Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking IoU-based Optimization for Single-stage 3D Object Detection

Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors. Recently, several 3D object detection methods adopt IoU-based optimization and directly replace the 2D IoU with 3D IoU. However, such a direct computation in 3D is very costly due to the complex implementation and inefficient backward operations. Moreover, 3D IoU-based optimization is sub-optimal as it is sensitive to rotation and thus can cause training instability and detection performance deterioration. In this paper, we propose a novel Rotation-Decoupled IoU (RDIoU) method that can mitigate the rotation-sensitivity issue, and produce more efficient optimization objectives compared with 3D IoU during the training stage. Specifically, our RDIoU simplifies the complex interactions of regression parameters by decoupling the rotation variable as an independent term, yet preserving the geometry of 3D IoU. By incorporating RDIoU into both the regression and classification branches, the network is encouraged to learn more precise bounding boxes and concurrently overcome the misalignment issue between classification and regression. Extensive experiments on the benchmark KITTI and Waymo Open Dataset validate that our RDIoU method can bring substantial improvement for the single-stage 3D object detection.
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games. Different from prior efforts on training agents to beat a fixed set of opponents, our objective is to find the Nash equilibrium policies that are free from exploitation by even the adversarial opponents. We propose (a) Nash-DQN algorithm, which integrates the deep learning techniques from single DQN into the classic Nash Q-learning algorithm for solving tabular Markov games; (b) Nash-DQN-Exploiter algorithm, which additionally adopts an exploiter to guide the exploration of the main agent. We conduct experimental evaluation on tabular examples as well as various two-player Atari games. Our empirical results demonstrate that (i) the policies found by many existing methods including Neural Fictitious Self Play and Policy Space Response Oracle can be prone to exploitation by adversarial opponents; (ii) the output policies of our algorithms are robust to exploitation, and thus outperform existing methods.
An inquiring framework for changing project management practice: A qualitative method study

This research is designed to advance the theoretical and practical knowledge in the area of project management through the development of an inquiring, integrative project management framework. The methodological framework used in this work is based on an interpretivist philosophy using grounded theory building within a systems thinking framework. This research is based on a qualitative methodology resulting in an in depth analysis of data provided by interviews. The data collected and analysed in this research shows that project management is a very complex and multifaceted issue, context dependent and in a continual state of flux. People are a major cause of complexity and the case for an inquiring approach is introduced by a study of project management literature and supported by the results of the analysis of the data sets acquired in this work. The research is concerned with the development of an inquiring, integrative project management framework that allows for a high degree of contextualization to take place during its application to reflect the real world nature of projects. The concept of a project system is used to underpin the framework. The project system used to underpin the framework, supports the dynamic, contextual and iterative nature of project management. A comparison of the framework with the real world view of project management practitioners was carried out (validation), resulting in some refinements of the framework, increased confidence that it has practical applicability and some future directions for research. A claimed methodological contribution is the use of grounded theory for construction of new knowledge within a systems thinking framework. The inquiring, integrative project management framework developed in this work contributes to project management practice and a user guide is provided to support its application. The framework, which represents the outcome of this research, can be seen as a useful instrument to aid project management practitioners. The framework can be utilized to carry out inquiry with varying levels of detail and it offers the flexibility necessary for contextualization of projects to support decision making. Project management is treated as a human activity, where the central idea is that people and context are key to the success of the project and not, for example, the project management method.
The Differential Structure of Generators of GNS-symmetric Quantum Markov Semigroups

A BSTRACT . We show that the generator of a GNS-symmetric quantum Markov semigroup can be written as the square of a derivation. This generalizes a result of Cipriani and Sauvageot for tracially symmetric semigroups. Compared to the tracially symmetric case, the derivations in the general case satisfy a twisted product rule, reﬂecting the non-triviality of their modular group. This twist is captured by the new concept of Tomita bimodules we introduce. If the quantum Markov semigroup satisﬁes a certain additional regularity condition, the associated Tomita bimodule can be realized inside the L 2 space of a bigger von Neumann algebra, whose construction is an operator-valued version of free Araki-Woods factors.
Unified 2D and 3D Pre-Training of Molecular Representations

Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represented by a 3D conformation with 3-dimensional coordinates of all atoms. We note that most previous work handles 2D and 3D information separately, while jointly leveraging these two sources may foster a more informative representation. In this work, we explore this appealing idea and propose a new representation learning method based on a unified 2D and 3D pre-training. Atom coordinates and interatomic distances are encoded and then fused with atomic representations through graph neural networks. The model is pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D conformation generation conditioned on 2D graph, and 2D graph generation conditioned on 3D conformation. We evaluate our method on 11 downstream molecular property prediction tasks: 7 with 2D information only and 4 with both 2D and 3D information. Our method achieves state-of-the-art results on 10 tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also achieves significant improvement on two 3D conformation generation tasks.
One or Nothing: Anti-unification over the Simply-Typed Lambda Calculus

Generalization techniques have many applications, including template construction, argument generalization, and indexing. Modern interactive provers can exploit advancement in generalization methods over expressive type theories to further develop proof generalization techniques and other transformations. So far, investigations concerned with anti-unification (AU) over λ -terms and similar type theories have focused on developing algorithms for well-studied variants. These variants forbid the nesting of generalization variables, restrict the structure of their arguments, and are unitary . Extending these methods to more expressive variants is important to applications. We consider the case of nested generalization variables and show that the AU problem is nullary (using capture-avoiding substitutions), even when the arguments to free variables are severely restricted.
Extending the fair sampling assumption using causal diagrams

Discarding undesirable measurement results in Bell experiments opens the detection loophole that prevents a conclusive demonstration of nonlocality. As closing the detection loophole represents a major technical challenge for many practical Bell experiments, it is customary to assume the so-called fair sampling assumption (FSA) that, in its original form, states that the collectively postselected statistics are a fair sample of the ideal statistics. Here, we analyze the FSA from the viewpoint of causal inference: We derive a causal structure that must be present in any causal model that faithfully encapsulates the FSA. This provides an easy, intuitive, and unifying approach that includes different accepted forms of the FSA and underlines what is really assumed when using the FSA. We then show that the FSA can not only be applied in scenarios with non-ideal detectors or transmission losses, but also in ideal experiments where only parts of the correlations are postselected, e.g., when the particles' destinations are in a superposition state. Finally, we demonstrate that the FSA is also applicable in multipartite scenarios that test for (genuine) multipartite nonlocality.
Don't Stop Learning: Towards Continual Learning for the CLIP Model

The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Benefiting from its gigantic image-text training set, the CLIP model has learned outstanding capabilities in zero-shot learning and image-text matching. To boost the recognition performance of CLIP on some target visual concepts, it is often desirable to further update the CLIP model by fine-tuning some classes-of-interest on extra training data. This operation, however, raises an important concern: will the update hurt the zero-shot learning or image-text matching capability of the CLIP, i.e., the catastrophic forgetting issue? If yes, could existing continual learning algorithms be adapted to alleviate the risk of catastrophic forgetting? To answer these questions, this work conducts a systemic study on the continual learning issue of the CLIP model. We construct evaluation protocols to measure the impact of fine-tuning updates and explore different ways to upgrade existing continual learning methods to mitigate the forgetting issue of the CLIP model. Our study reveals the particular challenges of CLIP continual learning problem and lays a foundation for further researches. Moreover, we propose a new algorithm, dubbed Learning without Forgetting via Replayed Vocabulary (VR-LwF), which shows exact effectiveness for alleviating the forgetting issue of the CLIP model.
Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.
Microtearing turbulence saturation via electron temperature flattening at low-order rational surfaces

