Miracle-dz/UniFAR-FTUs huggingface dataset:
1-Point RANSAC-Based Method for Ground Object Pose Estimation

Solving Perspective-n-Point (PnP) problems is a traditional way of estimating object poses. Given outlier-contaminated data, a pose of an object is calculated with PnP algorithms of n = {3, 4} in the RANSAC-based scheme. However, the computational complexity considerably increases along with n and the high complexity imposes a severe strain on devices which should estimate multiple object poses in real time. In this paper, we propose an efficient method based on 1-point RANSAC for estimating a pose of an object on the ground. In the proposed method, a pose is calculated with 1-DoF parameterization by using a ground object assumption and a 2D object bounding box as an additional observation, thereby achieving the fastest performance among the RANSAC-based methods. In addition, since the method suffers from the errors of the additional information, we propose a hierarchical robust estimation method for polishing a rough pose estimate and discovering more inliers in a coarse-to-fine manner. The experiments in synthetic and real-world datasets demonstrate the superiority of the proposed method.
Searching for wide-orbit gravitational instability protoplanets with ALMA in the dust continuum

Searches for young gas giant planets at wide separations have so far focused on techniques appropriate for compact (Jupiter sized) planets. Here we point out that protoplanets born through Gravitational Instability (GI) may remain in an initial pre-collapse phase for as long as the first $ 10^5-10^7$ years after formation. These objects are hundreds of times larger than Jupiter and their atmospheres are too cold ($T\sim$ tens of K) to emit in the NIR or H$\alpha$ via accretion shocks. However, it is possible that their dust emission can be detected with ALMA, even around Class I and II protoplanetary discs. In this paper we produce synthetic observations of these protoplanets. We find that making a detection in a disc at 140 parsecs would require a few hundred minutes of ALMA band 6 observation time. Protoplanets with masses of 3-5 $M_J$ have the highest chance of being detected; less massive objects require unreasonably long observation times (1000 minutes) while more massive ones collapse into giant planets before $10^5$ years. We propose that high resolution surveys of young ($10^5-10^6$ years), massive and face on discs offer the best chance for observing protoplanets. Such a detection would help to place constraints on the protoplanet mass spectrum, explain the turnover in the occurrence frequency of gas giants with system metallicity and constrain the prevalence of GI as a planet formation mechanism. Consistent lack of detection would be evidence against GI as a common planet formation mechanism.
Deciphering Lysis and its Regulation in Bacteriophage T4

Deciphering Lysis and Its Regulation in Bacteriophage T4. (August 2012) Samir H. Moussa, B.S., Baylor University; M.S., Baylor University Chair of Advisory Committee: Dr. Ryland F. Young Like all phages, T4 requires a holin (T) to effect lysis. The lysis event depends on the temporally regulated action of T, which accumulates in the inner membrane (IM) until, at an allele-specific time, it triggers to form a large “hole” in the membrane. Hole formation then releases T4 lysozyme into the periplasm where it degrades the cell wall to elicit cell lysis. Unlike other phages, T4 is unique in exhibiting real-time regulation of lysis based on environmental conditions. Specifically, lysis can be delayed indefinitely in the lysis-inhibited state (LIN), where the normal temporal schedule for holin-triggering is over-ridden. Recently, it was shown that the imposition of LIN was correlated with the interaction of the periplasmic domains (PD) of RI and T. These studies have been extended in this dissertation using genetic, biochemical, and structural techniques to address the molecular mechanism of the RI-T LIN system. First, the PD of RI and an RI-T complex were purified, characterized biophysically, and crystallized to yield the first atomic resolution structures of either a holin or antiholin. The RI PD is mostly alpha-helical that undergoes a conformational
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization

In this paper, we present a novel framework that can achieve multimodal image-to-image translation by simply encouraging the statistical dependence between the latent code and the output image in conditional generative adversarial networks. In addition, by incorporating a U-net generator into our framework, our method only needs to learn a one-sided translation model from the source image domain to the target image domain for both supervised and unsupervised multimodal image-to-image translation. Furthermore, our method also achieves disentanglement between the source domain content and the target domain style for free. We conduct experiments under supervised and unsupervised settings on various benchmark image-to-image translation datasets compared with the state-of-the-art methods, showing the effectiveness and simplicity of our method to achieve multimodal and high-quality results.
Deep Sketch-guided Cartoon Video Synthesis

