Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter

Gain Modulation by an Urgency Signal Controls the Speed–Accuracy Trade-Off in a Network Model of a Cortical Decision Circuit

The speed–accuracy trade-off (SAT) is ubiquitous in decision tasks. While the neural mechanisms underlying decisions are generally well characterized, the application of decision-theoretic methods to the SAT has been difficult to reconcile with experimental data suggesting that decision thresholds are inflexible. Using a network model of a cortical decision circuit, we demonstrate the SAT in a manner consistent with neural and behavioral data and with mathematical models that optimize speed and accuracy with respect to one another. In simulations of a reaction time task, we modulate the gain of the network with a signal encoding the urgency to respond. As the urgency signal builds up, the network progresses through a series of processing stages supporting noise filtering, integration of evidence, amplification of integrated evidence, and choice selection. Analysis of the network's dynamics formally characterizes this progression. Slower buildup of urgency increases accuracy by slowing down the progression. Faster buildup has the opposite effect. Because the network always progresses through the same stages, decision-selective firing rates are stereotyped at decision time

Understanding the user display names across social networks

The display names that an individual uses in various online social networks always contain some redundant information because some people tend to use the similar names across different networks to make them easier to remember or to build their online reputation. In this paper, we aim to measure the redundant information between different display names of the same individual. Based on the cross-site linking function, we first develop a specific distributed crawler to extract the display names that individuals select for different social networks, and we give an overview on the display names we extracted. Then we measure and analyze the redundant information in three ways: length similarity, character similarity and letter distribution similarity, comparing with display names of different individuals. We also analyze the evolution of redundant information over time. We find 45% of users tend to use the same display name across OSNs. Our findings also demonstrate that display names of the same individual show high similarity. The evolution analysis results show that redundant information is time-independent. Awareness of the redundant information between the display names can benefit many applications, such as user identification across social networks

A deep dive into user display names across social networks

The display names from an individual across Online Social Networks (OSNs) always contain abundant information redundancies because most users tend to use one main name or similar names across OSNs to make them easier to remember or to build their online reputation. These information redundancies are of great benefit to information fusion across OSNs. In this paper, we aim to measure these information redundancies between different display names of the same individual. Based on the cross-site linking function of Foursquare, we first develop a distributed crawler to extract the display names that individuals used in Facebook, Twitter and Foursquare, respectively. We construct three display name datasets across three OSNs, and measure the information redundancies in three ways: length similarity, character similarity and letter distribution similarity. We also analyze the evolution of redundant information over time. Finally, we apply the measurement results to the user identification across OSNs. We find that (1) more than 45% of users tend to use the same display name across OSNs; (2) the display names of the same individual for different OSNs show high similarity; (3) the information redundancies of display names are time-independent; (4) the AUC values of user identification results only based on display names are more than 0.9 on three datasets

Matching user accounts across social networks based on username and display name

Matching user accounts across social networks is helpful for building better user profile, which has practical significance for many applications. It has attracted many scholars’ attention. Existing works are mainly based on the rich online profiles or activities. However, due to privacy settings or some other specific purposes, the online rich data is usually unavailable, incomplete or unreliable. This makes the existing schemes fail to work properly. Users often make their display names and/or usernames public on different social networks. These names belonging to the same user often contain affluent information redundancies, which provide an opportunity to address the matching problem. In this paper, we focus on the problem of matching user accounts across social networks solely based on username and display name. The problem is two-fold: 1) how to characterize those information redundancies contained in the usernames or display names; 2) how to match the user accounts based on these information redundancies. To address this problem, we propose a solution to User Identification across Social Network based on Username and Display name (UISN-UD), which consists of three key components: 1) extracting features that exploit the information redundancies among names based on user naming habits; 2) training a two-stage classification framework to tackle the user identification problem based on the extracted features; 3) employing the Gale-Shapley algorithm to eliminate the one-to-many or many-to-many relationships existed in the identification results. We perform the experiments based on real social network datasets and the results show that the proposed method can provide excellent performance with F1 values reaching 90%+. From a computational point of view, comparing display names and/or usernames is surely more convenient than comparing the online rich profile attributes or activities of two accounts. This work shows the possibility of matching the user accounts with high accessible and small amount of online data

Matching user accounts based on user generated content across social networks

Matching user accounts can help us build better users' profiles and benefit many applications. It has attracted much attention from both industry and academia. Most of existing works are mainly based on rich user profile attributes. However, in many cases, user profile attributes are unavailable, incomplete or unreliable, either due to the privacy settings or just because users decline to share their information. This makes the existing schemes quite fragile. Users often share their activities on different social networks. This provides an opportunity to overcome the above problem. We aim to address the problem of user identification based on User Generated Content (UGC). We first formulate the problem of user identification based on UGCs and then propose a UGC-based user identification model. A supervised machine learning based solution is presented. It has three steps: firstly, we propose several algorithms to measure the spatial similarity, temporal similarity and content similarity of two UGCs; secondly, we extract the spatial, temporal and content features to exploit these similarities; afterwards, we employ the machine learning method to match user accounts, and conduct the experiments on three ground truth datasets. The results show that the proposed method has given excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work presents the possibility of matching user accounts with high accessible online data

Trading speed and accuracy by coding time: a coupled-circuit cortical model.

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by 'climbing' activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification