A Life Absolutely Bare? A Reflection on Resistance by Irregular Refugees against Fingerprinting as State Biopolitical Control in the European Union

In a legally transitory category, irregular refugees- experience a double precariousness. They risk their lives to travel across treacherous seas to Europe for a better life. However, upon the long-awaited embarkation on the European land, they are exposed once again to the precariousness of the asylum application. They are “powerless”, “with no rights” and “to be sacrificed” as Giorgio Agamben and Hannah Arendt suggested in their respective understanding of a “bare life”, la nuda vita. In light of the administrative difficulties in managing asylum application, the European Union introduced the “Dublin Agreement”, which stipulates mandatory biometric data collection for irregular refugees. However, the unprecedentedly high influx during the 2015 EU refugee crisis put the European legal structures in tension with humanitarian reasons, calling for a moment for critical analysis of refugee management as an institution. Facing Dublin Agreement’s biopolitical control, irregular refugees appear to be even more vulnerable, having no choice but to conform. Yet, in the documentary Qu’ils reposent en révolte by French film director Sylvain George, removing one’s fingerprints through self-mutilation represents an interesting ‘agency’ against the State’s control. This raises the question: is their life absolutely bare? This research paper is aimed at answering this question in a theoretical fashion. It begins by exploring the history of fingerprinting as an identification tool and by introducing the notion of a ‘bare life’. Through examining related EU Directives and member state laws, the paper first identifies conditions constituting a bare life for irregular refugees. Shifting the focus to the practice of self-mutilation as an agency for resistance, the second part of the paper examines the practical and theoretical significance of this resistance and makes recommendations with insights from psychoanalysis on returning from hostis to hospes in contemporary European refugee management

Multi-consensus Decentralized Accelerated Gradient Descent

This paper considers the decentralized optimization problem, which has applications in large scale machine learning, sensor networks, and control theory. We propose a novel algorithm that can achieve near optimal communication complexity, matching the known lower bound up to a logarithmic factor of the condition number of the problem. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Moreover, the proposed algorithm achieves the optimal computation complexity matching the lower bound up to universal constants. Furthermore, to achieve a linear convergence rate, our algorithm \emph{doesn't} require the individual functions to be (strongly) convex. Our method relies on a novel combination of known techniques including Nesterov's accelerated gradient descent, multi-consensus and gradient-tracking. The analysis is new, and may be applied to other related problems. Empirical studies demonstrate the effectiveness of our method for machine learning applications

Dynamic Self-training Framework for Graph Convolutional Networks

Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved state-of-the-art results on semi-supervised learning on graphs. However, when the number of labeled nodes is very small, the performances of GNNs downgrade dramatically. Self-training has proved to be effective for resolving this issue, however, the performance of self-trained GCN is still inferior to that of G2G and DGI for many settings. Moreover, additional model complexity make it more difficult to tune the hyper-parameters and do model selection. We argue that the power of self-training is still not fully explored for the node classification task. In this paper, we propose a unified end-to-end self-training framework called \emph{Dynamic Self-traning}, which generalizes and simplifies prior work. A simple instantiation of the framework based on GCN is provided and empirical results show that our framework outperforms all previous methods including GNNs, embedding based method and self-trained GCNs by a noticeable margin. Moreover, compared with standard self-training, hyper-parameter tuning for our framework is easier.Comment: 11page

Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes

3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstream tasks, which hinders their practicality in real-world applications. This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes. Specifically, we align 3D representations into the feature space of LLMs, thus enabling LLMs to perceive the 3D world. Given the scarcity of 3D scene-text data, we propose a three-stage training strategy to efficiently utilize the available data for better alignment. To enhance the reasoning ability and develop a user-friendly interaction scheme, we further construct a high-quality object-centric 3D instruction dataset and design an associated object-centric prompt. Our experiments show that Chat-3D achieves an impressive ability to comprehend diverse instructions for 3D scenes, engage in intricate spatial reasoning, and incorporate external knowledge into its responses. Chat-3D achieves a 75.6% relative score compared with GPT-4 on the constructed instruction dataset.Comment: The project page is \url{https://chat-3d.github.io/

Dynamical Analysis of a Parasite-Host Model within Fluctuating Environment

A parasite-host model within fluctuating environment is proposed. Firstly, the positivity and boundedness of solutions of the model within deterministic environment are discussed, and, then, the asymptotical stability and global stability of equilibria of deterministic model are investigated. Secondly, we show that the stochastic model has a unique global positive solution; furthermore, we show that the stochastic model has a stationary distribution under certain conditions. Finally, we give some numerical simulations to illustrate our analytical results

