Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation

Hypervolume contribution is an important concept in evolutionary multiobjective optimization (EMO). It involves hypervolume-based EMO algorithms and hypervolume subset selection algorithms. Its main drawback is that it is computationally expensive in high-dimensional spaces, which limits its applicability to many-objective optimization. Recently, an R2 indicator variant (i.e., <inline-formula> <tex-math notation="LaTeX">$R_{2}^{\text {HVC}}$ </tex-math></inline-formula> indicator) is proposed to approximate the hypervolume contribution. The <inline-formula> <tex-math notation="LaTeX">$R_{2}^{\text {HVC}}$ </tex-math></inline-formula> indicator uses line segments along a number of direction vectors for hypervolume contribution approximation. It has been shown that different direction vector sets lead to different approximation qualities. In this article, we propose learning to approximate (LtA), a direction vector set generation method for the <inline-formula> <tex-math notation="LaTeX">$R_{2}^{\text {HVC}}$ </tex-math></inline-formula> indicator. The direction vector set is automatically learned from training data. The learned direction vector set can then be used in the <inline-formula> <tex-math notation="LaTeX">$R_{2}^{\text {HVC}}$ </tex-math></inline-formula> indicator to improve its approximation quality. The usefulness of the proposed LtA method is examined by comparing it with other commonly used direction vector set generation methods for the <inline-formula> <tex-math notation="LaTeX">$R_{2}^{\text {HVC}}$ </tex-math></inline-formula> indicator. Experimental results suggest the superiority of LtA over the other methods for generating high-quality direction vector sets.
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits

We study the non-stationary stochastic multi-armed bandit problem, where the reward statistics of each arm may change several times during the course of learning. The performance of a learning algorithm is evaluated in terms of their dynamic regret, which is defined as the difference between the expected cumulative reward of an agent choosing the optimal arm in every time step and the cumulative reward of the learning algorithm. One way to measure the hardness of such environments is to consider how many times the identity of the optimal arm can change. We propose a method that achieves, in $K$-armed bandit problems, a near-optimal $\widetilde O(\sqrt{K N(S+1)})$ dynamic regret, where $N$ is the time horizon of the problem and $S$ is the number of times the identity of the optimal arm changes, without prior knowledge of $S$. Previous works for this problem obtain regret bounds that scale with the number of changes (or the amount of change) in the reward functions, which can be much larger, or assume prior knowledge of $S$ to achieve similar bounds.
The decay of N∗ (1895) to light hyperon resonances

In this talk I review the findings of our recent works where we have studied the decay of N ∗ (1895) and the implications of such properties on the photoproduction of light hyperons. I discuss that meson-baryon interactions play an essential role in describing the nature of N ∗ (1895) and report the details of our investigation of its decays to different meson-baryon systems and to final states involving Λ(1405) and a proposed Σ(1400). We find that the width of N ∗ (1895) gets important contributions from the decay to light hyperon resonances. Such an information can be used to look for alternative processes to study N ∗ (1895) in experimental data.
S-protomodularity of the category of cocommutative bialgebras

We prove that the category of cocommutative bialgebras in any symmetric monoidal category (that has equalizers) is an S-protomodular category with respect to a particular class of split extensions of cocommutative bialgebras. We also obtain the “partial” well-known Smith is Huq condition, meaning that two S-equivalence relations centralize each other as soon as the normal subobjects associated with them commute in the sense of Huq. Introduction The notion of protomodular category was introduced by Bourn in [2]. The categories of groups, rings, Lie algebras, crossed modules, rings, the dual of the category of sets are examples of protomodular categories. In the pointed case, a category C is protomodular if and only if the Split Short Five Lemma holds. This means that for any diagram of the form (0.1) A1 B1 X1 A2 B2 X2 0 0 0 0 g v p κ2 α2 e2 κ1 α1 e1 where κi is the kernel of αi and αi · ei = 1Bi for any i ∈ {1, 2}, then p is an isomorphism whenever v and g are, this property is often referred to as the “Split Short Five Lemma” holds in C. It is well-known that the category of internal groups in a finitely complete category is protomodular [2]. In particular, this result implies that the category of cocommutative Hopf algebras in a symmetric monoidal category, that has equalizers, is protomodular, seen as internal groups in the category of cocommutative coalgebras. The category of cocommutative Hopf algebras over a field is even semi-abelian [11]. In this paper, we are interested in cocommutative bialgebras in any symmetric monoidal category that has equalizers. As a matter of fact, it was proven in [10] that the category of cocommutative Kbialgebras is not a protomodular category. Similarly, the category of monoids is not a protomodular category. However, a class of split epimorphisms of monoids, called Schreier split epimorphisms, turned out to have some very interesting properties, similar to the ones of split epimorphisms of groups. For example, Schreier split epimorphisms are equivalent to the actions of monoids, the Split Short Five Lemma holds, etc. [5, 6]. The authors in [5], introduced the notion of S-protomodularity, that is the protomodularity with respect to a class S of split epimorphisms. It implies that we have the Split Short Five Lemma as in (0.1) whenever (α1, e1) and (α2, e2) are in the class S. The main example is the category of monoids with the class of Schreier split epimorphisms.
Network Coding for Cooperation in Dynamic Wireless Networks

