Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision Entry Navigation Dead-Reckoning Error Analysis: Theoretical Foundations of the Discrete-Time Case

A linear covariance analysis strategy is developed for application to hypersonic atmospheric planetary entry where the only available navigation data is provided by strapdown inertial measurement units. The navigation scenario considered encompasses the so-called dead-reckoning navigation, wherein the inertial measurement unit provides measures of the change in velocity and the change in attitude at discrete times. These measurements are used to propagate an initial state estimate forward in time. The question that is addressed is quantifying the accuracy of the state estimate using dead-reckoning during a typical Mars atmospheric entry. The inertial measurement data is assumed to be corrupted with random noise, random constant biases, misalignment errors, and scale factor errors. The location of the inertial measurement unit with respect to the spacecraft’s center of mass is also considered to contain uncertainty.
Fair and Efficient Division of Indivisibles

Finding fair allocations is a fundamental problem in algorithmic game theory. With divisible resources, it is commonly phrased as the cake cutting problem and has been extensively studied; see [71] for a summary. Here, a division of a cake that gave one piece to each of n agents was termed fair if it ensured properties like (a) envy-freeness, meaning every agent values her own piece more than those allocated to other agents, and (b) proportionality, meaning every agent values her piece at least 1/n fraction of her total value for the cake. When there are two agents, the simple cut and choose protocol is known to work since the biblical era, where one agent cuts the cake into two pieces and the other agent gets to choose first. Recent years have seen a surge of works on the fair division of indivisible items, like school/course seats, assets and liabilities, and computing resources on networks, due to their wide applications [73, 21, 78, 65, 36, 23, 45]. A simple example of allocating a single indivisible item among two agents shows that both envy-free and proportional allocations may not exist here. This led to the design of measures of fairness for the indivisible item setting. Today, several measures are known and have been extensively studied (for instance, [66] introduced the Nash welfare, and [23] introduced the maximin share notion and some relaxations of envy-freeness). The problem of finding allocations that are fair and additionally economically efficient as well was then studied, either using measures that balanced both objectives, like the Nash welfare [See Section 1.1 for a review of existing literature], or by adding efficiency constraints like Pareto optimality (PO) along with known fairness measures. For instance, [17] study EF1, a common relaxation of envy-freeness called envy-free up to (the removal of) one desirable item (from the envied bundle) with PO for the additive valuations setting1, [9, 81, 26, 72] study relaxations of envy-freeness with PO for various special cases of additive valuations, [9] study proportionality with PO. Additionally, [8] discuss EF1 solutions that satisfy the efficiency notions of utilitarian maximality and rank maximality. The focus of this article is the problem of finding fair and efficient allocations of indivisible items using two objectives: the Nash welfare, and the Maximin share and Pareto optimality. To formally describe these measures, we introduce the following notation. Let (N ,M,V) denote an instance where N is a set of n agents,M is a set of m indivisible items, and V is the set of n monotonically non-decreasing valuation functions V = {vi | vi : S ⊆ M → R, i ∈ N}. If all the items in M are non-negatively valued by each agent, the items are called goods and M is called a goods manna. If the items are all non-positively valued, M is called a chores manna, and every item is called a
Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation

Self-occlusion is challenging for cloth manipulation, as it makes it difficult to estimate the full state of the cloth. Ideally, a robot trying to unfold a crumpled or folded cloth should be able to reason about the cloth's occluded regions. We leverage recent advances in pose estimation for cloth to build a system that uses explicit occlusion reasoning to unfold a crumpled cloth. Specifically, we first learn a model to reconstruct the mesh of the cloth. However, the model will likely have errors due to the complexities of the cloth configurations and due to ambiguities from occlusions. Our main insight is that we can further refine the predicted reconstruction by performing test-time finetuning with self-supervised losses. The obtained reconstructed mesh allows us to use a mesh-based dynamics model for planning while reasoning about occlusions. We evaluate our system both on cloth flattening as well as on cloth canonicalization, in which the objective is to manipulate the cloth into a canonical pose. Our experiments show that our method significantly outperforms prior methods that do not explicitly account for occlusions or perform test-time optimization. Videos and visualizations can be found on our $\href{https://sites.google. com/view/occlusion-reason/home}{\text{project website}}. $
Coupling and Coupling Compromises in Supplier Factories’ Responses to Worker Activism

