Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimalist Dynamic Climbing

Dynamics in locomotion is highly useful, as can be seen in animals and is becoming apparent in robots. For instance, chimpanzees are dynamic climbers that can reach virtually any part of a tree and even move to neighboring trees, while sloths are quasistatic climbers confined only to a few branches. Although dynamic maneuvers are undoubtedly beneficial, only a few engineered systems use them, most of which locomote horizontally. This is because the design and control are often extremely complicated. This thesis explores a family of dynamic climbing robots which extend robotic dynamic legged locomotion from horizontal motions such as walking, hopping, and running, to vertical motions such as leaping maneuvers. The motion of these dynamic robots resembles the motion of an athlete jumping and climbing inside a chute. Whereas this environment might be an unnavigable obstacle for a slow, quasistatic climber, it is an invaluable source of reaction forces for a dynamic climber. The mechanisms described here achieve dynamic, vertical motions while retaining simplicity in design and control. The first mechanism called DSAC, for Dynamic Single Actuated Climber, comprises only two links connected by a single oscillating actuator. This simple, openloop oscillation, propels the robot stably between two vertical walls. By rotating the axis of revolution of the single actuator by 90 degrees, we also developed a simpler robot that can be easily miniaturized and can be used to climb inside tubes. The DTAR, for Dynamic Tube Ascending Robot, uses a single continuously rotating motor, unlike the oscillating DSAC motor. This continuous rotation even further simplifies and enables the miniaturization of the robot to enable robust climbing inside small tubes. The last mechanism explored in this thesis is the ParkourBot, which sacrifices some of the simplicity shown in the first two mechanism in favor of efficiency and more versatile climbing. This mechanism comprises two efficient springy legs connected to a body. We use this family of dynamic climbers to explore a minimalist approach to locomotion. We first analyze the open-loop stability characteristics of all three mechanisms. We show how an open-loop, sensorless control, such as the fixed oscillation of the DSAC’s leg can converge to a stable orbit. We also show that a change in the mechanism’s parameters not only changes the stability of the system but also changes the climbing pattern from a symmetric climb to a limping, non-symmetric climb. Corresponding analyses are presented for the DTAR and ParkourBot mechanisms. We finally show how the open-loop behavior can be used to traverse more complex terrains by incrementally adding feedback. We are able to achieve climbing inside a chute with wall width changes without the need for precise and fast sensing and control.
Provably Optimal Design of a Brain-Computer Interface

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into movement of the device in order to achieve the most proficient control? This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device, to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a non-biomimetic device, once the subject has engaged in extended practice and learned how to use it. In this thesis, I first recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. Based on this framework, I present a formal definition of the usability of a device under the assumption that the brain acts as an optimal controller. Using ideas from optimal control theory, it can be shown that the optimal, post-learning mapping can be written as the solution of a constrained optimization problem which maximizes this usability. I then derive the optimal mappings for particular cases common to most brain-computer interfaces. Results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, this method provides a blueprint for optimal device design in general control-theoretic contexts. Given the optimal, post-learning mapping, successful implementation of such brain-computer interface depends critically on the subject’s ability to learn how to modulate the neurons controlling the device. However, the subject’s learning process is probably the least understood aspect of the control loop. An effective training schedule should manipulate the difficulty of the task to provide enough information to guide improvement without overwhelming the subject. In this thesis, I introduce a Bayesian framework for modeling the closed-loop BCI learning process that treats the subject as a bandwidth-limited communication channel. I then develop an adaptive algorithm to find the optimal difficulty-schedule for performance improvement. Simulation results demonstrate that this algorithm yields faster learning rates than several other heuristic training schedules, and provides insight into the factors that might affect the learning process.
Data-Driven Robotic Grasping in the Wild

Robotic grasping has seen tremendous advancements in recent years. Yet, the current paradigm of manipulation research is typically some form of table-top manipulation in constrained setups or in simulation. Building general purpose personal robots that can autonomously grasp unknown objects in unstructured environments like homes is an open problem. In this thesis, we explore important directions in scaling data-driven grasping to the diversity and constraints imposed by the real world. We first discuss how we can go beyond picking individual objects in isolation to 6DOF grasping in clutter. Most existing methods train policies on datasets collected in curated settings (in lab or simulation) and hence may not cope with the mismatch in data distribution when deployed in the wild. We build and open-source a low-cost mobile manipulator platform to parallelize data collection in challenging settings like homes and show that policies trained on this data generalize to novel objects in unseen homes. As a result, we also discuss ideas for scaling robot learning with several robots and transferring policies between different hardware. Yet, we hypothesize that visual perception alone is insufficient for robustness and present a self-supervised tactile-based re-grasping framework to close the loop on grasp execution. Lastly, we strive to go beyond robotic pick-and-place and generalize to diverse semantic manipulation tasks. We do so by scaling task-oriented grasping datasets with crowdsourcing and learning from semantic information like knowledge graphs.
Vision with Small Baselines

