The degree-corrected block model (DCBM), latent space model (LSM), and $\beta$-model are all popular network models. We combine their modeling ideas and propose the logit-DCBM as a new model. Similar as the $\beta$-model and LSM, the logit-DCBM contains nonlinear factors, where fitting the parameters is a challenging open problem. We resolve this problem by introducing a cancellation trick. We also propose R-SCORE as a recursive community detection algorithm, where in each iteration, we first use the idea above to update our parameter estimation, and then use the results to remove the nonlinear factors in the logit-DCBM so the renormalized model approximately satisfies a low-rank model, just like the DCBM. Our numerical study suggests that R-SCORE significantly improves over existing spectral approaches in many cases. Also, theoretically, we show that the Hamming error rate of R-SCORE is faster than that of SCORE in a specific sparse region, and is at least as fast outside this region.

Existing camouflaged object detection (COD) approaches primarily rely on RGB domain features to segment camouflaged objects. However, they face a major limitation: insensitivity to subtle differences in colors, textures and patterns, which contradicts the essence of COD that discerns imperceptible cues of camouflaged objects. To address this challenge, we propose a dual-domain collaborative learning network, which leverages frequency domain cues to collaborate with RGB domain features, therefore utilizing their unique and complementary strengths for effectively detecting camouflaged objects. Specifically, we propose the dual-domain feature integration (DFI) module, which aggregates cross-level RGB and frequency features to alleviate level-specific limitations, resulting in discriminative object features. Furthermore, we design the frequency-aware global localization (FGL) module, employing different frequency components to perceive global contexts. Additionally, we introduce the foreground-background separation learning (FSL) module to simultaneously capture semantic contexts and intricate details in both foreground and background. Extensive experimental results demonstrate the effectiveness of our network. Our results are available at https://github.com/ZhangQing0329/DCLNet

Consider a $K$-vertex simplex in a $d$-dimensional space. We measure $n$ points on the simplex, but due to the measurement noise, some of the observed points fall outside the simplex. The interest is vertex hunting (i.e., estimating the vertices of the simplex). The successive projection algorithm (SPA) is one of the most popular approaches to vertex hunting, but it is vulnerable to noise and outliers, and may perform unsatisfactorily. We propose pseudo-point SPA (pp-SPA) as a new approach to vertex hunting. The approach contains two novel ideas (a projection step and a denoise step) and generates roughly $n$ pseudo-points, which can be fed in to SPA for vertex hunting. For theory, we first derive an improved non-asymptotic bound for the orthodox SPA, and then use the result to derive the bounds for pp-SPA. Compared with the orthodox SPA, pp-SPA has a faster rate and more satisfactory numerical performance in a broad setting. The analysis is quite delicate: the non-asymptotic bound is hard to derive, and we need precise results on the extreme values of (possibly) high-dimensional random vectors.

The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., the type II error). This paradigm is suitable for applications such as severe disease diagnosis, fraud detection, among others. A series of NP classifiers have been developed to guarantee the type I error control with high probability. However, these existing classifiers involve a sample splitting step: a mixture of class 0 and class 1 observations to construct a scoring function and some left-out class 0 observations to construct a threshold. This splitting enables classifier threshold construction built upon independence, but it amounts to insufficient use of data for training and a potentially higher type II error. Leveraging a canonical linear discriminant analysis (LDA) model, we derive a quantitative CLT for a certain functional of quadratic forms of the inverse of sample and population covariance matrices, and based on this result, develop for the first time NP classifiers without splitting the training sample. Numerical experiments have confirmed the advantages of our new non-splitting parametric strategy.

The detection of MIMO signals presents a formidable computation challenge due to the inherent complexity incurred by matrix-type wireless fading channels. To reduce the hardware cost, linear MIMO detection algorithms can be used instead of non-linear space-time processing. Conventionally, adaptive linear algorithms such as RLS and LMS are treated separately in implementation from matrix inversion based algorithms such as ZF and MMSE. In this paper, we identify the similarities between the QR-decomposition based forms of RLS and MMSE MIMO detection and present a unified systolic array implementation that can accommodate both. In the meanwhile, some subtle but important differences also exist. As a result, the unified array must have a size of Nrx Nr(assuming Ntles Nr) and both array inputs and array operations need to be controlled accordingly. As special cases of RLS and MMSE, LMS and ZF can also be realized in the same array, making the array flexible enough for all linear MIMO detection algorithms.

The implementation of wideband MIMO systems poses a major challenge to hardware designers due to the huge processing power required for MIMO detection. To achieve this goal with a complete VLSI solution, channel coding and MIMO detection are preferably separated so that each of them can be fitted into a single chip. In this paper, a comparative study is presented regarding various uncoded adaptive and non-adaptive MIMO detection algorithms. Intended to serve as a reference for system designers, this comparison is performed from several different perspectives including theoretical formulation, simulated BER/PER performance, and hardware complexity. All the simulations are conducted within MIMO-OFDM framework and with a packet structure similar to that of the IEEE 802.11a/g standard. As the comparison results show, the RLS algorithm appears to be an affordable solution for a wideband MIMO system targeted at gigabit wireless transmission. As a direct result of this work, an ASIC for a 25 MHz wideband 8 /spl times/ 8 MIMO-OFDM system using RLS has been designed and fabricated.

Detection of MIMO signals presents a formidable computation challenge due to the inherent complexity incurred by matrix-type wireless fading channels. When combined with OFDM, the MIMO detection over frequency-selective fading channels is divided into that over each flat-fading subchannel. Since this is usually done on per subchannel basis, it implies significant hardware cost. This work proposes the use of linear interpolation to exploit the correlation among adjacent subchannels. Since the weight matrices on non-pilot subchannels are linearly interpolated from those of pilot subchannels, the computation overhead is greatly reduced. This is particularly effective when the subchannel bandwidth is much smaller than the coherence bandwidth of the channel. Simulation using MMSE MIMO detection shows that, by keeping the product of interpolation factor and normalized RMS delay spread within a certain limit, linear interpolation can result in minimum degradation of system performance up to a practical SNR level of 20 dB to 25 dB.