Miracle-dz/UniFAR-FTUs huggingface dataset:
Preprocessing-based Methods for Robotic Manipulation

Robotic manipulation is a key problem for several applications such as welding, pick-and-place, and automated assembly. However, motion planning for manipulation can be computationally expensive as it requires planning in the high-dimensional configuration space of the manipulator. Additionally, task-specific constraints such as strict time limits or constraints on end-effector motion further increase the complexity of this problem. In order to solve the planning problem tractably, several methods heavily rely on preprocessing, in which relevant information about the planning problem is gathered beforehand in order to enhance the performance of the planner. In this thesis, we propose algorithmic and system-level contributions to preprocessing-based methods for robotic manipulation. In the first part of the thesis, we demonstrate how planning for constrained manipulation can be sped up by adding additional actions to the action set of a search-based planner. Macro-actions are ordered combinations of primitive actions and can help make faster progress towards the goal and reduce planning time. However, this introduces a trade-off as additional actions will increase the branching factor of the search. We leverage preprocessing to compute a set of macro-actions that provide probably approximately correct (PAC) bounds on the performance of the planner. We demonstrate the benefits of our approach on a container-opening task with a PR2 robot arm. In the second part of the thesis, we present a planning and perception framework for intercepting projectiles using robotic manipulators. We focus on the problem of intercepting a projectile moving towards a robot equipped with a manipulator holding a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) compute a trajectory that can intercept the projectile, and (iii) execute the found trajectory. These three steps need to be executed as fast as possible (<= 1 second in our setting) in order to maximize the number of episodes where the projectile is successfully intercepted. To this end, we propose a planning framework that constructs a trajectory database, wherein the individual trajectories can be used in order to intercept the projectile. Additionally, we also present a perception pipeline that continually provides state estimates of a fast-moving projectile within a short time window. We evaluate our approach both in simulation and on the physical PR2 robot with RGB-D cameras.
Theory Of Decision Under Uncertainty

This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.
Dynamic Coloring of Unit Interval Graphs with Limited Recourse Budget

In this paper we study the problem of coloring a unit interval graph which changes dynamically. In our model the unit intervals are added or removed one at the time, and have to be colored immediately, so that no two overlapping intervals share the same color. After each update only a limited number of intervals are allowed to be recolored. The limit on the number of recolorings per update is called the recourse budget. In this paper we show, that if the graph remains k -colorable at all times, the updates consist of insertions only, and the final instance consists of n intervals, then we can achieve an amortized recourse budget of O ( k 7 log n ) while maintaining a proper coloring with k colors. This is an exponential improvement over the result in [10] in terms of both k and n . We complement this result by showing the lower bound of Ω( n ) on the amortized recourse budget in the fully dynamic setting. Our incremental algorithm can be efficiently implemented. As an additional application of our techniques we include a new combinatorial result on coloring unit circular arc graphs. Let L be the maximum number of arcs intersecting in one point for some set of unit circular arcs A . We show that if there is a set A ′ of non-intersecting unit arcs of size L 2 − 1 such that A ∪ A ′ does not contain L + 1 arcs intersecting in one point, then it is possible to color A with L colors. This complements the work on circular arc coloring [4, 30, 31], which specifies sufficient conditions needed to color A with L + 1 colors or more.
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021

In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of suﬀicient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair.
Sub-bandgap photon-assisted electron trapping and detrapping in AlGaN/GaN heterostructure field-effect transistors

We have investigated photon-assisted trapping and detrapping of electrons injected from the gate under negative bias in a heterostructure field-effect transistor (HFET). The electron injection rate from the gate was found to be dramatically affected by sub-bandgap laser illumination. The trapped electrons reduced the two-dimensional electron gas (2DEG) density at the AlGaN/GaN heterointerface but could also be emitted from their trap states by sub-bandgap photons, leading to a recovery of 2DEG density. The trapping and detrapping dynamics were found to be strongly dependent on the wavelength and focal position of the laser, as well as the gate bias stress time prior to illumination of the HFET. Applying this phenomenon of trapping and detrapping assisted by sub-bandgap photons, red, green, and purple lasers were used to demonstrate photo-assisted dynamic switching operations by manipulation of trapped carriers at the surface of an AlGaN/GaN HFET. A physical model based on band diagrams, explaining the trapping and detrapping behavior of electrons, has been presented.
Suspended phospholipid bilayers: a new and versatile biological membrane model

