Secret Key Generation from Correlated Sources and Secure Link

In this paper, we study the problem of secret key generation from both correlated sources and a secure channel. We obtain the optimal secret key rate in this problem and show that the optimal scheme is to conduct secret key generation and key distribution jointly, where every bit in the secret channel will yield more than one bit of secret key rate. This joint scheme is better than the separation-based scheme, where the secure channel is used for key distribution, and as a result, every bit in the secure channel can only provide one bit of secret key rate.Comment: 5 pages, 1 figur

RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search

Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For one thing, existing methods cluster representations of all positive pairs without distinction and overlook the noise problem caused by the weak positive pairs where the text and the paired image have noise correspondences, thus leading to overfitting learning. RA offsets the overfitting risk by introducing a novel positive relation detection task (i.e., learning to distinguish strong and weak positive pairs). For another thing, learning invariant representation under data augmentation (i.e., being insensitive to some transformations) is a general practice for improving representation's robustness in existing methods. Beyond that, we encourage the representation to perceive the sensitive transformation by SA (i.e., learning to detect the replaced words), thus promoting the representation's robustness. Experiments demonstrate that RaSa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. Code is available at: https://github.com/Flame-Chasers/RaSa.Comment: Accepted by IJCAI 2023. Code is available at https://github.com/Flame-Chasers/RaS

Supervised coordinate descent method with a 3D bilinear model for face alignment and tracking

Face alignment and tracking play important roles in facial performance capture. Existing data-driven methods for monocular videos suffer from large variations of pose and expression. In this paper, we propose an efficient and robust method for this task by introducing a novel supervised coordinate descent method with 3D bilinear representation. Instead of learning the mapping between the whole parameters and image features directly with a cascaded regression framework in current methods, we learn individual sets of parameters mappings separately step by step by a coordinate descent mean. Because different parameters make different contributions to the displacement of facial landmarks, our method is more discriminative to current whole-parameter cascaded regression methods. Benefiting from a 3D bilinear model learned from public databases, the proposed method can handle the head pose changes and extreme expressions out of plane better than other 2D-based methods. We present the reliable result of face tracking under various head poses and facial expressions on challenging video sequences collected online. The experimental results show that our method outperforms state-of-art data-driven methods

Characteristics of attentional bias in adolescents with major depressive disorders: differentiating the impact of anxious distress specifier

BackgroundNo consistent conclusion has been reached regarding the attentional bias characteristics of adolescents with major depressive disorders (MDD), and unexamined co-occurring anxiety distress may contribute to this inconsistency.MethodsWe enrolled 50 MDD adolescents with anxiety distress, 47 MDD adolescents without anxiety distress and 48 healthy adolescents. We measured attentional bias using a point-probe paradigm during a negative-neutral emotional face task. Reaction time, correct response rate and attentional bias value were measured.ResultsMDD adolescents did not show a negative attentional bias; MDD adolescents with anxiety distress exhibited longer reaction time for negative and neutral stimuli, lower correct response rate for negative stimuli. Hamilton Anxiety Scale scores were positively correlated with reaction time, negatively correlated with correct response rate, and not significantly correlated with attentional bias value.LimitationsThe cross-sectional design hinders causal attribution, and positive emotional faces were not included in our paradigm.ConclusionNegative attentional bias is not a stable cognitive trait in adolescents with MDD, and avoidance or difficulty in disengaging attention from negative emotional stimuli may be the attentional bias characteristic of MDD adolescents with anxiety distress