3D sensing with portable imaging systems is becoming more and more popular in computer vision applications such as autonomous driving, virtual reality, robotics manipulation and surveillance, due to the decreasing expense and size of RGB cameras. Despite the compactness and portability of the small baseline vision systems, it is well-known that the uncertainty in range finding using multiple views and the sensor baselines are inversely related. On the other hand, besides compactness, the small baseline vision system has its unique advantages such as easier correspondence and large overlapping regions across views. The goal of this thesis is to develop computational methods and small baseline imaging systems for 3D sensing of complex scenes in real world conditions. Our design principle is to physically model the scene complexities and specifically infer the uncertainties for the images captured with small baseline setups. With this design principle, we make four contributions. In the first contribution, we propose a two-stage near-light photometric stereo method using a small (6 cm diameter) LED ring. The imaging system is compact compared to traditional photometric stereo systems. In the second contribution, we develop an algorithm to simultaneously estimate the occlusion pattern and depth for thin structures from a focal image stack, which is obtained either by varying the focus/aperture of the lens or computed from a one-shot light field image. As the third contribution, we propose a learning-based method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with small camera baselines across adjacent frames. These depth probability volumes are accumulated over time as more incoming frames are processed sequentially, which effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability. Finally, using a pair of high resolution camera and laser projector, we develop a high spatial resolution Diffuse Optical Tomography system that can detect accurate boundaries and relative depth of heterogeneous structures up to a depth of 8mm below a highly scattering medium such as whole milk. We showcase the application of a small baseline vision system for in-vivo microscale 3D reconstruction of capillary veins and develop a system for real-time analysis of microvascular blood flow for critical care. We believe that the computational methods developed in this thesis would find more applications of compact 3D sensing under challenging conditions.
Matching Theory Under Uncertainty

Traditionally, optimization in computer science has been studied in the full information setting: data is collected, a program is run, and then the output is used. However, the increasing pervasiveness of user-facing applications is increasingly shifting the focus to computation under incomplete information: data is generated continuously by users, who expect their new data to quickly affect the externalized solution. This modern computational paradigm motivates a renewed interest in computation under uncertainty (about the input), including online, dynamic and streaming algorithms. Many problems providing the renewed impetus for studying algorithms under uncertainty come from the field of matching theory—the study of pairing agents/items. Examples abound, arising in disparate applications, from ride-sharing apps, to Internet advertising, to online gaming. This motivates the study of matching theory under uncertainty. Moreover, the study of matching theory has historically played a key role in the development of immensely influential techniques for computation more broadly. An additional motivation for studying matching theory under uncertainty, then, is its potential to provide similar fundamental insights for computation under uncertainty more broadly. In this thesis we answer several longstanding open problems in the area of matching theory under uncertainty, and hint at some methods with potential broader applicability to computation under uncertainty. This again illustrates the pivotal role of matching theory, this time in modern settings. In Part I, we study online algorithms; here, the input is revealed in parts, in some adversarial order, in the form of requests, to which we must respond immediately and irrevocably. In Part II, we study online algorithms under structural and stochastic assumptions which are motivated by information about practical inputs of interest, allowing for better guarantees than possible for worst-case inputs. Finally, in Part III, we study dynamic algorithms, where the input is constantly changing, and the algorithm’s choices, while not irrevocable, must be quick; in the same part, we study streaming algorithms, motivated by big-data applications, where choices are not irrevocable, but are restricted to only using a limited amount of memory compared to the (massive) input size.
New Aspects of Beyond Worst-Case Analysis