We present a reliable method of forming a ﬂuid phase lipid bilayer suspended underneath a surfactant monolayer at the air-water interface, a novel bacterial membrane mimic. Bilayer formation proceeds by vesicle adsorption and subsequent rupture beneath a monolayer of a cationic surfactant adsorbed at the air-water interface of a laminar ﬂow trough. The laminar ﬂow facilitates buﬀer exchange beneath the surfactant monolayer, allowing for sequential deposition of vesicles, osmotic rupture and sub-phase contrast variation for neutron reﬂectometry. As the lipid bilayer formed by this process does not interact with a solid substrate, the suspended bilayer platform is well-suited to studying lipid bilayers including inserted membrane proteins.
Hypothesis Transfer in Bandits by Weighted Models

We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning. That is, we assume having access to a previously learned model on an unobserved set of contexts, and we leverage it in order to accelerate exploration on a new bandit problem. Our transfer strategy is based on a re-weighting scheme for which we show a reduction in the regret over the classic Linear UCB when transfer is desired, while recovering the classic regret rate when the two tasks are unrelated. We further extend this method to an arbitrary amount of source models, where the algorithm decides which model is preferred at each time step. Additionally we discuss an approach where a dynamic convex combination of source models is given in terms of a biased regularization term in the classic LinUCB algorithm. The algorithms and the theoretical analysis of our proposed methods substantiated by empirical evaluations on simulated and real-world data.
Query-based Hard-Image Retrieval for Object Detection at Test Time

There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applications such as autonomous driving, it is also crucial to characterise potential failures beyond simple requirements of detection performance. For example, a missed detection of a pedestrian close to an ego vehicle will generally require closer inspection than a missed detection of a car in the distance. The problem of predicting such potential failures at test time has largely been overlooked in the literature and conventional approaches based on detection uncertainty fall short in that they are agnostic to such fine-grained characterisation of errors. In this work, we propose to reformulate the problem of finding "hard" images as a query-based hard image retrieval task, where queries are specific definitions of "hardness", and offer a simple and intuitive method that can solve this task for a large family of queries. Our method is entirely post-hoc, does not require ground-truth annotations, is independent of the choice of a detector, and relies on an efficient Monte Carlo estimation that uses a simple stochastic model in place of the ground-truth. We show experimentally that it can be applied successfully to a wide variety of queries for which it can reliably identify hard images for a given detector without any labelled data. We provide results on ranking and classification tasks using the widely used RetinaNet, Faster-RCNN, Mask-RCNN, and Cascade Mask-RCNN object detectors. The code for this project is available at https://github.com/fiveai/hardest.
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension

Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available.
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients

Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms. Estimation of meta-gradients is central to the performance of these meta-algorithms, and has been studied in the setting of MAML-style short-horizon meta-RL problems. In this context, prior work has investigated the estimation of the Hessian of the RL objective, as well as tackling the problem of credit assignment to pre-adaptation behavior by making a sampling correction. However, we show that Hessian estimation, implemented for example by DiCE and its variants, always adds bias and can also add variance to meta-gradient estimation. Meanwhile, meta-gradient estimation has been studied less in the important long-horizon setting, where backpropagation through the full inner optimization trajectories is not feasible. We study the bias and variance tradeoff arising from truncated backpropagation and sampling correction, and additionally compare to evolution strategies, which is a recently popular alternative strategy to long-horizon meta-learning. While prior work implicitly chooses points in this bias-variance space, we disentangle the sources of bias and variance and present an empirical study that relates existing estimators to each other.
Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection

Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists. Many works have proposed to employ deep learning methods that involve the segmentation of optic disc and cup for glaucoma detection, in which the segmentation process is often considered merely as an upstream sub-task. The relationship between fundus images and segmentation masks in terms of joint decision-making in glaucoma assessment is rarely explored. We propose a novel segmentation-based information extraction and amalgamation method for the task of glaucoma detection, which leverages the robustness of segmentation masks without disregarding the rich information in the original fundus images. Experimental results on both private and public datasets demonstrate that our proposed method outperforms all models that utilize solely either fundus images or masks.
The RITAS algorithm: a constructive yield monitor data processing algorithm

Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or empirically-motivated statistical rules for extreme value identification and removal. We propose a constructive algorithm for handling well-documented yield monitor data artifacts without resorting to data deletion. The four-step Rectangle creation, Intersection assignment and Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample observations as overlapping, unequally-shaped, irregularly-sized, time-ordered, areal spatial units to better replicate the nature of the destructive sampling process. Positional data is used to create rectangular areal spatial units. Time-ordered intersecting area tessellation and harvested mass apportioning generate regularly-shaped and -sized polygons partitioning the entire harvested area. Finally, smoothing via a Gaussian process is used to provide map users with spatial-trend visualization. The intermediate steps as well as the algorithm output are illustrated in maize and soybean grain yield maps for five years of yield monitor data collected at a research agricultural site located in the US Fish and Wildlife Service Neal Smith National Wildlife Refuge.
Automatic Sign Reading and Localization for Semantic Mapping with an Office Robot

—Semantic mapping is the task of providing a robot with a map of its environment beyond the open, navigable space of traditional Simultaneous Localization and Mapping (SLAM) algorithms by attaching semantics to locations. The system presented in this work reads door placards to annotate the lo- cations of ofﬁces. Whereas prior work on this system developed hand-crafted detectors, this system leverages YOLOv5 for sign detection and EAST for text recognition. Placards are localized by computing their pose from a point cloud in a RGB-D camera frame localized by a modiﬁed ORB-SLAM. Semantic mapping is accomplished in a post-processing step after robot exploration from video recording. System performance is reported in terms of the number of placards identiﬁed, the accuracy of their placement onto a SLAM map, the accuracy of the map built, and the correctness transcribed placard text.
Ensemble Kalman Methods: A Mean Field Perspective

This paper provides a unifying mean field based framework for the derivation and analysis of ensemble Kalman methods. Both state estimation and parameter estimation problems are considered, and formulations in both discrete and continuous time are employed. For state estimation problems both the control and filtering approaches are studied; analogously, for parameter estimation (inverse) problems the optimization and Bayesian perspectives are both studied. The approach taken unifies a wide-ranging literature in the field, provides a framework for analysis of ensemble Kalman methods, and suggests open problems.
The complexity of unsupervised learning of lexicographic preferences

This paper considers the task of learning users' preferences on a combinatorial set of alternatives, as generally used by online configurators, for example. In many settings, only a set of selected alternatives during past interactions is available to the learner. Fargier et al. [2018] propose an approach to learn, in such a setting, a model of the users' preferences that ranks previously chosen alternatives as high as possible; and an algorithm to learn, in this setting, a particular model of preferences: lexicographic preferences trees (LP-trees). In this paper, we study complexity-theoretical problems related to this approach. We give an upper bound on the sample complexity of learning an LP-tree, which is logarithmic in the number of attributes. We also prove that computing the LP tree that minimises the empirical risk can be done in polynomial time when restricted to the class of linear LP-trees.
Learning Rigid Body Dynamics with Lagrangian Graph Neural Network