Many companies have adopted corporate social responsibility (CSR) policies in response to activist pressure, but ensuring the implementation of these policies is challenging. Recognizing the paucity of research on the effect of contentious activism on companies’ coupling of CSR policies and practices in the private politics and (de)coupling literatures, we posit that companies engage in “coupling compromises” when faced with such institutional pressure—improving their practices and more tightly coupling them with CSR policies in the domain contested by activists, but relaxing the coupling of policy and practice in competing CSR domains. Furthermore, we theorize that the nature and extent of coupling compromises can be explained by the interaction of activism with organizational structures that construct managerial perceptions of issue salience and internal frictions to change. We test our theory in the context of global supply chain factories’ compliance with CSR policies on working conditions when they face local worker activism. Analyzing 3,495 audits of 2,352 factories in 114 Chinese cities from 2012 to 2015, we find that worker activism over wages-and-benefits issues pushes factories to improve their wages-and-benefits practices and couple them more tightly with CSR policies, but these factories concurrently loosen the coupling between policy and practice in the domain of occupational health and safety. Both effects are stronger in factories with organizational structures that foreground the salience of wages-and-benefits issues and mitigate friction to changing organizational practices. These findings make significant contributions to the literatures on private politics, (de)coupling, and global supply chain labor practices. These results indicate that, for example, compared to factories in cities in which no collective action events occurred between the factories’ successive audits, factories in cities in which three large events occurred exhibit a 2-point increase in wages & benefits score and a nearly 1-point decrease in health & safety score , resulting in a 3-point increase in the performance gap between the two scores.
Autonomously Untangling Long Cables

Cables are ubiquitous in many settings and it is often useful to untangle them. However, cables are prone to self-occlusions and knots, making them difficult to perceive and manipulate. The challenge increases with cable length: long cables require more complex slack management to facilitate observability and reachability. In this paper, we focus on autonomously untangling cables up to 3 meters in length using a bilateral robot. We develop RGBD perception and motion primitives to efficiently untangle long cables and novel gripper jaws specialized for this task. We present Sliding and Grasping for Tangle Manipulation (SGTM), an algorithm that composes these primitives to iteratively untangle cables with success rates of 67% on isolated overhand and figure-eight knots and 50% on more complex configurations. Supplementary material, visualizations, and videos can be found at https://sites.google. com/view/rss-2022-untangling/home.
Comparative characteristics of gap filling methods in high-frequency data of micrometeorological measurements

This article is devoted to one of the most complex technical aspects of the primary processing of micrometeorological data. The article contains a description of basic algorithms for restoring missing values in time series of data. The gap filling methods implemented by the authors on the basis of the thus described algorithms are tested using an example of filling-in generated gaps in series of temperature and vertical wind speed. The series of high-frequency measurements of an acoustic anemometer installed on the micrometeorological tower of the meteorological observatory (MO) of Moscow State University (MSU) are used as test data. As a result of this review of gap-filling methods, the effectiveness of various methods for filling-in missing values is assessed.
Daily streamflow forecasting using hybrid long short-term memory model

Characterized by heterogeneity, complexity and non-stationary, streamflow forecasting has always been a challenge in hydrological sciences. In this study, a multiscale wavelet decomposition method with long short-term memory model (WLSTM) is developed to handle the daily streamflow forecasting. Discrete wavelet transform (DWT) is employed to extract multiscale features, which are then simulated by long short-term memory models (LSTMs), respectively. The outputs of the different scales LSTMs are finally reconstructed toward the forecasting results. Seven years daily streamflow sequences of three tributaries and one mainstream in the upper reaches of the Yangtze River are investigated by the WLSTM models. For comparison, standard LSTM, and traditional ANN are chosen for the same streamflow forecasting task. Experimental results show that the proposed hybrid model is better than other comparison models in terms of evaluation indicators. Considering that large floods often occur in the Yangtze River Basin, the performance of flood forecasting is also analyzed and the result shows that forecasting errors of WLSTM are more concentrated, which means WLSTM outperforms traditional ANN and LSTM for the extreme flood forecasting.
Development of Land Used Intensity-Traffic Interaction Model for Bandar Lampung City, Indonesia