Traditionally, the theory of algorithms has focused on the analysis of worst-case instances. While this has led to beautiful results and a thorough understanding of a wide variety of problems, there are many problems for which worst-case analysis is not useful for empirical or real-world instances. A rapidly developing line of research, the so-called beyond worst-case analysis of algorithms (BWCA), considers the design and analysis of problems using more realistic models or using natural structural properties. The goals of BWCA are to design realistic models and new theoretical algorithms which perform well in practice, as well as to explain why preexisting algorithms that do well in practice perform better than worst-case analysis suggests. In other words, the goal of BWCA is to bridge the gap between theory and practice. In this thesis, we continue the line of work of BWCA for clustering and distributed learning by making contributions in several areas. Specifically, we focus on three main problems and models. • Clustering is one problem that has benefited greatly from BWCA. The perturbation resilience assumption proposed by Bilu and Linial (2011) states that the optimal clustering does not change under small perturbations to the input distances. We design efficient algorithms that output optimal or near-optimal clusterings for the canonical k-center objective (in both symmetric and asymmetric forms) under perturbation resilience. Our results are tight up to the level of perturbation resilience assumed. Next, we give robust algorithms with guarantees even if only part of the data satisfies the perturbation resilience assumption. • Clustering has many varied applications in practice, and an algorithm may have drastically different performance between two applications. To reconcile this fact, we consider a data-driven approach, in which a clustering application is represented by a distribution over problem instances, and the goal is to find the best algorithm for the (unknown) distribution. This model was first studied theoretically by Gupta and Roughgarden (2016). We define rich, infinite classes of linkage algorithms with dynamic programming, as well as local search algorithms with initialization, generalizing the k-means algorithm. We bound the pseudo-dimension of these classes, which leads to computationaland sampleefficient meta-algorithms for some of the algorithm classes we study. • As datasets become larger and more complex, distributed learning becomes more prevalent. Motivated by the fact that similar datapoints often belong to the same or similar classes, we propose data-dependent dispatching that takes advantage of such structure. We provide new dispatching algorithms which cast the problem as clustering with important balance and fault-tolerance conditions. Finally, we design general and robust distributed clustering algorithms.
Considerate Systems

Recent technological advances have witnessed the rapid encroachment of computing systems into our social spaces. Their acceptance in these social spaces by other occupants, however, might be mostly contingent on their social appropriateness. Notions of social appropriateness might seem vague but even people who don’t act on this commonsense knowledge, and accord to social norms, can sometimes find themselves ostracized from society. It is reflected in behavior that supports a sense of successful engagement and connection. Such behavior communicates a desire to be accepted and a willingness to engage, as opposed to inappropriateness that conveys indifference, rejection or even danger. As social actors, how can systems improve their interactions with us in order to better succeed at their tasks? Perhaps, more interestingly, how might they even improve our communications with each other? In this thesis we describe a framework to identify opportunities to design systems that can begin to act appropriately in social settings, which we call Considerate Systems. It includes a design process and guidelines, which allows an interaction to be viewed from the perspectives of the user, system and task. It also includes an architecture that guides the addition of productive social responses to interactive systems. We demonstrate the utility of this framework by exploring two types of scenarios that impact social interactions in contrasting ways. Remote interactions (such as on a conference call) suffer from an impinging of social cues that people rely on while communicating. On the other hand, situated multitasking interactions (such as texting while driving) can easily overwhelm users and detract from their performance. The framework is applied towards the design of autonomous agents tackling problems endemic to such scenarios. We evaluate their success with respect to specific scenario goals. We conclude by noting that while the challenges of instilling computing systems with a sense of appropriateness seem daunting, our productive use of systems can be enhanced with them.
HSOA of Cardiology: Study & Research Effect of Intra-Coronary (IC) Tirofiban Following Aspiration Thrombectomy on Infarct Size, in Patients with Large Anterior STEMI undergoing Primary PCI

Effect of (IC) Following Aspiration on Infarct Size, in Patients with Large Anterior STEMI undergoing Primary PCI. J Cardiol Stud Res 5: 015. Abstract Background/Aim: Thrombus embolization during Percutaneous Coronary Inter- vention (PCI) in STEMI results in sub-optimal myocardial perfusion and increased infarct size. This study aimed to evaluate effect of Intra-Coronary (IC) delivery of bolus Tirofiban following aspiration thrombectomy on reduction of infarct size using cardiac Magnetic Resonance (cMR) in patients with large anterior STEMI undergoing primary PCI. A Prospective single-blinded randomized controlled trial of 100 patients with large anterior STEMI was screened at 2 sites in one country (Egypt). Aspiration thrombectomy was performed in all pa - tients using a 6 F aspiration catheter. Patients were randomized to IC Tirofiban (Study group) and no IC Tirofiban (control group). To ensure high intra-thrombus drug concentrations, Tirofiban was ad ministered locally at the site of the infarct lesion via the aspiration catheter after flushing of the aspiration catheter well. Patients randomized to IC Tirofiban compared with no IC Tiro - fiban had a significant reduction of infarct size at 30 days (median, vs (median, 49.599 - P value = 0.002. There is no significant dif ference in MACCE at 90 days between patients received IC Tirofiban and patients who (P value = 0.723). Conclusion In patients with large anterior STEMI presenting early after symp - tom onset and undergoing primary PCI, infarct size at 30 days was significantly reduced by bolus intracoronary Tirofiban delivered to the infarct lesion site followed aspiration thrombectomy but not by manual aspiration thrombectomy only.
First comprehensive sanitary report of citrus viruses and viroids in Uruguay