Lagrangian and Hamiltonian neural networks (L NN and H NN respectively) encode strong inductive biases that allow them to outperform other models of physical systems signiﬁcantly. However, these models have, thus far, mostly been limited to simple systems such as pendulums and springs or a single rigid body such as a gyroscope or a rigid rotor. Here, we present a Lagrangian graph neural network (L GNN ) that can learn the dynamics of rigid bodies by exploiting their topology. We demonstrate the performance of L GNN by learning the dynamics of ropes, chains, and trusses with the bars modeled as rigid bodies. L GNN also exhibits generalizability—L GNN trained on chains with a few segments exhibits generalizability to simulate a chain with large number of links and arbitrary link length. We also show that the L GNN can simulate unseen hybrid systems including bars and chains, on which they have not been trained on. Speciﬁcally, we show that the L GNN can be used to model the dynamics of complex real-world structures such as the stability of tensegrity structures. Finally, we discuss the non-diagonal nature of the mass matrix and it’s ability to generalize in complex systems.
Some expected extraordinary behaviours of nitride vertical-cavity surface-emitting lasers following from special features of nitride materials

In spite of dramatic efforts of numerous technological centres in the world, until now room -temperature continuous-wave operation of nitride diode vertical-cavity surface-emitting lasers (VCSEL) has not been reported. It is probably associated with special features of nitride materials, which essentially distinguish them from other AIIIBV semiconductors. In this situation, technological centres need theoretical support to successfully design, modify and optimise possible structures of nitride VCSELs. Therefore, in the present paper, important mutual interactions between physical phenomena taking place inside nitride VCSELs and crucial for their operation are analysed. Some practical guidelines for nitride VCSEL designers are also drawn up.
UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low Vision

Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases, from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible at scale. Herein, we propose a novel vision-based localization pipeline for a specific use case: navigation support for end users with blindness and low vision. Given a query image taken by an end user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space. The geolocations of these similar images are utilized in a downstream task that employs a weighted-average method to estimate the end user’s location. Another downstream task utilizes the perspective-n-point (PnP) algorithm to estimate the end user’s direction by exploiting the 2D–3D point correspondences between the query image and the 3D environment, as extracted from matched images in the database. Additionally, this system implements Dijkstra’s algorithm to calculate a shortest path based on a navigable map that includes the trip origin and destination. The topometric map used for localization and navigation is built using a customized graphical user interface that projects a 3D reconstructed sparse map, built from a sequence of images, to the corresponding a priori 2D floor plan. Sequential images used for map construction can be collected in a pre-mapping step or scavenged through public databases/citizen science. The end-to-end system can be installed on any internet-accessible device with a camera that hosts a custom mobile application. For evaluation purposes, mapping and localization were tested in a complex hospital environment. The evaluation results demonstrate that our system can achieve localization with an average error of less than 1 m without knowledge of the camera’s intrinsic parameters, such as focal length.
Foreground removal and 21 cm signal estimates: comparing different blind methods for the BINGO Telescope

Context. Currently, Dark Energy (DE) does not have a satisfactory explanation in theoretical terms. However, its inﬂuence is well established in the post reionisation epoch. BINGO will observe Hydrogen distribution by means of the 21 cm line signal by drift-scan mapping through a tomographic analysis called Intensity Mapping (IM) between 980 and 1260 MHz which aims at analyzing DE using Baryon Acoustic Oscillations . In the same frequency range, there are several other unwanted signals as well as instrumental noise, contaminating the target signal. For the BINGO sky range and sensitivity and the foreground model considered in the current simulation, the e ective dimension of the foreground subspace leading to best results is equal to three, composed of non-physical templates. At this moment of the pipeline conﬁguration, using 50 or 400 simulations is statistically equivalent. It is also possible to reduce the number of multipoles by half to speed up the process and maintain the quality of results. All three algorithms used to perform foreground removal yielded statistically equivalent results for estimating the 21cm signal when we assume 400 realizations and GMCA and FastICA’s mixing matrix dimensions equal to three. However, concerning computational cost in this stage of the BINGO pipeline, FastICA is faster than other algorithms. A new comparison will be necessary when the time-ordered-data and map-making are available.