Miracle-dz/UniFAR-FTUs huggingface dataset:
The Improvement of Strabismus Surgery : The Role of the Suspension of the Human Eye

Background Reoperations are frequently necessary in strabismus surgery. The goal of this study was to analyze human-error related factors that introduce variability in the results of strabismus surgery in a systematic fashion. Methods We identified the primary factors that influence the outcome of strabismus surgery. For each of the human-error related factors we quantified variation with clinical assessments: measurement of the angle of strabismus, surgical strategy and surgical accuracy. Firstly, six patients were examined by six orthoptists and accuracy of prism cover tests was assessed. Secondly, a questionnaire with sample cases (10, 15and 20of infantile esotropia) was put to orthoptists and ophthalmologists to determine variation in current surgical strategy. Finally, photographs made during surgery were analyzed to assess surgical accuracy. The influence of human-error related factors was related to the influence of inter-patient differences with a mechanical model. The relative contribution of all factors was assessed with a sensitivity analysis and results were compared to clinical studies. Results The surgical trajectory of strabismus surgery could be modeled mathematically. Measurement of angle of strabismus, surgical technique, anatomy and physiology were considered. Variations in the human-error related factors were: (1) The latent angle at distant fixation was measured with a 90% confidence interval of 5and comprised 20% of the total variance of the postoperative angle (2) orthoptists decided for bilateral recessions of, respectively, 7.3±1.7mm (total amount of two recessions), 9.1±1.2mm and 10.3±1.3mm, which comprised 15% of the total variance. (2) Surgical accuracy was estimated at ±0.5mm, which comprised 20% of the total variance. Conclusion The human error in strabismus surgery could be quantified with a sensitivity analysis. Approximately half of the reoperations in strabismus surgery are caused by inaccuracy in the measurement of the angle of strabismus, variability in surgical strategy and imprecise surgery.
Multi-Weights Intra Prediction with Double Reference Lines

Conventional intra prediction applies the nearest reference line to predict the current block and improves the performance by adding more angular prediction modes. However, the performance of prediction near the bottom-right corner of the block suffers from non-negligible degradation due to the reference samples used for prediction locate at the above and left sides of the current block. Therefore, in this paper, to improve this situation, multi-weights intra prediction is proposed. Considering the coding complexity, the optimal two reference lines are picked out at first based on minimum distortion criterion. Following, the best weight combination is selected out from candidate weight combinations and is applied to predict the current block. Experimental results show that the proposed algorithm achieves 2.19% bitrate saving on average compared with the conventional intra prediction.
Comparative Analysis of 3D Shape Recognition in the Presence of Data Inaccuracies

Classification of 3D shapes into physically meaningful categories is one of the most important tasks in understanding the immediate environment. Methods that leverage the recent advancements in deep learning have shown to outperform the traditional approaches. However, performances of those methods have only been analyzed with relatively clean data. Three-dimensional measurement sets (point clouds) produced by 3D scanners are rarely that accurate and often contain noise, outliers or missing points. This paper presents an extensive analysis of the robustness of the state-of-the-art neural network algorithms to realistic data inaccuracies. Our experiments show that the existence of these inaccuracies can significantly affect the performance of the deep learning-based algorithms.
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification

The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data. Most previous work follows a two-step process. They first separate the content from the original sentiment, and then directly generate text with the target sentiment only based on the content produced by the first step. However, the second step bears both the target sentiment addition and content reconstruction, thus resulting in a lack of specific information like proper nouns in the generated text. To remedy this, we propose a specificity-driven cascading approach in this work, which can effectively increase the specificity of the generated text and further improve content preservation. In addition, we propose a more reasonable metric to evaluate sentiment modification. The experiments show that our approach outperforms competitive baselines by a large margin, which achieves 11% and 38% relative improvements of the overall metric on the Yelp and Amazon datasets, respectively.
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning

This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.
Optimal Timing for Episodic Retrieval and Encoding for Event Understanding

When should an intelligent agent encode and retrieve episodic memories? In this work, we use a memoryaugmented neural network to study how episodic memory can be most effectively deployed in the service of event understanding. Events are generated from underlying situation models and situations sometimes re-occur, making it useful to have an episodic memory system that can store and retrieve these situation models. For retrieval, our model learned to wait adaptively to accumulate information to ensure accurate retrieval of the target memory. Additionally, model variants that stored episodic memories at event boundaries (but not mid-event) had better subsequent recall performance. This latter result provides a normative explanation of the finding (from human fMRI) that the hippocampus is differentially engaged at event boundaries.
Learning to Bootstrap for Entity Set Expansion

Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category. Traditional bootstrapping methods often suffer from two problems: 1) delayed feedback, i.e., the pattern evaluation relies on both its direct extraction quality and extraction quality in later iterations. 2) sparse supervision, i.e., only few seed entities are used as the supervision. To address the above two problems, we propose a novel bootstrapping method combining the Monte Carlo Tree Search (MCTS) algorithm with a deep similarity network, which can efficiently estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. Experimental results confirm the effectiveness of the proposed method.
Kpsnet: Keypoint Detection and Feature Extraction for Point Cloud Registration

