OpenRaiser/Intern-Atlas huggingface dataset:
Making Harmful Behaviors Unlearnable for Large Language Models
Venue: arXiv cs.LG 2023
Year: 2023

Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often customized by further fine-tuning. However, the powerful learning ability of LLMs not only enables them to acquire new tasks but also makes them susceptible to learning undesired behaviors. For example, even safety-aligned LLMs can be easily fine-tuned into harmful assistants as the fine-tuning data often contains implicit or explicit harmful content. Can we train LLMs on harmful data without learning harmful behaviors? This paper proposes a controllable training framework that makes harmful behaviors unlearnable during the fine-tuning process. Specifically, we introduce ``security vectors'', a few new parameters that can be separated from the LLM, to ensure LLM's responses are consistent with the harmful behavior. Security vectors are activated during fine-tuning, the consistent behavior makes LLM believe that such behavior has already been learned, there is no need to further optimize for harmful data. During inference, we can deactivate security vectors to restore the LLM's normal behavior. The experimental results show that the security vectors generated by 100 harmful samples are enough to prevent LLM from learning 1000 harmful samples, while preserving the ability to learn other useful information.
Feature Attribution Explanations for Spiking Neural Networks
Venue: arXiv cs.NE 2023
Year: 2023

Third-generation artificial neural networks, Spiking Neural Networks (SNNs), can be efficiently implemented on hardware. Their implementation on neuromorphic chips opens a broad range of applications, such as machine learning-based autonomous control and intelligent biomedical devices. In critical applications, however, insight into the reasoning of SNNs is important, thus SNNs need to be equipped with the ability to explain how decisions are reached. We present \textit{Temporal Spike Attribution} (TSA), a local explanation method for SNNs. To compute the explanation, we aggregate all information available in model-internal variables: spike times and model weights. We evaluate TSA on artificial and real-world time series data and measure explanation quality w.r.t. multiple quantitative criteria. We find that TSA correctly identifies a small subset of input features relevant to the decision (i.e., is output-complete and compact) and generates similar explanations for similar inputs (i.e., is continuous). Further, our experiments show that incorporating the notion of \emph{absent} spikes improves explanation quality. Our work can serve as a starting point for explainable SNNs, with future implementations on hardware yielding not only predictions but also explanations in a broad range of application scenarios. Source code is available at https://github.com/ElisaNguyen/tsa-explanations.
Static Virus Spread Algorithm for DNA Sequence Design
Venue: arXiv cs.ET 2023
Year: 2023

DNA is not only the genetic material of life, but also a favorable material for a new computing model. Various research works based on DNA computing have been carried out in recent years. DNA sequence design is the foundation of such research. The sequence quality directly affects the universality, robustness, and stability of DNA computing. How to design DNA sequences depends on the biological properties and target requirements, which is a typical combinatorial optimization problem. In this paper, in order to design DNA sequences with high-quality, we propose a novel meta-heuristic evolutionary algorithm, termed the static virus spread algorithm (SVS). Through this algorithm, we focus on the constraints of universal DNA sequence design and produce a large number of DNA sequences with non-complementarity and small difference in melting temperature as the objectives, and fully considering the balanced proportion of the four bases. The computer simulation and polyacrylamide gel electrophoresis experiments show that the high-quality DNA sequences designed by this algorithm are effective, which is expected to provide a convenient tool for sequence preparation before DNA biochemical operations.
Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval
Venue: arXiv cs.CV 2023
Year: 2023

Fashion stylists have historically bridged the gap between consumers' desires and perfect outfits, which involve intricate combinations of colors, patterns, and materials. Although recent advancements in fashion recommendation systems have made strides in outfit compatibility prediction and complementary item retrieval, these systems rely heavily on pre-selected customer choices. Therefore, we introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions given by users. Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation based on textual input. Here, we leverage strategies similar to those in the contrastive language-image pretraining model to address the intricate-style matrix within the outfit sets. Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks, solidifying its effectiveness in the fashion recommendation domain. This research not only pioneers a new facet of fashion recommendation systems, but also introduces a method that captures the essence of individual style preferences through textual descriptions.
A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics
Venue: arXiv cs.LG 2023
Year: 2023