We propose a novel framework to produce cartoon videos by fetching the color information from two input keyframes while following the animated motion guided by a user sketch. The key idea of the proposed approach is to estimate the dense cross-domain correspondence between the sketch and cartoon video frames, following by a blending module with occlusion estimation to synthesize the middle frame guided by the sketch. After that, the inputs and the synthetic frame equipped with established correspondence are fed into an arbitrary-time frame interpolation pipeline to generate and refine additional inbetween frames. Finally, a video post-processing approach is used to further improve the result. Compared to common frame interpolation methods, our approach can address frames with relatively large motion and also has the flexibility to enable users to control the generated video sequences by editing the sketch guidance. By explicitly considering the correspondence between frames and the sketch, our methods can achieve high-quality synthetic results compared with image synthesis methods. Our results show that our system generalizes well to different movie frames, achieving better results than existing solutions.
Accelerating black hole thermodynamics with boost time

We derive a thermodynamic first law for the electrically charged C-metric with vanishing cosmological constant. This spacetime describes a pair of identical accelerating black holes each pulled by a cosmic string. Treating the ‘boost time’ of this spacetime as the canonical time, we find a thermodynamic first law in which every term has an unambiguous physical meaning. We then show how this first law may be derived using Noetherian methods in the covariant phase space formalism. We argue that the area of the acceleration horizon contributes to the entropy and that the appropriate notion of energy of this spacetime is a ‘boost mass’ which vanishes identically. The recovery of the Reissner–Nordstrom first law in the limit of small string tension is also demonstrated. Finally, we compute the action of the Euclidean section of the C-metric and show it agrees with the thermodynamic grand potential, providing an independent confirmation of the validity of our first law. We also briefly speculate on the significance of firewalls in this spacetime.
Virtual to Real Adaptation of Pedestrian Detectors

Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there has been an increasing interest in convolutional neural network-based architectures to execute such a task. One of these supervised networks’ critical goals is to generalize the knowledge learned during the training phase to new scenarios with different characteristics. A suitably labeled dataset is essential to achieve this purpose. The main problem is that manually annotating a dataset usually requires a lot of human effort, and it is costly. To this end, we introduce ViPeD (Virtual Pedestrian Dataset), a new synthetically generated set of images collected with the highly photo-realistic graphical engine of the video game GTA V (Grand Theft Auto V), where annotations are automatically acquired. However, when training solely on the synthetic dataset, the model experiences a Synthetic2Real domain shift leading to a performance drop when applied to real-world images. To mitigate this gap, we propose two different domain adaptation techniques suitable for the pedestrian detection task, but possibly applicable to general object detection. Experiments show that the network trained with ViPeD can generalize over unseen real-world scenarios better than the detector trained over real-world data, exploiting the variety of our synthetic dataset. Furthermore, we demonstrate that with our domain adaptation techniques, we can reduce the Synthetic2Real domain shift, making the two domains closer and obtaining a performance improvement when testing the network over the real-world images.
Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search

Recent research on conversational search highlights the importance of mixed-initiative in conversations. To enable mixed-initiative, the system should be able to ask clarifying questions to the user. However, the ability of the underlying ranking models (which support conversational search) to account for these clarifying questions and answers has not been analysed when ranking documents, at large. To this end, we analyse the performance of a lexical ranking model on a conversational search dataset with clarifying questions. We investigate, both quantitatively and qualitatively, how different aspects of clarifying questions and user answers affect the quality of ranking. We argue that there needs to be some fine-grained treatment of the entire conversational round of clarification, based on the explicit feedback which is present in such mixed-initiative settings. Informed by our findings, we introduce a simple heuristic-based lexical baseline, that significantly outperforms the existing naive baselines. Our work aims to enhance our understanding of the challenges present in this particular task and inform the design of more appropriate conversational ranking models.
Biochemical and molecular characterization of the glycosomal PTS2 import receptor peroxin 7 in «Leishmania donovani»