24 Citrus is the highest-value fruit crop in terms of international trade. However, citrus species are 25 susceptible to several diseases caused by different pathogens which directly cause a decrease 26 in production leading to economic losses. In the last half-century, the citrus industry in Uruguay 27 has had a strong socio-economic impact and is also constantly evolving to stay competitive in 28 world markets, by introduction of new varieties and improvement of production practices to 29 obtain high yielding orchards. Nevertheless, despite the existence since 2014 of the Uruguayan 30 National Citrus Sanitary and Certification Program, scarce information is available regarding 31 the virus and viroid status of commercial citrus in Uruguay. The incidence of citrus tristeza 32 virus (CTV), citrus psorosis virus (CPsV), satsuma dwarf virus (SDV), citrus exocortis viroid 33 (CEVd), hop stunt viroid (HSVd), citrus dwarfing viroid (CDVd), citrus bark cracking viroid 34 (CBCVd) and citrus bent leaf viroid (CBLVd) was investigated in this study, as well as CTV 35 genotypes prevalent in the country. Molecular diagnostic assays were used to test 1175 samples 36 including Valencia and Navel sweet oranges, Mandarin hybrids, Clementines and lemons, 37 which were randomly collected from seven citrus-producing provinces. Only 6% of the 38 samples were negative for the pathogens screened, while 93% of them were CTV positive. 39 SDV, CBLVd and CBCVd were not detected. Co-infections were frequently detected, finding 40 plants with up to four simultaneous pathogens, including CTV in all co-infected plants. This is 41 the first comprehensive survey of several citrus-infecting viruses and viroids in Uruguay, as 42 well as a determination of the CTV genotypes prevalent in the country. 43 detection
T Regulatory Cells + CD25 + Capacity and Expression of FOXP3 in CD4 Ligand Flagellin Enhances the Suppressive T Cells Express TLR5 and Its + Human CD4

Germline encoded pattern recognition receptors, such as TLRs, provide a critical link between the innate and adaptive immune systems. There is also evidence to suggest that pathogen-associated molecular patterns may have the capacity to modulate immune responses via direct effects on CD4 (cid:1) T cells. Given the key role of both CD4 (cid:1) CD25 (cid:1) T regulatory (Treg) cells and the TLR5 ligand ﬂagellin in regulating mucosal immune responses, we investigated whether TLR5 may directly inﬂuence T cell function. We found that both human CD4 (cid:1) CD25 (cid:1) Treg and CD4 (cid:1) CD25 (cid:2) T cells express TLR5 at levels comparable to those on monocytes and dendritic cells. Costimulation of effector T cells with anti-CD3 and ﬂagellin resulted in enhanced proliferation and production of IL-2, at levels equivalent to those achieved by costimulation with CD28. In contrast, costimulation with ﬂagellin did not break the hyporesponsiveness of CD4 (cid:1) CD25 (cid:1) Treg cells, but rather, potently increased their suppressive capacity and enhanced expression of FOXP3. These observations suggest that, in addition to their APC-mediated indirect effects, TLR ligands have the capacity to directly regulate T cell responses and modulate the suppressive activity of Treg cells. The Journal of Immunology, 2005, 175: 8051–8059. and T
A Counter-Example-Driven Approach to the Bounded Synthesis of Synchronization Code for Distributed Algorithms

In this paper, we present an approach to automatically synthesize synchronization code for distributed programs assuming a multi-thread shared-variables model of computation, common in object-oriented programming languages like JAVA. Our method combines SAT solving over logical theories written in First-Order Logic with Transitive Closure, and symbolic model checking. Intuitively, our approach starts by using SAT solving to enumerate potential implementations of the (local) processes, and checking whether their concurrent composition satisﬁes the required global properties, until a valid synchronization code is obtained. This latter step is performed using symbolic model checking, and the obtained counterexamples are used to incrementally reﬁne the SAT-based search for local process implementations. We developed a prototype of our approach and applied it to well-known case studies of distributed algorithms.