69 research outputs found
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
Model-Based Control with Sparse Neural Dynamics
Learning predictive models from observations using deep neural networks
(DNNs) is a promising new approach to many real-world planning and control
problems. However, common DNNs are too unstructured for effective planning, and
current control methods typically rely on extensive sampling or local gradient
descent. In this paper, we propose a new framework for integrated model
learning and predictive control that is amenable to efficient optimization
algorithms. Specifically, we start with a ReLU neural model of the system
dynamics and, with minimal losses in prediction accuracy, we gradually sparsify
it by removing redundant neurons. This discrete sparsification process is
approximated as a continuous problem, enabling an end-to-end optimization of
both the model architecture and the weight parameters. The sparsified model is
subsequently used by a mixed-integer predictive controller, which represents
the neuron activations as binary variables and employs efficient
branch-and-bound algorithms. Our framework is applicable to a wide variety of
DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It
can efficiently handle tasks involving complicated contact dynamics, such as
object pushing, compositional object sorting, and manipulation of deformable
objects. Numerical and hardware experiments show that, despite the aggressive
sparsification, our framework can deliver better closed-loop performance than
existing state-of-the-art methods.Comment: Accepted at NeurIPS 2023. For tutorial code and additional
visualizations, see https://robopil.github.io/Sparse-Dynamics
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