The Leishmania peroxin 7 (LmPEX7 or LdPEX7) is the receptor that translocates PTS2 signal-containing proteins into the glycosome. This microbody is unique to and crucial for the survival of trypanosomatids which include Leishmania and Trypanosoma, the causative agents of leishmaniasis and African sleeping sickness, respectively. Proteins are imported into the glycosome via two pathways, PTS1 and PTS2, which involves the formation of a PTS-receptor complex in the cytosol, docking of the complex on a translocation apparatus on the glycosomal membrane, and subsequent release of the cargo protein into the lumen. However, the precise steps in glycosome protein trafficking are not well-defined and to understand the function of these organelles and prove their potential as chemotherapeutic targets, the mechanism of glycosome biogenesis needs to be fully elucidated. Not much is known about the mechanism of PTS2 import pathway in glycosomes as studies on PEX7 have been hampered by the difficulty in expressing a soluble recombinant form of this receptor. To dissect the PTS2 import pathway and to determine the role of PEX7 in Leishmania, this protein was cloned and characterized. LmPEX7 is a ~41 kDa protein containing six conserved WD40 motifs that displays limited sequence similarity to PEX7 homologues involved in the biogenesis of evolutionarily-related peroxisomes found in other eukaryotes. LmPEX7 interacts with PTS2 proteins, the PTS1 receptor LdPEX5, and the membrane-associated docking protein LdPEX14. These interactions, characterized through various biochemical techniques, were mediated by specific binding domains, formation of stable protein-protein complexes, and conform%%%%La peroxine Leishmania 7 (LmPEX7 ou LdPEX7) est un recepteur qui transloque les proteines qui contiennent le signal PTS2 dans le glycosome. Ce glycosome est unique et critique aux trypanosomes, tels que Leishmania et Trypanosoma, les agents causant la leishmaniose et la maladie Africaine du sommeil. Les proteines sont importees vers le glycosome par deux voies, PTS1 et PTS2, qui necessitent la formation d'un complexe PTS dans le cytosol, l'amarrage du complexe sur un appareil de translocation sur la membrane du glycosome, et permet la liberation de la charge proteique dans le lumen. Par contre, les etapes precises dans l'acheminement de proteines glycosomales ne sont pas bien definies et pour comprendre les fonctions de ces organelles et prouver qu'elles etre des cibles chimiotherapiques, les mecanismes impliques dans la biogenese doivent etre tres bien elucides. Pour dissequer le mecanisme…
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose between supporting just one of these frameworks or reimplementing the library for each framework and dealing with code duplication. Users of such libraries can more easily switch frameworks without being locked in by a specific 3rd party library. Beyond multi-framework support, EagerPy also brings comprehensive type annotations and consistent support for method chaining to any framework. The latest documentation is available online at this https URL and the code can be found on GitHub at this https URL.
Spectral Algorithms for Community Detection in Directed Networks

Community detection in large social networks is affected by degree heterogeneity of nodes. The D-SCORE algorithm for directed networks was introduced to reduce this effect by taking the element-wise ratios of the singular vectors of the adjacency matrix before clustering. Meaningful results were obtained for the statistician citation network, but rigorous analysis on its performance was missing. First, this paper establishes theoretical guarantee for this algorithm and its variants for the directed degree-corrected block model (Directed-DCBM). Second, this paper provides significant improvements for the original D-SCORE algorithms by attaching the nodes outside of the community cores using the information of the original network instead of the singular vectors.
Knowledge Distillation and Data Selection for Semi-Supervised Learning in CTC Acoustic Models

Semi-supervised learning (SSL) is an active area of research which aims to utilize unlabelled data in order to improve the accuracy of speech recognition systems. The current study proposes a methodology for integration of two key ideas: 1) SSL using connectionist temporal classification (CTC) objective and teacher-student based learning 2) Designing effective data-selection mechanisms for leveraging unlabelled data to boost performance of student models. Our aim is to establish the importance of good criteria in selecting samples from a large pool of unlabelled data based on attributes like confidence measure, speaker and content variability. The question we try to answer is: Is it possible to design a data selection mechanism which reduces dependence on a large set of randomly selected unlabelled samples without compromising on Word Error Rate (WER)? We perform empirical investigations of different data selection methods to answer this question and quantify the effect of different sampling strategies. On a semi-supervised ASR setting with 40000 hours of carefully selected unlabelled data, our CTC-SSL approach gives 17% relative WER improvement over a baseline CTC system trained with labelled data. It also achieves on-par performance with CTC-SSL system trained on order of magnitude larger unlabeled data based on random sampling.
SEALing Neural Network Models in Secure Deep Learning Accelerators