This paper presents the KPSNet, a KeyPoint Siamese Network to simultaneously learn task-desirable keypoint detector and feature extractor. The keypoint detector is optimized to predict a score vector, which signifies the probability of each candidate being a keypoint. The feature extractor is optimized to learn robust features of keypoints by exploiting the correspondence between the keypoints generated from two inputs, respectively. For training, the KPSNet does not require to manually annotate keypoints and local patches pairwise. Instead, we design an alignment module to establish the correspondence between the two inputs and generate positive and negative samples on-the-fly. Therefore, our method can be easily extended to new scenes. We test the proposed method on the open-source benchmark and experiments show the validity of our method.
Clustering Approaches for Top-k Recommender Systems

Clustering-based recommender systems bound the seek of similar users within small user clusters providing fast recommendations in large-scale datasets. Then groups can naturally be distributed into different data partitions scaling up in the number of users the recommender system can handle. Unfortunately, while the number of users and items included in a cluster solution increases, the performance in terms of precision of a clustering-based recommender system decreases. We present a novel approach that introduces a cluster-based distance function used for neighborhood computation. In our approach, clusters generated from the training data provide the basis for neighborhood selection. Then, to expand the search of relevant users, we use a novel measure that can exploit the global cluster structure to infer cluster-outside user’s distances. Empirical studies on five widely known benchmark datasets show that our proposal is very competitive in terms of precision, recall, and NDCG. However, the strongest point of our method relies on scalability, reaching speedups of 20× in a sequential computing evaluation framework and up to 100× in a parallel architecture. These results show that an efficient implementation of our cluster-based CF method can handle very large datasets providing also good results in terms of precision, avoiding the high computational costs involved in the application of more sophisticated techniques.
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer

Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naive way such as data preprocessing. To mitigate this problem, we study how to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets
An Inference Network Model for Goal-directed Attentional Selection

”Listen to the cello in this symphony! ” How can we direct selective attention according to different goals, even in distracting environments which we haven’t experienced before? It is an essential cognitive ability of the brain, but remains challenging for machines. We developed a computational model that can identify individual digits in images containing multiple overlapping digits, without ever having seen overlapping digits during training. The goal-driven attentional selection is modelled as inferring the posterior distribution of latent variables (the attended target) in a generative model, conditioned on both sensory input and different semantic goals. A neural network model has been build to efficiently carry out the the inference process by predicting the most likely results, instead of using classic per-sample based iterative optimization methods which may not naturally map onto neural structures. Our model also help to understand how top-down and bottom-up attention are combined during perception in the brain.
Multi-Level Network for High-Speed Multi-Person Pose Estimation

In multi-person pose estimation, the left/right joint type discrimination is always a hard problem because of the similar appearance. Traditionally, we solve this problem by stacking multiple refinement modules to increase network’s receptive fields and capture more global context, which can also increase a great amount of computation. In this paper, we propose a Multi-level Network (MLN) that learns to aggregate features from lower-level (left/right information), upper-level (localization information), joint-limb level (complementary information) and global-level (context) information for discrimination of joint type. Through feature reuse and its intra-relation, MLN can attain comparable performance to other conventional methods while runtime speed retains at 42 FPS.
Design of Logically Obfuscated n-bit ALU for Enhanced Security

The need of a computer in the modern world is inevitable, whose sole purpose is to compute or aid calculations. The need for an Arithmetic Logic Unit (ALU) is as important as the computer, simply because ALU forms the fundamental part of any Central Processing Unit (CPU). And so the encryption of an ALU is highly mandatory for the safety of the device as there are hardly any device without an ALU. This paper deals with the design of an n-bit ALU using a hardware description language, VHDL that is structurally modelled to extend the flexibility and reusability of the device. Moreover, the logic of obfuscation used to enhance its security without compromising on the correct functionality and maximum efficiency of the device is proposed.The results are verified and analysed using FPGAs, which proves the inclusion of the obfuscation module without a considerable increase in area,power overhead of the ALU.
Wireless sensor networks for indoor mapping and accurate localization for low speed navigation in smart cities