In recent years, tasks of machine learning ranging from image processing & audio/video analysis to natural language understanding have been transformed by deep learning. The data content in all these scenarios are expressed via Euclidean space. However, a considerable amount of application data is structured in non-Euclidean space and is expressed as graphs, e.g. dealing with complicated interactions & object interdependencies. Modelling physical systems, learning molecular signatures, identifying protein interactions and predicting diseases involve utilising a model that can adapt from graph data. Graph neural networks (GNNs), specified as artificial-neural models, employ message transmission between graph nodes to represent graph dependencies and are primarily used in the non-Euclidean domain. Variants of GNN like Graph Recurrent Networks (GRN), Graph Auto Encoder (GAE), Graph Convolution Networks (GCN), Graph Adversarial Methods & Graph Reinforcement learning have exhibited breakthrough productivity on a wide range of tasks, especially in the field of bioinformatics, in recent years as a result of the rapid collection of biological network data. Apart from presenting all existing GNN models, mathematical analysis and comparison of the variants of all types of GNN have been highlighted in this survey. Graph neural networks are investigated for their potential real-world applications in various fields, focusing on Bioinformatics. Furthermore, resources for evaluating graph neural network models and accessing open-source code & benchmark data sets are included. Ultimately, we provide some (seven) proposals for future research in this rapidly evolving domain. GNNs have the potential to be an excellent tool for solving a wide range of biological challenges in bioinformatics research, as they are best represented as connected complex graphs.
Resource savings from fault-tolerant circuit design
Venue: arXiv cs.CE 2023
Year: 2023

Using fault-tolerant constructions, computations performed with unreliable components can simulate their noiseless counterparts though the introduction of a modest amount of redundancy. Given the modest overhead required to achieve fault-tolerance, and the fact that increasing the reliability of basic components often comes at a cost, are there situations where fault-tolerance may be more economical? We present a general framework to account for this overhead cost in order to effectively compare fault-tolerant to non-fault-tolerant approaches for computation, in the limit of small logical error rates. Using this detailed accounting, we determine explicit boundaries at which fault-tolerant designs become more efficient than designs that achieve comparable reliability through direct consumption of resources. We find that the fault-tolerant construction is always preferred in the limit of high reliability in cases where the resources required to construct a basic unit grows faster than $\log(1 / ε)$ asymptotically for small $ε$.
Fast and Accurate Approximations of the Optimal Transport in Semi-Discrete and Discrete Settings
Venue: arXiv cs.CG 2023
Year: 2023

Given a $d$-dimensional continuous (resp. discrete) probability distribution $μ$ and a discrete distribution $ν$, the semi-discrete (resp. discrete) Optimal Transport (OT) problem asks for computing a minimum-cost plan to transport mass from $μ$ to $ν$; we assume $n$ to be the size of the support of the discrete distributions, and we assume we have access to an oracle outputting the mass of $μ$ inside a constant-complexity region in $O(1)$ time. In this paper, we present three approximation algorithms for the OT problem. (i) Semi-discrete additive approximation: For any $ε>0$, we present an algorithm that computes a semi-discrete transport plan with $ε$-additive error in $n^{O(d)}\log\frac{C_{\max}}ε$ time; here, $C_{\max}$ is the diameter of the supports of $μ$ and $ν$. (ii) Semi-discrete relative approximation: For any $ε>0$, we present an algorithm that computes a $(1+ε)$-approximate semi-discrete transport plan in $nε^{-O(d)}\log(n)\log^{O(d)}(\log n)$ time; here, we assume the ground distance is any $L_p$ norm. (iii) Discrete relative approximation: For any $ε>0$, we present a Monte-Carlo $(1+ε)$-approximation algorithm that computes a transport plan under any $L_p$ norm in $nε^{-O(d)}\log(n)\log^{O(d)}(\log n)$ time; here, we assume that the spread of the supports of $μ$ and $ν$ is polynomially bounded.
Elastic energy storage of spring-driven jumping robots
Venue: arXiv cs.RO 2023
Year: 2023

Spring-driven jumping robots use an energised spring for propulsion, while the onboard motor only serves as a spring-charging source. A common mechanism in designing these robots is the rhomboidal linkage, which has been combined with linear springs (spring-linkage) to create a nonlinear spring, thereby increasing elastic energy storage and jump height for a given motor force. The effectiveness of this spring-linkage has been examined for individual designs, but a generalised design theory of this class of system remains absent. This paper presents an energetics analysis of the spring-linkage and provides insight into designing an ideal constant force spring, which stores the maximum energy for a given motor force. A quasi-static analysis shows that the force-displacement relationship of the spring-linkage changes with the orientation and type of the spring, but is independent of the linkage scale. Combining different types and orientations of springs within the linkage enables higher elastic energy storage than using single springs. Placing two translational springs at the diagonals of the rhomboidal linkage creates an ideal spring that could increase the jump height of prior robots by 50-160%.
Linear difference operators with sequence coefficients having infinite-dimentional solution spaces
Venue: arXiv cs.SC 2023
Year: 2023

