach outperforms state-of-the-art baselines, in terms of both automatic and human evaluations.