With the increasing demand for urban space, more and more multistory carparks are needed. Although these carparks help to utilize urban space more efficient, they also introduce a new problem. Reports suggest approximately 70 million hours of parking slot searching each year, equivalently 700 million euros loss for France alone. In addition, carparks uses are exceeding their original purposes. Demanding features such as electric charger, online booking of parking spaces, dynamic guidance or mobile payment etc. turn a carpark into a competitive smart environment. One solution to this problem is to develop an autonomous navigation system for intelligent vehicles in the carpark situation. The thesis will identify one of these sub-tasks namely localization in GPS-denied environments. This thesis will present a novel method to solve the indicated problem while keeping the system follows four criteria: availability, scalability, universality and accuracy. There are two main steps: (1) a solution to replicate the GPS behaviour for the GPS-denied environment, and (2) a framework that allows the fusion of GPS-like systems with other localization methods to achieve a high localization accuracy. First, a Wi-Fi Fingerprinting localization system is employed. An approach using an ensemble neural network on a hybrid Wi-Fi fingerprinting database is proposed in this thesis. Experiments in a year-long duration show that this system is capable of localizing vehicles with 2.25m of mean error in the global coordinate frame (WGS84). Second, a complete localization solution must be a fusion of multiple techniques. This allows global as well as local levels of localization to function together. At the same time, having redundancy in the system boosts accuracy and reliability. In this thesis, a flexible fusion framework for multiple localization sensors is proposed. This fusion framework will not only deal with the GPS-denied environment but could be potentially used in the GPS-aided environment and provide a smooth transition between the two areas. To accomplish this demanding task, a Gaussian Mixture Model Particle Filter is developed. While the motion model of this particle filter incorporates data from the IMU (Inertial Measurement Unit) or laser-SLAM, the correction model is a Gaussian mixture model of multiple observations obtained from the Wi-Fi fingerprinting localization system. With two intelligent vehicles (a Cybercar and a Citroen C1 car), 64 experiments were carried out to validate the framework. A mean localization error of 0.5m is achieved in a global coordinate frame. Compare to other solutions with 0.2m of mean localization error in local coordinate frames; this proposed solution has advantages in terms of scalability, availability and universality as well.
A Factorized Version Space Algorithm for "Human-In-the-Loop" Data Exploration

While active learning (AL) has been recently applied to help the user explore a large database to retrieve data instances of interest, existing methods often require a large number of instances to be labeled in order to achieve good accuracy. To address this slow convergence problem, our work augments version space-based AL algorithms, which have strong theoretical results on convergence but are very costly to run, with additional insights obtained in the user labeling process. These insights lead to a novel algorithm that factorizes the version space to perform active learning in a set of subspaces. Our work offers theoretical results on optimality and approximation for this algorithm, as well as optimizations for better performance. Evaluation results show that our factorized version space algorithm significantly outperforms other version space algorithms, as well as a recent factorization-aware algorithm, for large database exploration.
Nonconvex Low-Rank Tensor Completion from Noisy Data

This paper investigates a problem of broad practical interest, namely, the reconstruction of a large-dimensional low-rank tensor from highly incomplete and randomly corrupted observations of its entries. Although a number of papers have been dedicated to this tensor completion problem, prior algorithms either are computationally too expensive for large-scale applications or come with suboptimal statistical performance. Motivated by this, we propose a fast two-stage nonconvex algorithm—a gradient method following a rough initialization—that achieves the best of both worlds: optimal statistical accuracy and computational efficiency. Specifically, the proposed algorithm provably completes the tensor and retrieves all low-rank factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e., minimal sample complexity and optimal estimation accuracy). The insights conveyed through our analysis of nonconvex optimization might have implications for a broader family of tensor reconstruction problems beyond tensor completion.
A Novel Scheme for Single-Channel Speech Dereverberation

This paper presents a novel scheme for speech dereverberation. The core of our method is a two-stage single-channel speech enhancement scheme. Degraded speech obtains a sparser representation of the linear prediction residual in the first stage of our proposed scheme by applying orthogonal matching pursuit on overcomplete bases, trained by the K-SVD algorithm. Our method includes an estimation of reverberation and mixing time from a recorded hand clap or a simulated room impulse response, which are used to create a time-domain envelope. Late reverberation is suppressed at the second stage by estimating its energy from the previous envelope and removed with spectral subtraction. Further speech enhancement is applied on minimizing the background noise, based on optimal smoothing and minimum statistics. Experimental results indicate favorable quality, compared to two state-of-the-art methods, especially in real reverberant environments with increased reverberation and background noise.
Are Topographic Deep Convolutional Neural Networks Better Models of the Ventral Visual Stream?

Neural computations along the ventral visual stream, -which culminates in the inferior temporal (IT) cortex -enable humans and monkeys to recognize objects quickly. Primate IT is organized topographically: nearby neurons have similar response properties. Yet the best models of the ventral visual stream deep artificial neural networks (ANNs) – have “IT” layers that lack topography. We built Topographic Deep ANNs (TDANNs) by incorporating a proxy wiring cost alongside the standard ImageNet categorization cost in the two “IT-like” layers of AlexNet (Lee et al., 2018), by specifying that “neurons” that have similar response properties should be physically close to each other. This cost both induced topographic structure and altered tuning characteristics of model IT neurons. We presented 2560 naturalistic images to monkeys and to ANNs. We found that, relative to the base (nontopographic) model, the “neurons” in the “IT” layer of some of the TDANN models matched actual IT neurons slightly better, and the dimensionality of the TDANN “IT” neural population was much closer to that of the measured monkey IT neural population. We also found that, while TDANNs did not show a statistically significant better match to human object discrimination behavior, detailed analysis suggests a trend in that direction. Taken together, TDANNs may better capture properties of IT cortex and wiring costs might be the cause of topographic organization in primate IT.
VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.