Deep learning (DL) accelerators are increasingly deployed on edge devices to support fast local inferences. However, they suffer from a new security problem, i.e., being vulnerable to physical access based attacks. An adversary can easily obtain the entire neural network (NN) model by physically snooping the GDDR memory bus that connects the accelerator chip with DRAM memory. Therefore, memory encryption becomes important for DL accelerators on edge devices to improve the security of NN models. Nevertheless, we observe that traditional memory encryption solutions that have been efficiently used in CPU systems cause significant performance degradation when directly used in DL accelerators. The main reason comes from the big bandwidth gap between the GDDR memory bus and the encryption engine. To address this problem, our paper proposes SEAL, a Secure and Efficient Accelerator scheme for deep Learning. SEAL enhances the performance of the encrypted DL accelerator from two aspects, i.e., improving the data access bandwidth and the efficiency of memory encryption. Specifically, to improve the data access bandwidth, SEAL leverages a criticality-aware smart encryption scheme which identifies partial data that have no impact on the security of NN models and allows them to bypass the encryption engine, thus reducing the amount of data to be encrypted. To improve the efficiency of memory encryption, SEAL leverages a colocation mode encryption scheme to eliminate memory accesses from counters used for encryption by co-locating data and their counters. Our experimental results demonstrate that, compared with traditional memory encryption solutions, SEAL achieves 1.4 ~ 1.6 times IPC improvement and reduces the inference latency by 39% ~ 60%. Compared with a baseline accelerator without memory encryption, SEAL compromises only 5% ~ 7% IPC for significant security improvement.
Speaker discrimination in humans and machines: Effects of speaking style variability

Does speaking style variation affect humans' ability to distinguish individuals from their voices? How do humans compare with automatic systems designed to discriminate between voices? In this paper, we attempt to answer these questions by comparing human and machine speaker discrimination performance for read speech versus casual conversations. Thirty listeners were asked to perform a same versus different speaker task. Their performance was compared to a state-of-the-art x-vector/PLDA-based automatic speaker verification system. Results showed that both humans and machines performed better with style-matched stimuli, and human performance was better when listeners were native speakers of American English. Native listeners performed better than machines in the style-matched conditions (EERs of 6.96% versus 14.35% for read speech, and 15.12% versus 19.87%, for conversations), but for style-mismatched conditions, there was no significant difference between native listeners and machines. In all conditions, fusing human responses with machine results showed improvements compared to each alone, suggesting that humans and machines have different approaches to speaker discrimination tasks. Differences in the approaches were further confirmed by examining results for individual speakers which showed that the perception of distinct and confused speakers differed between human listeners and machines.
More Effective Software Repository Mining

Background: Data mining and analyzing of public Git software repositories is a growing research field. The tools used for studies that investigate a single project or a group of projects have been refined, but it is not clear whether the results obtained on such ``convenience samples'' generalize. Aims: This paper aims to elucidate the difficulties faced by researchers who would like to ascertain the generalizability of their findings by introducing an interface that addresses the issues with obtaining representative samples. Results: To do that we explore how to exploit the World of Code system to make software repository sampling and analysis much more accessible. Specifically, we present a resource for Mining Software Repository researchers that is intended to simplify data sampling and retrieval workflow and, through that, increase the validity and completeness of data. Conclusions: This system has the potential to provide researchers a resource that greatly eases the difficulty of data retrieval and addresses many of the currently standing issues with data sampling.
Differential diffusion model with two equilibrium states

The difference diffusion model with two equilibrium states is given by a stochastic equation with two components: the predicted one, which is determined by the regression function of increments with two equilibriums, and the stochastic one, which is the martingale difference. We propose a classification of zones of influence of equilibriums according to asymptotic properties of trajectories of statistical experiments. We study asymptotic behavior of statistical experiments, determined by the sums of $N$ sample values, as $N\to\infty$.
Block Shuffle: A Method for High-Resolution Fast Style Transfer With Limited Memory

Fast Style Transfer is a series of Neural Style Transfer algorithms that use feed-forward neural networks to render input images. Because of the high dimension of the output layer, these networks require much memory for computation. Therefore, for high-resolution images, most mobile devices and personal computers cannot stylize them, which greatly limits the application scenarios of Fast Style Transfer. At present, the two existing solutions are purchasing more memory and using the feathering-based method, but the former requires additional cost, and the latter has poor image quality. To solve this problem, we propose a novel image synthesis method named block shuffle, which converts a single task with high memory consumption to multiple subtasks with low memory consumption. This method can act as a plug-in for Fast Style Transfer without any modification to the network architecture. We use the most popular Fast Style Transfer repository on GitHub as the baseline. Experiments show that the quality of high-resolution images generated by our method is better than that of the feathering-based method. Although our method is an order of magnitude slower than the baseline, it can stylize high-resolution images with limited memory, which is impossible with the baseline. The code, models, and Android demonstration application will be made available on https://github.com/czczup/block-shuffle.