With the growing concern on minimizing the cost of packet transmission in multi-user wireless applications, network coded cooperative communication has been proposed and evaluated as a powerful technology that can provide a better quality of service in the next generation wireless systems. Previous contributions have focused on performance evaluation of predefined scenarios rather than searching for optimal policies that can minimize the total cost of reliable packet transmission. In this thesis, we study network coded cooperation for dynamic wireless networks with the goal of finding optimal policies that can minimize the total cost of packet transmission and help us to design near optimal multi-cast protocols for dynamic networks. We start by analysing the gain of network coding protocols in terms of reducing the number of active nodes and the active area as representatives of packet transmission cost. To this end, a multicast scenario in a mesh network is considered. We seek to find a network coding protocol that can achieve the same throughput as no network coding protocols while using less number of active nodes. A new multicast geographic coding-aware communication protocol is proposed which is capable of reducing significantly both the required transmission area and the number of active nodes when compared with traditional multi-casting while achieving the same throughput. The proposed protocol is shown to have linear complexity in terms of number of receivers, compared to the traditional protocols that have non-linear complexities. We then focus on finding optimal network coded cooperative packet transmission policies that can minimize the cost of reliable packet transmission. A simple network with one source and two receivers is considered to answer the question of when is the optimal time of starting cooperation between receivers in order to minimize the total cost of packet transmission. We model this problem for time-invariant and time-varying erasure channels using a Markov decision process (MDP). Then, the packet transmission policies that are selected by the MDP are analysed to extract the optimal rules of packet transmission. We further propose four powerful heuristics that are shown to provide near-optimal performance in dynamic wireless environments. Our proposed heuristics could be used for any number of receivers. A real-world implementation of the proposed heuristics reveals that the proposed heuristics are able to reduce the mean of completion time by up to 4.75x compared with simply broadcasting coded packets. Finally, we address the effect of a relay on reducing the transmission time of data packets from a source to two receivers using network coding and in the presence of X active neighbours sharing the same channel. We show that using a relay in the presence of active neighbours is beneficial even if the channel from relay to destination is not better than the channel between source and destination.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg.
CISRNet: Compressed Image Super-Resolution Network

In recent years, tons of research has been conducted on Single Image Super-Resolution (SISR). However, to the best of our knowledge, few of these studies are mainly focused on compressed images. A problem such as complicated compression artifacts hinders the advance of this study in spite of its high practical values. To tackle this problem, we proposed CISRNet; a network that employs a two-stage coarse-to-fine learning framework that is mainly optimized for Compressed Image Super-Resolution Problem. Specifically, CISRNet consists of two main subnetworks; the coarse and refinement network, where recursive and residual learning is employed within these two networks respectively. Extensive experiments show that with a careful design choice, CISRNet performs favorably against competing Single-Image Super-Resolution methods in the Compressed Image Super-Resolution tasks.
Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?

Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose. Therefore, in this work, we use one of the most extensive datasets, BraTS 2021, to study the potency of employing deep learning solutions, including 2D and 3D CNN models and vision transformers. After conducting a thorough analysis of the models' performance, we concluded that there seems to be no connection between the MRI scans and the state of the MGMT promoter.
Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Class-conditioning offers a direct means to control a Generative Adversarial Network (GAN) based on a discrete input variable. While necessary in many applications, the additional information provided by the class labels could even be expected to benefit the training of the GAN itself. On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability. Motivated by this observation, we propose a training strategy for class-conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning. Our training strategy starts with an unconditional GAN and gradually injects the class conditioning into the generator and the objective function. The proposed method for training cGANs with limited data results not only in stable training but also in generating high-quality images, thanks to the early-stage exploitation of the shared information across classes. We analyze the observed mode collapse problem in comprehensive experiments on four datasets. Our approach demonstrates outstanding results compared with state-of-the-art methods and established baselines. The code is available at https://github.com/mshahbazi72/transitional-cGAN
Spatiotemporal Costmap Inference for MPC Via Deep Inverse Reinforcement Learning