Social history has shown that a city plays an important role in meeting human needs such as housing, service facilities, entertainment areas, and transportation networks. Therefore, the interaction between demand and infrastructure creates a traffic flow. Previous studies show that traffic flow is strongly correlated with land use types’ scale and operational characteristics. In addition, traffic volume can represent logistics distribution, which is one of the parameters to assess regional economic conditions. This condition prompted researchers to research to develop an interaction model between land-use intensity and traffic. The study was conducted in Bandar Lampung City, Indonesia, the economic center of Lampung Province, Indonesia. The methodology used in this study is the multiple linear regression analysis for origin-destination matrix. This analysis was performed using 23 parameters that could influence the model, but we try to find the most significant influence variables in this study. The process of evaluating some statistical parameters is considered as decision parameters. Finally, the analysis results show a strong correlation between land use and traffic volume in the city of Bandar Lampung, Indonesia the best model y = 62,3 + 2,275 x16 + 1,348 x20 with x16 is store building area, and x20 is the number of schools.
Study on application of color filters in vision system of hot forgings

In order to improve the quality and efficiency of forging process, it needs to execute on-line dimensional measurement of the forgings. In the paper, a laboratory color vision measuring system is set up and the combination of digital and physical filtering is adopted to improve the image quality based on the radiation characteristics of high-temperature forgings. The digital filtering technology is a kind of image processing methods, in which the R component of the forging image is removed. While, the physical filtering technology is achieved by optical filters installed in front of the CCD, in which strong self-emitted radiation from the hot parts can be filtered out. In order to evaluate the image quality, the image contrast is applied, which is generally defined as the difference value between average gray scale of object region and that of background region. In the experiments, image contrast derived with filters at different sample points set from 800°C to 1200°C is compared to determine the optimal scheme of filters to be selected. Results of experiments indicate that the application effect of filters is dissimilar when the forging is in different temperature ranges. Through comparison, the optimal selection scheme of filters is determined to derive high quality image of forgings at different temperatures, which lays a solid foundation for the subsequent image processing.
Lipoprotein Kolesteroel ve Ortalama Trombosit Arter Stenozunun Ciddiyeti ile İlişkili Olabilir Monocyte Count to High-Density Lipoprotein Cholesterol Ratio and Mean Platelet Volume May Be Related to the Severity of Carotid Artery Stenosis

has been identified as a predictor of cardiovascular events in patients. In the light of these findings, the goal of this study was to evaluate the potential association of MPV and MHR with the severity of CAD.Material-Method: The patients were divided into three groups: CAD≥ 60% (40 patients), CAD <60% (40 patients) and no carotid atherosclerotic disease (40 individuals). Patients' demographic characteristics, clinical features and laboratory findings were compared between the groups.Results: MPV and MHR levels were lower in the control group than CAD <60 group and lower in CAD <60 group than CAD ≥60 group (p <0.001, p <0.001). Higher MPV (OR= 6.050; 95% CI: 2.946-12.427; p <0.001) and MHR (OR= 1.107; 95% CI: 1.045-1.159; p <0.001) levels were detected as an independent predictor for ≥60% carotid artery stenosis after multiple linear regression analysis. In the correlation analysis, a positive correlation was found between MPV and the rate of carotid artery stenosis (p<0.001, r=0.680). Conclusion: In our study, a potential relationship between MPV and MHR with carotid artery disease was determined. Our study may make contributions to understanding the pathophysiology of carotid artery atherosclerosis and developing treatments for it. cerebral arteries. Technical parameters related to the brain neck CT angiography examination: Starting from the bifurcation level of the truncus pulmonalis in the caudal to the vertex in the superior, 0.6 mm slice thickness, 100 kV and effective milliamper seconds were obtained with mAs 170 mAs, FOV (Field of View) at an average of 190 mm and pitch value of 0.8.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