Longitudinal changes in prospective memory and their clinical correlates at 1-year follow-up in first-episode schizophrenia

This study aimed to investigate prospective memory (PM) and the association with clinical factors at 1-year follow-up in first-episode schizophrenia (FES). Thirty-two FES patients recruited from a university-affiliated psychiatric hospital in Beijing and 17 healthy community controls (HCs) were included. Time- and event-based PM (TBPM and EBPM) performances were measured with the Chinese version of the Cambridge Prospective Memory Test (CCAMPROMPT) at baseline and at one-year follow-up. A number of other neurocognitive tests were also administered. Remission was determined at the endpoint according to the PANSS score _ 3 for selected items. Repeated measures analysis of variance revealed a significant interaction between time (baseline vs. endpoint) and group (FES vs. HCs) for EBPM (F(1, 44) = 8.8, p = 0.005) and for all neurocognitive components. Paired samples ttests showed significant improvement in EBPM in FES (13.1±3.7 vs. 10.3±4.8; t = 3.065, p = 0.004), compared to HCs (15.7±3.6 vs. 16.5±2.3; t = -1.248, p = 0.230). A remission rate of 59.4% was found in the FES group. Analysis of covariance revealed that remitters performed significantly better on EBPM (14.9±2.6 vs. 10.4±3.6; F(1, 25) = 12.2, p = 0.002) than non-remitters at study endpoint. The association between EBPM and 12-month clinical improvement in FES suggests that EBPM may be a potential neurocognitive marker for the effectiveness of standard pharmacotherapy. Furthermore, the findings also imply that PM may not be strictly a trait-related endophenotype as indicated in previous studies

Extending Multi-modal Contrastive Representations

Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high dependence on large-scale, high-quality paired data and the expensive training costs limit their further development. Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for more than three modalities by integrating the knowledge of existing MCR spaces. Specifically, Ex-MCR aligns multiple existing MCRs into the same based MCR, which can effectively preserve the original semantic alignment of the based MCR. Besides, we comprehensively enhance the entire learning pipeline for aligning MCR spaces from the perspectives of training data, architecture, and learning objectives. With the preserved original modality alignment and the enhanced space alignment, Ex-MCR shows superior representation learning performance and excellent modality extensibility. To demonstrate the effectiveness of Ex-MCR, we align the MCR spaces of CLAP (audio-text) and ULIP (3D-vision) into the CLIP (vision-text), leveraging the overlapping text and image modality, respectively. Remarkably, without using any paired data, Ex-MCR learns a 3D-image-text-audio unified contrastive representation, and it achieves state-of-the-art performance on audio-visual, 3D-image, audio-text, visual-text retrieval, and 3D object classification tasks. More importantly, extensive qualitative results further demonstrate the emergent semantic alignment between the extended modalities (e.g., audio and 3D), which highlights the great potential of modality extensibility.Comment: Our code is available at https://github.com/MCR-PEFT/Ex-MC

Semiconducting transport in Pb10−x_{10-x}Cux_x(PO4_4)6_6O sintered from Pb2_2SO5_5 and Cu3_3P

The very recent claim on the discovery of ambient-pressure room-temperature superconductivity in modified lead-apatite has immediately excited sensational attention in the entire society, which is fabricated by sintering lanarkite (Pb2SO5) and copper(I) phosphide (Cu$_3$P). To verify this exciting claim, we have successfully synthesized Pb$_2$SO$_5$, Cu$_3$P, and finally the modified lead-apatite Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O. Detailed electrical transport and magnetic properties of these compounds were systematically analyzed. It turns out that Pb$_2$SO$_5$ is a highly insulating diamagnet with a room-temperature resistivity of ~7.18x10$^9$ Ohm.cm and Cu$_3$P is a paramagnetic metal with a room-temperature resistivity of ~5.22x10$^{-4}$ Ohm.cm. In contrast to the claimed superconductivity, the resulting Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O compound sintered from Pb$_2$SO$_5$ and Cu$_3$P exhibits semiconductor-like transport behavior with a large room-temperature resistivity of ~1.94x10$^4$ Ohm.cm although our compound shows greatly consistent x-ray diffraction spectrum with the previously reported structure data. In addition, when a pressed Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O pellet is located on top of a commercial Nd$_2$Fe$_{14}$B magnet at room temperature, no repulsion could be felt and no magnetic levitation was observed either. These results imply that the claim of a room-temperature superconductor in modified lead-apatite may need more careful re-examination, especially for the electrical transport properties.Comment: 12 pages, 13 figure