Microtearing instability is one of the major sources of turbulent transport in high-$\beta$ tokamaks. These modes lead to very localized transport at low-order rational magnetic field lines, and we show that flattening of the local electron temperature gradient at these rational surfaces plays an important role in setting the saturated flux level in microtearing turbulence. This process depends crucially on the density of rational surfaces, and thus the system-size, and gives rise to a worse-than-gyro-Bohm transport scaling for system-sizes typical of existing tokamaks and simulations.
Moment Centralization based Gradient Descent Optimizers for Convolutional Neural Networks

Convolutional neural networks (CNNs) have shown very appealing performance for many computer vision applications. The training of CNNs is generally performed using stochastic gradient descent (SGD) based optimization techniques. The adaptive momentum-based SGD optimizers are the recent trends. However, the existing optimizers are not able to maintain a zero mean in the first-order moment and struggle with optimization. In this paper, we propose a moment centralization-based SGD optimizer for CNNs. Specifically, we impose the zero mean constraints on the first-order moment explicitly. The proposed moment centralization is generic in nature and can be integrated with any of the existing adaptive momentum-based optimizers. The proposed idea is tested with three state-of-the-art optimization techniques, including Adam, Radam, and Adabelief on benchmark CIFAR10, CIFAR100, and TinyImageNet datasets for image classification. The performance of the existing optimizers is generally improved when integrated with the proposed moment centralization. Further, The results of the proposed moment centralization are also better than the existing gradient centralization. The analytical analysis using the toy example shows that the proposed method leads to a shorter and smoother optimization trajectory. The source code is made publicly available at \url{https://github.com/sumanthsadhu/MC-optimizer}.
Frequency decomposition techniques for increased discriminative 3D facial information capture

Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches.
Methods to study folding of alpha-helical membrane proteins in lipids

How alpha-helical membrane proteins fold correctly in the highly hydrophobic membrane interior is not well understood. Their folding is known to be highly influenced by the lipids within the surrounding bilayer, but the majority of folding studies have focused on detergent-solubilized protein rather than protein in a lipid environment. There are different ways to study folding in lipid bilayers, and each method has its own advantages and disadvantages. This review will discuss folding methods which can be used to study alpha-helical membrane proteins in bicelles, liposomes, nanodiscs or native membranes. These folding methods include in vitro folding methods in liposomes such as denaturant unfolding studies, and single-molecule force spectroscopy studies in bicelles, liposomes and native membranes. This review will also discuss recent advances in co-translational folding studies, which use cell-free expression with liposomes or nanodiscs or are performed in vivo with native membranes.
Quantifying the effect of human population mobility on malaria risk in the Peruvian Amazon

The impact of human population movement (HPM) on the epidemiology of vector-borne diseases, such as malaria, has been described. However, there are limited data on the use of new technologies for the study of HPM in endemic areas with difficult access such as the Amazon. In this study conducted in rural Peruvian Amazon, we used self-reported travel surveys and GPS trackers coupled with a Bayesian spatial model to quantify the role of HPM on malaria risk. By using a densely sampled population cohort, this study highlighted the elevated malaria transmission in a riverine community of the Peruvian Amazon. We also found that the high connectivity between Amazon communities for reasons such as work, trading or family plausibly sustains such transmission levels. Finally, by using multiple human mobility metrics including GPS trackers, and adapted causal inference methods we identified for the first time the effect of human mobility patterns on malaria risk in rural Peruvian Amazon. This study provides evidence of the causal effect of HPM on malaria that may help to adapt current malaria control programmes in the Amazon.
Molecular identification of trematode parasites infecting the freshwater snail Bithynia siamensis goniomphalos in Thailand

