Miracle-dz/UniFAR-FTUs huggingface dataset:
ControlVAE

In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the generation of realistic human behaviors by sampling in the latent space and allows high-level control policies to reuse the learned skills to accomplish a variety of downstream tasks. In the training of ControlVAE, we employ a learnable world model to realize direct supervision of the latent space and the control policy. This world model effectively captures the unknown dynamics of the simulation system, enabling efficient model-based learning of high-level downstream tasks. We also learn a state-conditional prior distribution in the VAE-based generative control policy, which generates a skill embedding that outperforms the non-conditional priors in downstream tasks. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.
FedProp: Cross-client Label Propagation for Federated Semi-supervised Learning

Federated learning (FL) allows multiple clients to jointly train a machine learning model in such a way that no client has to share their data with any other participating party. In the supervised setting, where all client data is fully labeled, FL has been widely adopted for learning tasks that require data privacy. However, it is an ongoing research question how to best perform federated learning in a semi-supervised setting, where the clients possess data that is only partially labeled or even completely unlabeled. In this work, we propose a new method, FedProp , that follows a manifold-based approach to semi-supervised learning (SSL). It estimates the data manifold jointly from the data of multiple clients and computes pseudo-labels using cross-client label propagation. To avoid that clients have to share their data with anyone, FedProp employs two cryptographically secure yet highly efﬁcient protocols: secure Hamming distance computation and secure summation . Experiments on three standard benchmarks show that FedProp achieves higher classiﬁcation accuracy than previous federated SSL methods. Furthermore, as a pseudo-label-based technique, FedProp is complementary to other federated SSL approaches, in particular consistency-based ones. We demonstrate experimentally that further accuracy gains are possible by combining both.
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation

This paper addresses issues on cough-based COVID-19 detection. We propose a cross-dataset transfer learning approach to improve the performance of COVID-19 detection by incorporating cough detection, cough segmentation, and data augmentation. The first aimed at removing non-cough signals and cough signals with low probability. The second aimed at segregating several coughs in a waveform into individual coughs. The third aimed at increasing the number of samples for the deep learning model. These three processing blocks are important as our finding revealed a large margin of improvement relative to the baseline methods without these blocks. An ablation study is conducted to optimize hyperparameters and it was found that alpha mixup is an important factor among others in improving the model performance via this augmentation method. A summary of this study with previous studies on the same evaluation set was given to gain insights into different methods of cough-based COVID-19 detection.
Towards Theoretically Inspired Neural Initialization Optimization

Automated machine learning has been widely explored to reduce human efforts in designing neural architectures and looking for proper hyperparameters. In the domain of neural initialization, however, similar automated techniques have rarely been studied. Most existing initialization methods are handcrafted and highly dependent on specific architectures. In this paper, we propose a differentiable quantity, named GradCosine, with theoretical insights to evaluate the initial state of a neural network. Specifically, GradCosine is the cosine similarity of sample-wise gradients with respect to the initialized parameters. By analyzing the sample-wise optimization landscape, we show that both the training and test performance of a network can be improved by maximizing GradCosine under gradient norm constraint. Based on this observation, we further propose the neural initialization optimization (NIO) algorithm. Generalized from the sample-wise analysis into the real batch setting, NIO is able to automatically look for a better initialization with negligible cost compared with the training time. With NIO, we improve the classification performance of a variety of neural architectures on CIFAR-10, CIFAR-100, and ImageNet. Moreover, we find that our method can even help to train large vision Transformer architecture without warmup.
GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods

We revisit the performance of the classic gradual magnitude pruning (GMP) baseline for large language models, focusing on the classic BERT benchmark on various popular tasks. Despite existing evidence in the literature that GMP performs poorly, we show that a simple and general variant, which we call GMP*, can match and sometimes outperform more complex state-of-the-art methods. Our results provide a simple yet strong baseline for future work, highlight the importance of parameter tuning for baselines, and even improve the performance of the state-of-the-art second-order pruning method in this setting.
Indoor Localization With Robust Global Channel Charting: A Time-Distance-Based Approach

Fingerprinting (FP) significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. Recently, channel charting (CC) has been investigated because it is an unsupervised method that does not need labeled measurement data. CC uses pairwise-distance metrics to estimate the physical distance between channel-state-information (CSI) measurements in the proximity. CC can then learn the underlying manifold of the channel measurements. While CC has shown promising results in modelling the local geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending. We therefore contribute a novel distance metric for CC that approaches a global linear correlation to the physical distance based on time-synchronized single-input/single-output (SISO) CSIs. This allows to learn the environment’s global geometry in a channel chart without annotations. We leverage a Siamese network, which enables CC-assisted FP only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different real-world data sets recorded with a 5G and UWB radio setup. Unlike FP-based localization which needs a large number of labeled data points to achieve a superior localization accuracy, our approach outperforms FP when only a few labeled data samples are available.
Reconfinement of highly magnetized jets: implications for HST-1 in M87