The notion of lacunary infinite numerical sequence is introduced. It is shown that for an arbitrary linear difference operator L with coefficients belonging to the set R of infinite numerical sequences, a criterion (i.e., a necessary and sufficient condition) for the infinite dimensionality of its space $V_L$ of solutions belonging to R is the presence of a lacunary sequence in $V_L$.
Comparative Knowledge Distillation
Venue: arXiv cs.LG 2023
Year: 2023

In the era of large scale pretrained models, Knowledge Distillation (KD) serves an important role in transferring the wisdom of computationally heavy teacher models to lightweight, efficient student models while preserving performance. Traditional KD paradigms, however, assume readily available access to teacher models for frequent inference -- a notion increasingly at odds with the realities of costly, often proprietary, large scale models. Addressing this gap, our paper considers how to minimize the dependency on teacher model inferences in KD in a setting we term Few Teacher Inference Knowledge Distillation (FTI KD). We observe that prevalent KD techniques and state of the art data augmentation strategies fall short in this constrained setting. Drawing inspiration from educational principles that emphasize learning through comparison, we propose Comparative Knowledge Distillation (CKD), which encourages student models to understand the nuanced differences in a teacher model's interpretations of samples. Critically, CKD provides additional learning signals to the student without making additional teacher calls. We also extend the principle of CKD to groups of samples, enabling even more efficient learning from limited teacher calls. Empirical evaluation across varied experimental settings indicates that CKD consistently outperforms state of the art data augmentation and KD techniques.
STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
Venue: arXiv cs.RO 2023
Year: 2023

Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here, robots must handle object rearrangement, including shifting, removal, and partial occlusion by new items, and track these items after substantial temporal gaps. The task is further complicated when robots encounter objects not learned in their training sets, which requires the ability to segment and track previously unseen items. Considering that continuous observation is often inaccessible in such settings, our task involves working with a discrete set of frames separated by indefinite periods during which substantial changes to the scene may occur. This task also translates to domestic robotic applications, such as rearrangement of objects on a table. To address these demanding challenges, we introduce new synthetic and real-world datasets that replicate these industrial and household scenarios. We also propose a novel paradigm for joint segmentation and tracking in discrete frames along with a transformer module that facilitates efficient inter-frame communication. The experiments we conduct show that our approach significantly outperforms recent methods. For additional results and videos, please visit \href{https://sites.google.com/view/stow-corl23}{website}. Code and dataset will be released.
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Venue: arXiv cs.RO 2023
Year: 2023

Reinforcement Learning (RL) plays an important role in the robotic manipulation domain since it allows self-learning from trial-and-error interactions with the environment. Still, sample efficiency and reward specification seriously limit its potential. One possible solution involves learning from expert guidance. However, obtaining a human expert is impractical due to the high cost of supervising an RL agent, and developing an automatic supervisor is a challenging endeavor. Large Language Models (LLMs) demonstrate remarkable abilities to provide human-like feedback on user inputs in natural language. Nevertheless, they are not designed to directly control low-level robotic motions, as their pretraining is based on vast internet data rather than specific robotics data. In this paper, we introduce the Lafite-RL (Language agent feedback interactive Reinforcement Learning) framework, which enables RL agents to learn robotic tasks efficiently by taking advantage of LLMs' timely feedback. Our experiments conducted on RLBench tasks illustrate that, with simple prompt design in natural language, the Lafite-RL agent exhibits improved learning capabilities when guided by an LLM. It outperforms the baseline in terms of both learning efficiency and success rate, underscoring the efficacy of the rewards provided by an LLM.
Ultra-Long Sequence Distributed Transformer
Venue: arXiv cs.DC 2023
Year: 2023

Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due to the overwhelming computation and memory requirements. Existing methods for long sequence training offer limited speedup and memory reduction, and may compromise accuracy. This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences. It distributes a long sequence into segments among GPUs, with each GPU computing a partial self-attention for its segment. Then, it uses a fused communication and a novel double gradient averaging technique to avoid the need to aggregate partial self-attention and minimize communication overhead. We evaluated the performance between LSS Transformer and the state-of-the-art Nvidia sequence parallelism on a Wikipedia enwik8 dataset. Results show that our proposed method lead to 5.6x faster and 10.2x more memory-efficient implementation compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs. Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456 GPUs, achieving 161% super-linear parallel efficiency and a throughput of 32 petaflops.
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
Venue: arXiv cs.CV 2023
Year: 2023

