Local convexity inspired low-complexity non-coherent signal detector for nano-scale molecular communications

Molecular communications via diffusion (MCvD) represents a relatively new area of wireless data transfer with especially attractive characteristics for nanoscale applications. Due to the nature of diffusive propagation, one of the key challenges is to mitigate inter-symbol interference (ISI) that results from the long tail of channel response. Traditional coherent detectors rely on accurate channel estimations and incur a high computational complexity. Both of these constraints make coherent detection unrealistic for MCvD systems. In this paper, we propose a low-complexity and noncoherent signal detector, which exploits essentially the local convexity of the diffusive channel response. A threshold estimation mechanism is proposed to detect signals blindly, which can also adapt to channel variations. Compared to other noncoherent detectors, the proposed algorithm is capable of operating at high data rates and suppressing ISI from a large number of previous symbols. Numerical results demonstrate that not only is the ISI effectively suppressed, but the complexity is also reduced by only requiring summation operations. As a result, the proposed noncoherent scheme will provide the necessary potential to low-complexity molecular communications, especially for nanoscale applications with a limited computation and energy budget

Low-complexity non-coherent signal detection for nano-scale molecular communications

Nano-scale molecular communication is a viable way of exchanging information between nano-machines. In this letter, a low-complexity and non-coherent signal detection technique is proposed to mitigate the intersymbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed non-coherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The concentration difference is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nano-scale molecular communication systems with a small energy budget or limited computation resource

Differential-Algebraic Approach to Speed and Parameter Estimation of the Induction Motor

This thesis considers a differential-algebraic approach to estimating the speed and rotor time constant of an induction motor using only the measured terminal voltages and currents. It is shown that the induction motor speed satisfies both a second-order and a third-order polynomial equation whose coefficients depend the stator voltages, stator currents, and their derivatives. Further, it is shown that as long as the stator electrical frequency is nonzero, the speed is uniquely determined by these polynomials. The speed so determined is then used to stabilize a dynamic (Luenberger type) observer to obtain a smoothed speed estimate. With full knowledge of the machine parameters and filtering of the sensor noise, simulations and experiments indicate that this estimator has the potential to provide low speed (including zero speed) control of an induction motor under full load. A differential-algebraic approach is also used to obtain an estimate of the rotor time constant of an induction motor, again using only the measured stator voltages and currents. Experimental results are presented to demonstrate the practical use of the identification method

Earnings Management for Second-time IPOs: Evidence from China

In China’s IPO market, firms that fail in their first IPO application make considerable adjustments before making their second IPO application. Examining firms that applied for IPOs during 2004-2018, we find that failed IPO applicant firms “package” themselves to obtain approval of the China Securities Regulatory Commission (CSRC) by reducing accrual earnings management and increasing real earnings management. In addition, after a successful second IPO application, these firms relax their vigilance vis-à-vis the CSRC and increase both accrual and real earnings management. This pre-IPO “packaging” behavior deceives investors, leading to higher IPO prices and higher post-IPO returns

Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis

Current deep learning-based approaches for the segmentation of microscopy images heavily rely on large amount of training data with dense annotation, which is highly costly and laborious in practice. Compared to full annotation where the complete contour of objects is depicted, point annotations, specifically object centroids, are much easier to acquire and still provide crucial information about the objects for subsequent segmentation. In this paper, we assume access to point annotations only during training and develop a unified pipeline for microscopy image segmentation using synthetically generated training data. Our framework includes three stages: (1) it takes point annotations and samples a pseudo dense segmentation mask constrained with shape priors; (2) with an image generative model trained in an unpaired manner, it translates the mask to a realistic microscopy image regularized by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining high coherence between input masks and generated images. When using the identical segmentation backbones, the models trained on our synthetic dataset significantly outperform those trained with pseudo-labels or baseline-generated images. Moreover, our framework achieves comparable results to models trained on authentic microscopy images with dense labels, demonstrating its potential as a reliable and highly efficient alternative to labor-intensive manual pixel-wise annotations in microscopy image segmentation. The code is available.Comment: Accepted by The 3rd MICCAI Workshop on Data Augmentation, Labeling, and Imperfection

Exploring the antecedents of green human resource management: A path dependence perspective

Green HRM (GHRM) delineates organizations’ efforts to address environmental concerns. However, the current research has not thoroughly investigated the antecedents of GHRM. Moreover, the internal structure of GHRM remains unclear, further limiting our understanding of firms’ different approaches to GHRM adoption. Using a sample of Spanish firms, our first study revealed GHRM to be a two-dimensional construct, with one bundle of practices emphasizing employer branding and another bundle emphasizing employee green performance. In our second study, we draw upon path dependence theory to examine the relationship between the use of high-performance work systems (HPWS) and GHRM adoption using a sample of Spanish plants in highly polluting sectors. We further examine how a plant’s green strategy and industry emissions serve as contingencies influencing the relative adoption emphasis on the two GHRM bundles. Our findings suggest that the adoption of GHRM is influenced by HPWS coupled with their strategic and institutional context

Spectrum Sensing for Cognitive Radios with Unknown Noise Variance and Time-variant Fading Channels

The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios (CRs). Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we firstly formulate a dynamic state-space model (DSM) for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user (PU) states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the timevariant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability (MAP) criterion and the marginal particle filtering (MPF) schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.</p

FwdLLM: Efficient FedLLM using Forward Gradient

Large Language Models (LLMs) are transforming the landscape of mobile intelligence. Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM. Though recent efforts have addressed the network issue induced by the vast model size, they have not practically mitigated vital challenges concerning integration with mobile devices, such as significant memory consumption and sluggish model convergence. In response to these challenges, this work introduces FwdLLM, an innovative FL protocol designed to enhance the FedLLM efficiency. The key idea of FwdLLM to employ backpropagation (BP)-free training methods, requiring devices only to execute ``perturbed inferences''. Consequently, FwdLLM delivers way better memory efficiency and time efficiency (expedited by mobile NPUs and an expanded array of participant devices). FwdLLM centers around three key designs: (1) it combines BP-free training with parameter-efficient training methods, an essential way to scale the approach to the LLM era; (2) it systematically and adaptively allocates computational loads across devices, striking a careful balance between convergence speed and accuracy; (3) it discriminatively samples perturbed predictions that are more valuable to model convergence. Comprehensive experiments with five LLMs and three NLP tasks illustrate FwdLLM's significant advantages over conventional methods, including up to three orders of magnitude faster convergence and a 14.6x reduction in memory footprint. Uniquely, FwdLLM paves the way for federated learning of billion-parameter LLMs such as LLaMA on COTS mobile devices -- a feat previously unattained.Comment: under revie

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts

DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs

Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.Comment: Accepted by NLPCC 202