Stationary features are occasionally observed in AGN jets. A notable example is the HST-1 knot in M87. Such features are commonly interpreted as re-confinement shocks in hydrodynamic jets or focusing nozzles in Poynting jets. In this paper we compute the structure and Lorentz factor of a highly magnetized jet confined by external pressure having a profile that flattens abruptly at some radius. We find the development of strong oscillations upon transition from the steeper to the flatter pressure profile medium. Analytic formula is derived for the location of the nodes of these oscillations. We apply the model to the M87 jet and show that if the jet remains magnetically dominated up to sub-kpc scales, then focusing is expected. The location of the HST-1 knot can be reconciled with recent measurements of the pressure profile around the Bondi radius if the jet luminosity satisfies $L_j\simeq10^{43}$ erg/s. However, we find that magnetic domination at the collimation break implies a Lorentz factor in excess of $10^2$, atypical to FRI sources. A much lower value of the asymptotic Lorentz factor would require substantial loading close to the black hole. In that case HST-1 may be associated with a collimation nozzle of a hydrodynamic flow.
Reinforcement Learning with Automated Auxiliary Loss Search

A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely heavily on expert knowledge and may be sub-optimal. In this paper, we propose a principled and universal method for learning better representations with auxiliary loss functions, named Automated Auxiliary Loss Search (A2LS), which automatically searches for top-performing auxiliary loss functions for RL. Specifically, based on the collected trajectory data, we define a general auxiliary loss space of size $7.5 \times 10^{20}$ and explore the space with an efficient evolutionary search strategy. Empirical results show that the discovered auxiliary loss (namely, A2-winner) significantly improves the performance on both high-dimensional (image) and low-dimensional (vector) unseen tasks with much higher efficiency, showing promising generalization ability to different settings and even different benchmark domains. We conduct a statistical analysis to reveal the relations between patterns of auxiliary losses and RL performance.
Deep Learning for Iris Recognition: A Survey

ABSTRACT In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition.
Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding

Knowledge graph (KG) embedding is widely used in many downstream applications using KGs. Generally, since KGs contain only ground truth triples, it is necessary to construct arbitrary negative samples for representation learning of KGs. Recently, various methods for sampling high-quality negatives have been studied because the quality of negative triples has great effect on KG embedding. In this paper, we propose a novel method called Entity Aware Negative Sampling (EANS), which is able to sample negative entities resemble to positive one by adopting Gaussian distribution to the aligned entity index space. Additionally, we introduce auxiliary loss for false negative prediction that can alleviate the impact of the sampled false negative triples. The proposed method can generate high-quality negative samples regardless of negative sample size and effectively mitigate the influence of false negative samples. The experimental results on standard benchmarks show that our EANS outperforms existing the state-of-the-art methods of negative sampling on several knowledge graph embedding models. Moreover, the proposed method achieves competitive performance even when the number of negative samples is limited to only one.
Volume preserving Gauss curvature flow of convex hypersurfaces in the hyperbolic space

We consider the volume preserving flow of smooth, closed and convex hypersurfaces in the hyperbolic space $\mathbb{H}^{n+1} (n\geq 2)$ with the speed given by arbitrary positive power $\alpha$ of the Gauss curvature. We prove that if the initial hypersurface is convex, then the smooth solution of the flow remains convex and exists for all positive time $t\in [0,\infty)$. Moreover, we apply a result of Kohlmann which characterises the geodesic ball using the hyperbolic curvature measures and an argument of Alexandrov reflection to prove that the flow converges to a geodesic sphere exponentially in the smooth topology. This can be viewed as the first result for non-local type volume preserving curvature flows for hypersurfaces in the hyperbolic space with only convexity required on the initial data.
Transmission of high-definition video signals underwater using surface electromagnetic waves

A portable radio communication system operating in the 30 MHz band and capable of transmitting high-definition live underwater video images is presented. The system operation is based on launching electromagnetic surface waves propagating along water-air interface using specially designed surface wave antennas. Since the propagation length of the surface electromagnetic waves far exceeds the skin depth of bulk radio waves at the same frequency, this technique is useful for video communication underwater over distances of several meters. Also, this system appears to be efficient at communicating through the water-air interface.
Two Conjectures on the Largest Minimum Distances of Binary Self-Orthogonal Codes With Dimension 5

The purpose of this paper is to solve the two conjectures on the largest minimum distance <inline-formula> <tex-math notation="LaTeX">$d_{so}(n,5)$ </tex-math></inline-formula> of a binary self-orthogonal <inline-formula> <tex-math notation="LaTeX">$[n, 5]$ </tex-math></inline-formula> code proposed by Kim and Choi (2022). The determination of <inline-formula> <tex-math notation="LaTeX">$d_{so}(n,k)$ </tex-math></inline-formula> has been a fundamental and difficult problem in coding theory because there are too many binary self-orthogonal codes as the dimension <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> increases. Recently, Kim et al. (2021) considered the shortest self-orthogonal embedding of a binary linear code, and many binary optimal self-orthogonal <inline-formula> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> codes were constructed for <inline-formula> <tex-math notation="LaTeX">$k=4,5$ </tex-math></inline-formula>. Kim and Choi (2022) improved some results of Kim et al. (2021) and made two conjectures on <inline-formula> <tex-math notation="LaTeX">$d_{so}(n,5)$ </tex-math></inline-formula>. In this paper, we develop a general method to determine the exact value of <inline-formula> <tex-math notation="LaTeX">$d_{so}(n,k)$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$k=5,6$ </tex-math></inline-formula> and show that the two conjectures made by Kim and Choi (2022) are true.
Decentralized Planning for Car-Like Robotic Swarm in Unstructured Environments

—Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in unstructured environments. In this system, path ﬁnding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leveraged to escape from infeasible initial value’s local minima. Then spatial-temporal optimization is employed to generate a safe, smooth and dynamically feasible trajectory. During optimization, penalty is imposed on signed distance between agents to realize collision avoidance, and differential ﬂatness cooperated with limitation on front steer angle satisﬁes the non-holonomic constraints. With trajectories broadcast to the wireless network, agents are able to check and prevent from potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.
Broad-band spectral analysis of Aql X-1

We present the results of a broadband spectral study of the transient Low Mass X-ray Binary Aql X-1 observed by Suzaku and Rossi X-ray Timing Explorer satellites. The source was observed during its 2007 outburst in the High/Soft (Banana) state and in the Low/Hard (Extreme Island) state. Both the Banana state and the Extreme Island state spectra are best described by a two component model consisting of a soft multi-colour blackbody emission likely originating from the accretion disk and a harder Comptonized emission from the boundary layer. Evidence for a hard tail (extending to � 50 keV) is found during the Banana state; this further (transient) component, accounting for atleast � 1.5% of the source luminosity, is modeled by a power-law. Aql X-1 is the second Atoll source after GX 13+1 to show a high energy tail. The presence of a weak but broad Fe line provides further support for a standard accretion disk extending nearly to the neutron star surface. The input photons for the Comptonizing boundary layer could either be the disk photons or the hidden surface of the star or both. The luminosity of the boundary layer is similar to the disk luminosity in the banana state and is about six times larger in the extreme island state. The temperature of the Comptonizing boundary layer changes from � 2 keV in the banana state to � 20 keV in the extreme island state.
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition

Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. In particular, DG-GCN uses learned affinity matrices to capture dynamic graphical structures instead of relying on a prescribed one, while DG-TCN performs group-wise temporal convolutions with varying receptive fields and incorporates a dynamic joint-skeleton fusion module for adaptive multi-level temporal modeling. On a wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.
Momentum Aggregation for Private Non-convex ERM

We introduce new algorithms and convergence guarantees for privacy-preserving non-convex Empirical Risk Minimization (ERM) on smooth $d$-dimensional objectives. We develop an improved sensitivity analysis of stochastic gradient descent on smooth objectives that exploits the recurrence of examples in different epochs. By combining this new approach with recent analysis of momentum with private aggregation techniques, we provide an $(\epsilon,\delta)$-differential private algorithm that finds a gradient of norm $\tilde O\left(\frac{d^{1/3}}{(\epsilon N)^{2/3}}\right)$ in $O\left(\frac{N^{7/3}\epsilon^{4/3}}{d^{2/3}}\right)$ gradient evaluations, improving the previous best gradient bound of $\tilde O\left(\frac{d^{1/4}}{\sqrt{\epsilon N}}\right)$.
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation

We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and displacement field. Our formulation enables defining tetrahedral convolution, pooling, and upsampling operations to synthesize explicit mesh connectivity with variable topological genus. The proposed neural network layers learn deep features over each tetrahedron and learn to extract patterns within spatial regions across multiple scales. We illustrate the capabilities of our technique to encode tetrahedral meshes into a semantically meaningful latent-space which can be used for shape editing and synthesis. Our project page is at https://threedle.github. io/tetGAN/.
Liquid noble gas detectors for low energy particle physics

We review the current status of liquid noble gas radiation detectors with energy threshold in the keV range, which are of interest for direct dark matter searches, measurement of coherent neutrino scattering and other low energy particle physics experiments. Emphasis is given to the operation principles and the most important instrumentation aspects of these detectors, principally of those operated in the double-phase mode. Recent technological advances and relevant developments in photon detection and charge readout are discussed in the context of their applicability to those experiments.
Decoupled Context Processing for Context Augmented Language Modeling

Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and modularity. In this paper we examined a simple yet effective architecture for incorporating external context into language models based on decoupled Encoder Decoder architecture. We showed that such a simple architecture achieves competitive results on auto-regressive language modeling and open domain question answering tasks. We also analyzed the behavior of the proposed model which performs grounded context transfer. Finally we discussed the computational implications of such retrieval augmented models.
Analyzing Text Representations under Tight Annotation Budgets: Measuring Structural Alignment

Annotating large collections of textual data can be time consuming and expensive. That is why the ability to train models with limited annotation budgets is of great importance. In this context, it has been shown that under tight annotation budgets the choice of data representation is key. The goal of this paper is to better understand why this is so. With this goal in mind, we propose a metric that measures the extent to which a given representation is structurally aligned with a task. We conduct experiments on several text classification datasets testing a variety of models and representations. Using our proposed metric we show that an efficient representation for a task (i.e. one that enables learning from few samples) is a representation that induces a good alignment between latent input structure and class structure.