Inter-individual deep image reconstruction via hierarchical neural code conversion

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual content. In this study, we trained a method of functional alignment called neural code converter that predicts a target subject’s brain activity pattern from a source subject given the same stimulus, and analyzed the converted patterns by decoding hierarchical visual features and reconstructing perceived images. The converters were trained on fMRI responses to identical sets of natural images presented to pairs of individuals, using the voxels on the visual cortex that covers from V1 through the ventral object areas without explicit labels of the visual areas. We decoded the converted brain activity patterns into the hierarchical visual features of a deep neural network using decoders pre-trained on the target subject and then reconstructed images via the decoded features. Without explicit information about the visual cortical hierarchy, the converters automatically learned the correspondence between visual areas of the same levels. Deep neural network feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The visual images were reconstructed with recognizable silhouettes of objects even with relatively small numbers of data for converter training. The decoders trained on pooled data from multiple individuals through conversions led to a slight improvement over those trained on a single individual. These results demonstrate that the hierarchical and fine-grained representation can be converted by functional alignment, while preserving sufficient visual information to enable inter-individual visual image reconstruction

HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation

Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component is integrated with Reinforcement Learning (RL) to guide the control policy to produce behaviors aligned with the data distribution of mixed reference motions. Additionally, we employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1 distance with a novel soft boundary constraint to stabilize the training process and prevent model collapse. Our system is evaluated on a full-sized humanoid JAXON in the simulator. The resulting control policy demonstrates a wide range of locomotion patterns, including standing, push-recovery, squat walking, human-like straight-leg walking, and dynamic running. Notably, even in the absence of transition motions in the demonstration dataset, robots showcase an emerging ability to transit naturally between distinct locomotion patterns as desired speed changes

Versatile Multilinked Aerial Robot with Tilting Propellers: Design, Modeling, Control and State Estimation for Autonomous Flight and Manipulation

Multilinked aerial robot is one of the state-of-the-art works in aerial robotics, which demonstrates the deformability benefiting both maneuvering and manipulation. However, the performance in outdoor physical world has not yet been evaluated because of the weakness in the controllability and the lack of the state estimation for autonomous flight. Thus we adopt tilting propellers to enhance the controllability. The related design, modeling and control method are developed in this work to enable the stable hovering and deformation. Furthermore, the state estimation which involves the time synchronization between sensors and the multilinked kinematics is also presented in this work to enable the fully autonomous flight in the outdoor environment. Various autonomous outdoor experiments, including the fast maneuvering for interception with target, object grasping for delivery, and blanket manipulation for firefighting are performed to evaluate the feasibility and versatility of the proposed robot platform. To the best of our knowledge, this is the first study for the multilinked aerial robot to achieve the fully autonomous flight and the manipulation task in outdoor environment. We also applied our platform in all challenges of the 2020 Mohammed Bin Zayed International Robotics Competition, and ranked third place in Challenge 1 and sixth place in Challenge 3 internationally, demonstrating the reliable flight performance in the fields

Administering asylum seekers in Hong Kong : government policies and action

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Type I IFN induces protein ISGylation to enhance cytokine expression and augments colonic inflammation

Type I IFNs have broad activity in tissue inflammation and malignant progression that depends on the expression of IFN-stimulated genes (ISGs). ISG15, one such ISG, can form covalent conjugates to many cellular proteins, a process termed "protein ISGylation." Although type I IFNs are involved in multiple inflammatory disorders, the role of protein ISGylation during inflammation has not been evaluated. Here we report that protein ISGylation exacerbates intestinal inflammation and colitis-associated colon cancer in mice. Mechanistically, we demonstrate that protein ISGylation negatively regulates the ubiquitin-proteasome system, leading to increased production of IFN-induced reactive oxygen species (ROS). The increased cellular ROS then enhances LPS-induced activation of p38 MAP kinase and the expression of inflammation-related cytokines in macrophages. Thus our studies reveal a regulatory role for protein ISGylation in colonic inflammation and its related malignant progression, indicating that targeting ubiquitin-activating enzyme E1 homolog has therapeutic potential in treating inflammatory diseases

Statistical Correlation between the Distribution of Lyα Emitters and Intergalactic Medium Hi at z∼2.2 Mapped by the Subaru/Hyper Suprime-Cam

The correlation between neutral Hydrogen (HI) in the intergalactic medium (IGM) and galaxies now attracts great interests. We select four fields which include several coherently strong Ly$\alpha$ absorption systems at $z\sim2.2$ detected by using background quasars from the whole SDSS/(e)BOSS database. Deep narrow-band and $g$-band imaging are performed using the Hyper Suprime-Cam on the Subaru Telescope. We select out 2,642 Ly$\alpha$ emitter (LAE) candidates at $z=2.177\pm0.023$ down to the Ly$\alpha$ luminosity of $L_{\text{Ly}\alpha}\approx 2 \times 10^{42} {\rm erg~s}^{-1}$ to construct the galaxy overdensity maps, covering an effective area of 5.39 deg$^2$. Combining the sample with the Ly$\alpha$ absorption estimated from 64 (e)BOSS quasar spectra, we find a moderate to strong correlation between the LAE overdensity $\delta_{\rm LAE}$ and the effective optical depth $\tau_{\rm LoS}$ in line-of-sights, with $P$-value$=0.09\%$ ($<0.01\%$) when the field that contains a significant quasar overdensity is in(ex)cluded. The cross-correlation analysis also clearly suggests that up to $4\pm1$ pMpc, LAEs tend to cluster in the regions rich in HI gas, indicated by the high $\tau_{\rm LoS}$, and avoid the low $\tau_{\rm LoS}$ region where the HI gas is deficient. By averaging the $\tau_{\rm LoS}$ as a function of the projected distance ($d$) to LAEs, we find a $30\%$ excess signal at $2\sigma$ level at $d<200$ pkpc, indicating the dense HI in circumgalactic medium, and a tentative excess at $400<d<600$ pkpc in IGM regime, corroborating the cross-correlation signal detected at about $0.5$ pMpc. These statistical analyses indicate that galaxy$-$IGM HI correlations exist on scales ranging from several hundred pkpc to several pMpc at $z\sim2.2$.Comment: 27 pages, 15 figures; Resubmitted to ApJ after the first referee's report. Comments are welcom

Snowball: Another View on Side-Channel Key Recovery Tools

The performance of Side-Channel Attacks (SCAs) decays rapidly when considering more sub-keys, making the full-key recovery a very challenging problem. Limited to independent collision information utilization, collision attacks establish the relationship among sub-keys but do not significantly slow down this trend. To solve it, we first exploit the samples from the previously attacked S-boxes to assist attacks on the targeted S-box under an assumption that similar leakage occurs in program loop or code reuse scenarios. The later considered S-boxes are easier to be recovered since more samples participate in this assist attack, which results in the ``snowball\u27\u27 effect. We name this scheme as Snowball, which significantly slows down the attenuation rate of attack performance. We further introduce confusion coefficient into the collision attack to construct collision confusion coefficient, and deduce its relationship with correlation coefficient. Based on this relationship, we give two optimizations on our Snowball exploiting the ``values\u27\u27 information and ``rankings\u27\u27 information of collision correlation coefficients named Least Deviation from Pearson correlation coefficient (PLD) and Least Deviation from confusion coefficient (CLD). Experiments show that the above optimizations significantly improve the performance of our Snowball