We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields. To this end, we designed and built a custom multi-camera rig to densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in unprecedented quality and density. We extend instant neural graphics primitives with a novel perceptual color space for learning accurate HDR appearance, and an efficient mip-mapping mechanism for level-of-detail rendering with anti-aliasing, while carefully optimizing the trade-off between quality and speed. Our multi-GPU renderer enables high-fidelity volume rendering of our neural radiance field model at the full VR resolution of dual 2K$\times$2K at 36 Hz on our custom demo machine. We demonstrate the quality of our results on our challenging high-fidelity datasets, and compare our method and datasets to existing baselines. We release our dataset on our project website.
3D-Aware Talking-Head Video Motion Transfer
Venue: arXiv cs.CV 2023
Year: 2023

Motion transfer of talking-head videos involves generating a new video with the appearance of a subject video and the motion pattern of a driving video. Current methodologies primarily depend on a limited number of subject images and 2D representations, thereby neglecting to fully utilize the multi-view appearance features inherent in the subject video. In this paper, we propose a novel 3D-aware talking-head video motion transfer network, Head3D, which fully exploits the subject appearance information by generating a visually-interpretable 3D canonical head from the 2D subject frames with a recurrent network. A key component of our approach is a self-supervised 3D head geometry learning module, designed to predict head poses and depth maps from 2D subject video frames. This module facilitates the estimation of a 3D head in canonical space, which can then be transformed to align with driving video frames. Additionally, we employ an attention-based fusion network to combine the background and other details from subject frames with the 3D subject head to produce the synthetic target video. Our extensive experiments on two public talking-head video datasets demonstrate that Head3D outperforms both 2D and 3D prior arts in the practical cross-identity setting, with evidence showing it can be readily adapted to the pose-controllable novel view synthesis task.
IPVNet: Learning Implicit Point-Voxel Features for Open-Surface 3D Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

Reconstruction of 3D open surfaces (e.g., non-watertight meshes) is an underexplored area of computer vision. Recent learning-based implicit techniques have removed previous barriers by enabling reconstruction in arbitrary resolutions. Yet, such approaches often rely on distinguishing between the inside and outside of a surface in order to extract a zero level set when reconstructing the target. In the case of open surfaces, this distinction often leads to artifacts such as the artificial closing of surface gaps. However, real-world data may contain intricate details defined by salient surface gaps. Implicit functions that regress an unsigned distance field have shown promise in reconstructing such open surfaces. Nonetheless, current unsigned implicit methods rely on a discretized representation of the raw data. This not only bounds the learning process to the representation's resolution, but it also introduces outliers in the reconstruction. To enable accurate reconstruction of open surfaces without introducing outliers, we propose a learning-based implicit point-voxel model (IPVNet). IPVNet predicts the unsigned distance between a surface and a query point in 3D space by leveraging both raw point cloud data and its discretized voxel counterpart. Experiments on synthetic and real-world public datasets demonstrates that IPVNet outperforms the state of the art while producing far fewer outliers in the resulting reconstruction.
Pilot-Based Key Distribution and Encryption for Secure Coherent Passive Optical Networks
Venue: arXiv cs.CR 2023
Year: 2023

The security issues of passive optical networks (PONs) have always been a concern due to broadcast transmission. Physical-layer security enhancement for the coherent PON should be as significant as improving transmission performance. In this paper, we propose the advanced encryption standard (AES) algorithm and geometric constellation shaping four-level pulse amplitude modulation (GCS-PAM4) pilot-based key distribution for secure coherent PON. The first bit of the GCS-PAM4 pilot is used for the hardware-efficient carrier phase recovery (CPR), while the second bit is utilized for key distribution without occupying the additional overhead. The key bits are encoded by the polar code to ensure error-free distribution. Frequent key updates are permitted for every codeword to improve the security of coherent PON. The experimental results of the 200-Gbps secure coherent PON using digital subcarrier multiplexing with 16-ary quadrature amplitude modulation show that the GCS-PAM4 pilot-based key distribution could be error-free at upstream transmission without occupying the additional overhead and the eavesdropping would be prevented by AES algorithm at downstream transmission. Moreover, there is almost no performance penalty on the CPR using the GCS-PAM4 pilot compared to the binary phase shift keying pilot.