It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from human demonstrations. We propose a new IRL algorithm that learns a goal-conditioned spatio-temporal reward function. The resulting costmap is used by Model Predictive Controllers (MPCs) to perform a task without any hand-designing or hand-tuning of the cost function. We evaluate our proposed Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL framework together with MPC in the CARLA simulator for autonomous driving, lane keeping, and lane changing tasks in a challenging dense traffic highway scenario. Our proposed methods show higher success rates compared to other baseline methods including behavior cloning, state-of-the-art RL policies, and MPC with a learning-based behavior prediction model.
Transferability in Deep Learning: A Survey

The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relevant knowledge from previous learning experiences when encountering and solving unseen tasks. Such an ability to acquire and reuse knowledge is known as transferability in deep learning. It has formed the long-term quest towards making deep learning as data-efficient as human learning, and has been motivating fruitful design of more powerful deep learning algorithms. We present this survey to connect different isolated areas in deep learning with their relation to transferability, and to provide a unified and complete view to investigating transferability through the whole lifecycle of deep learning. The survey elaborates the fundamental goals and challenges in parallel with the core principles and methods, covering recent cornerstones in deep architectures, pre-training, task adaptation and domain adaptation. This highlights unanswered questions on the appropriate objectives for learning transferable knowledge and for adapting the knowledge to new tasks and domains, avoiding catastrophic forgetting and negative transfer. Finally, we implement a benchmark and an open-source library, enabling a fair evaluation of deep learning methods in terms of transferability.
Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow

Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constraints incorporating temporal information based on motion estimation. However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them. Therefore, the synthetic motion can be applied in the regularization paradigm to keep motions consistent across domains without the risk of errors in motion estimation. Thereafter, we utilize our unsupervised recycle and unsupervised spatial loss, guided by the pseudo-supervision provided by the synthetic optical flow, to accurately enforce spatiotemporal consistency in both domains. Experiments show that our method is versatile in various scenarios and achieves state-of-the-art performance in generating temporally and semantically consistent videos. Code is available at: https://github.com/wangkaihong/Unsup_Recycle_GAN/.
FourierNet: Shape-Preserving Network for Henle's Fiber Layer Segmentation in Optical Coherence Tomography Images

Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Müller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This paper addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which estimated Fourier descriptors are used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans of healthy-looking macula reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main segmentation task leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation without performing directional OCT imaging.
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing

Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes one step further by skipping computing and communication through DNN layer freezing. Our key insight is that the training progress of internal DNN layers differs significantly, and front layers often become well-trained much earlier than deep layers. To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers. Then we design Egeria, a knowledge-guided DNN training system that employs semantic knowledge from a reference model to accurately evaluate individual layers' training plasticity and safely freeze the converged ones, saving their corresponding backward computation and communication. Our reference model is generated on the fly using quantization techniques and runs forward operations asynchronously on available CPUs to minimize the overhead. In addition, Egeria caches the intermediate outputs of the frozen layers with prefetching to further skip the forward computation. Our implementation and testbed experiments with popular vision and language models show that Egeria achieves 19%-43% training speedup w.r. t. the state-of-the-art without sacrificing accuracy.
Instant neural graphics primitives with a multiresolution hash encoding

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.
Identifying the contribution of higher $\rho$ mesons around 2 GeV in the $e^+ e^- \to \omega\pi^0$ and $e^+ e^- \to \rho \eta^{\prime}$ processes

(Dated: The properties of the light vector meson states around 2.0 GeV have been poorly understood for a long time, which has become a barrier to the expansion to higher light vector meson spectrum. Recently, the BESIII collaboration released the measurements of the e + e − → ωπ 0 and e + e − → ρη (cid:48) reactions above 2.0 GeV, both of which are ideal processes to study the isovector ρ meson family. In this work, through carrying out a combined analysis of the Born cross section data for the above two processes with the theoretical support on mass spectrum, and production and strong decay behaviors of the ρ meson family around 2.0 GeV, we identify the enhancement structure near 2034 MeV observed in e + e − → ωπ 0 to be the interference contribution from two resonances ρ (1900) and ρ (2150), and another enhancement structure at 2111 MeV reported in e + e − → ρη (cid:48) to be the contributions from ρ (2000) and ρ (2150). This conclusion means that the e + e − → ωπ 0 and e + e − → ρη (cid:48) are the excellent golden channels to establish ρ (1900) and ρ (2000), especially for a D -wave state ρ (2000), whose experimental search in the e + e − collision should be quite challenging. The relevant cross section measurements with higher precision are expected in the future BESIII and Belle II experiments.