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In this article, we study the challenging cross-modal image retrieval task, Composed Query-Based Image Retrieval (CQBIR), in which the query is not a single text query but a composed query, i.e., a reference image, and a modification text. Compared with the conventional cross-modal image-text retrieval task, the CQBIR is more challenging as it requires properly preserving and modifying the specific image region according to the multi-level semantic information learned from the multi-modal query. Most recent works focus on extracting preserved and modified information and compositing it into a unified representation. However, we observe that the preserved regions learned by the existing methods contain redundant modified information, inevitably degrading the overall retrieval performance. To this end, we propose a novel method termed Cross-Modal Attention Preservation (CMAP). Specifically, we first leverage the cross-level interaction to fully account for multi-granular semantic information, which aims to supplement the high-level semantics for effective image retrieval. Furthermore, different from conventional contrastive learning, our method introduces self-contrastive learning into learning preserved information, to prevent the model from confusing the attention for the preserved part with the modified part. Extensive experiments on three widely used CQBIR datasets, i.e., FashionIQ, Shoes, and Fashion200k, demonstrate that our proposed CMAP method significantly outperforms the current state-of-the-art methods on all the datasets. The anonymous implementation code of our CMAP method is available at https://github.com/CFM-MSG/Code_CMAP. [ABSTRACT FROM AUTHOR]

Introduction: The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space. Methods: This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly. Results: The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm. Discussion: The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI. [ABSTRACT FROM AUTHOR]

"Faster, higher, stronger" is the motto of any professional athlete. Does that apply to brain dynamics as well? In our paper, we performed a series of EEG experiments on Visually Evoked Potentials and a series of cognitive tests—reaction time and visual search, with professional eSport players in Counter-Strike: Global Offensive (CS:GO) and novices (control group) in order to find important differences between them. EEG data were studied in a temporal domain by Event-Related Potentials (ERPs) and in a frequency domain by Variational Mode Decomposition. The EEG analysis showed that the brain reaction of eSport players is faster (P300 latency is earlier on average by 20-70 ms, p < 0.005) and stronger (P300 peak amplitude is higher on average by 7-9 mkV, p < 0.01). Professional eSport players also exhibit stronger stimulus-locked alpha-band power. Besides, the Spearman correlation analysis showed a significant correlation between hours spend in CS:GO and mean amplitude of P200 and N200 for the professional players. The comparison of cognitive test results showed the superiority of the professional players to the novices in reaction time (faster) and choice reaction time—faster reaction, but similar correctness, while a significant difference in visual search skills was not detected. Thus, significant differences in EEG signals (in spectrograms and ERPs) and cognitive test results (reaction time) were detected between the professional players and the control group. Cognitive tests could be used to separate skilled players from novices, while EEG testing can help to understand the skilled player's level. The results can contribute to understanding the impact of eSport on a player's cognitive state and associating eSport with a real sport. Moreover, the presented results can be useful for evaluating eSport team members and making training plans. [ABSTRACT FROM AUTHOR]

Purpose: Adrenal gland is a common site of metastasis and on the other hand, metastases are the most frequent malignant adrenal tumors. The aim of this study was to estimate the risk of malignancy in suspicious adrenal mass in patients with a history of cancer. Methods: This is a single-center retrospective analysis of patients with adrenal tumors treated previously for different types of cancers. Between 2004 and 2021 a hundred and six such patients were identified. Mean age of patients was 62.6 years (30-78), and mean time from oncologic treatment was 55.8 months (0-274). The most common primary cancer was kidney (RCC): 29 (27.4%), colon/rectum (CRC): 20 (18.9%) and lung (NSCLC): 20 (18.9%). Results: Of 106 patients, 12 had hormonally active (HA) (11,3%) and 94 (88,7%) non active (HNA) tumors In group of patients with HA tumours 4 had hypercortisolaemia and 8 had elevation of urinary metanephrines. In the first group of HA patients pathology confirmed preoperative diagnosis of adrenocortical cancer and no metastasis was found. In all patients from the second group pheochromocytomas were confirmed. Primary (PM) and secondary (SM) malignancies were found in 50 patients (47.2%). In hormone inactive group only SM - 46/94 (48.9%) were diagnosed. The odds that adrenal lesion was a metastasis were higher if primary cancer was RCC (OR 4.29) and NSCLC (OR 12.3). Metastases were also more likely with high native tumor density, and bigger size in CT. The cut-off values for tumor size and native density calculated from receiver operating characteristic (ROC) curves were 37mm and 24, respectively. Conclusion: Risk of malignancy of adrenal mass in a patient with a history of cancer is high (47,2%), regardless of hormonal status. 47,2% risk of malignancy. In preoperative assessment type of primary cancer, adrenal tumour size and native density on CT should be taken into consideration as predictive factors of malignancy. Native density exceeding 24 HU was the strongest risk factor of adrenal malignancy (RR 3.23), followed by history of lung or renal cancer (RR 2.82) and maximum tumor diameter over 37 mm (RR 2.14). [ABSTRACT FROM AUTHOR]

Neuroendocrine neoplasms of the small intestine (SI-NENs) are one of the most commonly recognized gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). Carcinoid heart disease (CHD) is the primary cause of death in patients with the carcinoid syndrome (CS). The aim of this retrospective study was to evaluate possible factors impacting upon overall survival (OS) in subjects with both neuroendocrine tumors (NETs) G1/G2 of the small intestine (SI-NET) and CHD. Enrolled in our study of 275 patients with confirmed G1/G2 SI-NET, were 28 (10%) individuals with CHD. Overall survival was assessed using the Kaplan–Meier method. The Cox–Mantel test was used to determine how OS varied between groups. A Cox proportional hazards model was used to conduct univariate analyses of predictive factors for OS and estimate hazard ratios (HRs). Of the 28 individuals with confirmed carcinoid heart disease, 12 (43%) were found to have NET G1 and 16 (57%) were found to have NET G2. Univariate analysis revealed that subjects with CHD and without resection of the primary tumor had a lower OS. Our retrospective study observed that patients who presented with CHD and without resection of primary tumor had worse prognosis of survival. These results suggest that primary tumors may need to be removed when feasible, but further research is needed. However, no solid recommendations can be issued on the basis of our single retrospective study. [ABSTRACT FROM AUTHOR]

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The open-access BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p < 0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI. [ABSTRACT FROM AUTHOR]

Low-rank matrix/tensor decompositions are promising methods for reducing the inference time, computation, and memory consumption of deep neural networks (DNNs). This group of methods decomposes the pre-trained neural network weights through low-rank matrix/tensor decomposition and replaces the original layers with lightweight factorized layers. A main drawback of the technique is that it demands a great amount of time and effort to select the best ranks of tensor decomposition for each layer in a DNN. This paper proposes a Proxy-based Automatic tensor Rank Selection method (PARS) that utilizes a Bayesian optimization approach to find the best combination of ranks for neural network (NN) compression. We observe that the decomposition of weight tensors adversely influences the feature distribution inside the neural network and impairs the predictability of the post-compression DNN performance. Based on this finding, a novel proxy metric is proposed to deal with the abovementioned issue and to increase the quality of the rank search procedure. Experimental results show that PARS improves the results of existing decomposition methods on several representative NNs, including ResNet-18, ResNet-56, VGG-16, and AlexNet. We obtain a 3 × FLOP reduction with almost no loss of accuracy for ILSVRC-2012ResNet-18 and a 5.5 × FLOP reduction with an accuracy improvement for ILSVRC-2012 VGG-16. [ABSTRACT FROM AUTHOR]