Abstract Digenetic trematodes are important parasites of humans and animals. They have complex life cycles and typically infect a gastropod as the first intermediate host. Bithynia siamensis goniomphalos, the first intermediate host of the liver fluke, Opisthorchis viverrini, harbours a wide variety of other trematode species. Morphological details of cercariae of 20 trematode taxa from B. s. goniomphalos, collected mainly in Thailand from 2009 to 2014, were provided in an earlier paper. Correct identification to the species or genus level based on morphology of these cercariae is generally not possible. Therefore, we used molecular data to improve identification and to investigate the diversity of the species of trematodes infecting B. s. goniomphalos. We were successful in extracting, amplifying and sequencing portions of the 28S ribosomal RNA (rRNA) gene for 19 of these 20 types of cercaria, and the internal transcribed spacer 2 region for 18 types. BLAST searches in GenBank and phylogenetic trees inferred from the 28S rRNA sequences identified members of at least nine superfamilies and 12 families. Only a few cercariae could be assigned confidently to genus or species on the basis of the sequence data. Matching sequence data from named adult trematodes will be required for definitive identification. There is clearly a great diversity of trematode species utilizing B. s. goniomphalos in Thailand.
Ubiquitination of SARS-CoV-2 NSP6 and ORF7a Facilitates NF-κB Activation

The detailed molecular basis of the induction of proinflammatory cytokines and chemokines by SARS-CoV-2 is unclear, although such induction is clearly related to the severity of COVID-19. Here, we show that SARS-CoV-2 NSP6 and ORF7a lead to NF-κB activation through associations with TAK1. ABSTRACT Patients with severe coronavirus disease 2019 tend to have high levels of proinflammatory cytokines, which eventually lead to cytokine storm and the development of acute respiratory distress syndrome. However, the detailed molecular mechanisms of proinflammatory cytokine production remain unknown. Here, we screened severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genes and found that nonstructural protein 6 (NSP6) and open reading frame 7a (ORF7a) activated the NF-κB pathway. NSP6 and ORF7a interacted with transforming growth factor β-activated kinase 1 (TAK1), and knockout (KO) of TAK1 or NF-κB essential modulator (NEMO) abolished NF-κB activation by NSP6 and ORF7a. Interestingly, K61 of NSP6 was conjugated to K63-linked polyubiquitin chains by the E3 ubiquitin ligase tripartite motif-containing 13, and this polyubiquitination of NSP6 appeared crucial for recruitment of NEMO to the NSP6-TAK1 complex and NF-κB activation. On the other hand, ring finger protein 121 (RNF121) was required for the polyubiquitination of ORF7a. Knockdown of RNF121 significantly decreased ORF7a binding of TAK1 and NEMO, resulting in the suppression of NF-κB activation. Taken together, our results provide novel molecular insights into the pathogenesis of SARS-CoV-2 and the host immune response to SARS-CoV-2 infection. IMPORTANCE The detailed molecular basis of the induction of proinflammatory cytokines and chemokines by SARS-CoV-2 is unclear, although such induction is clearly related to the severity of COVID-19. Here, we show that SARS-CoV-2 NSP6 and ORF7a lead to NF-κB activation through associations with TAK1. K63-linked polyubiquitination of NSP6 and ORF7a by TRIM13 and RNF121, respectively, appears essential for NF-κB activation. These results suggest that inhibition of the NSP6 and ORF7a gene products may reduce the severity of COVID-19 symptoms by decreasing proinflammatory cytokine levels.
Operational Performance Modelling of Indian Banks: A Data Envelopment Analysis Approach

The rising level of non-performing loans (NPLs) posits risk on the operational working of the banking sector. The study focuses on developing an operational performance model for banks by considering the NPLs. The study uses non-parametric methodology to develop a non-oriented and non-radial Data Envelopment Analysis (DEA) model with NPLs as undesirable output. The dataset of 39 Indian commercial banks for period of 5 years is selected for the study. The findings of the study prove that considering risk into performance modelling leads to an unbiased efficiency indicator. Also, the non-parametric test confirms the relationship between operational efficiency and ownership. The study uses non-orientation modelling results to reveal inefficient sources within under-performing banks. As per the findings, slack variable analysis reveals two problem areas: ‘fixed assets’ and ‘NPLs’. A higher focus on improving utilization of fixed assets as well as controlling the level of rising NPL (risk) is highly significant for benchmarking performance. Overall, the study supports the business decision to control excess inputs and outputs for banks to achieve the efficient frontier. Significant managerial implications are linked to findings of the study focusing towards performance improvement of banks.