The bare-metal cloud service is a type of IaaS (Infrastructure as a Service) that offers dedicated server hardware to customers along with access to other shared infrastructure in the cloud, including network and storage. This paper presents our experiences in designing, implementing, and deploying Bluebird, the high-performance network virtualization system for the bare-metal cloud service on Azure. Bluebird’s data plane is built using high-performance programmable switch ASICs. This design allows us to ensure the high performance, scale, and custom forwarding capabilities necessary for network virtualization on Azure. Bluebird employs a few well-established technical principles in the control plane that ensure scalability and high availability, including route caching, device abstraction, and architectural decoupling of switch-local agents from a remote controller. The Bluebird system has been running on Azure for more than two years. During this time, it has served thousands of bare-metal tenant nodes and delivered full line-rate NIC speed of bare-metal servers of up to 100Gb/s while ensuring less than 1µs of maximum latency at each Bluebird-enabled SDN switch. We share our experiences of running bare-metal services on Azure, along with the P4 data plane program used in the Bluebird-enabled switches.
Design optimization of a parallel manipulator for otological surgery

—The use of an endoscope in otological surgery as compared to using a microscope provides many beneﬁts in terms of visibility and access to the operating region. The disadvantage of using an endoscope is that it has to be handled by the surgeon during the surgery. A novel parallel kine- matic mechanism has been proposed recently for manipulating the endoscope during surgery. This paper details the design requirements considered for the optimization of the process, after consulting surgeons with various expertise. The paper further presents the different evaluation strategies that can be used to optimize the design parameters as per the needs of the application. The contribution of the presented work is the implementation of the surgeons’ requirements into an objective function and the optimization of a proposed architecture of a parallel kinematic mechanism suitable for otological surgery.
The Cayley Semigroup Membership Problem

The Cayley semigroup membership problem asks, given a multiplication table representing a semigroup 𝑆 , a subset 𝑋 of 𝑆 and an element 𝑡 of 𝑆 , whether 𝑡 can be expressed as a product of elements of 𝑋 . It is well-known that this problem is NL -complete under AC 0 -reductions. For groups, the problem can be solved in deterministic Logspace . This raised the question of determining the exact complexity of this variant. Barrington, Kadau, Lange and McKenzie showed that for Abelian groups and for certain solvable groups, the problem is contained in the complexity class FOLL (polynomial-size, 𝑂 ( log log 𝑛 ) -depth circuits) and they concluded that these variants are not hard, under AC 0 reductions, for any complexity class containing the Parity language. The more general case of arbitrary groups remained open. In this article, we apply results by Babai and Szemerédi directly to this setting to show that the problem is solvable in qAC 0 (quasi-polynomial size circuits of constant depth with unbounded fan-in). We prove a similar result for commutative semigroups. Combined with the Yao–Håstad circuit lower bound, it follows immediately that Cayley semigroup membership for groups and Cayley semigroup membership for commutative semigroups are not hard, under AC 0 reductions, for any class containing Parity. Moreover, we prove that NL -completeness already holds for the classes of 0-simple semigroups and nilpotent semigroups. Together with our results on groups and commutative semigroups, we prove the existence of a natural class of ﬁnite semigroups that generates a variety of ﬁnite semigroups with NL -complete Cayley semigroup membership, while the Cayley semigroup membership problem for the class itself is not NL -hard. We also discuss applications of our technique to FOLL and describe varieties for which the Cayley semigroup membership problem is in AC 0 .
A GENT , DO YOU SEE IT NOW ? SYSTEMATIC GENERALI - SATION IN DEEP REINFORCEMENT LEARNING

Systematic generalisation, i.e., the algebraic capacity to understand and execute unseen tasks by combining already known primitives, is one of the most desirable features for a computational model. Good adaptation to novel tasks in open-ended settings relies heavily on the ability of agents to reuse their past experience and recombine meaningful learning pieces to tackle new goals. In this work, we analyse how the architecture of convolutional layers impacts on the performance of autonomous agents when generalising to zero-shot, unseen tasks while executing human instructions. Our findings suggest that the convolutional architecture that is correctly suited to the environment the agent will interact with, may be of greater importance than having a generic convolutional network trained in the given environment.
DialSummEval: Revisiting Summarization Evaluation for Dialogues

Dialogue summarization is receiving increasing attention from researchers due to its extraordinary difficulty and unique application value. We observe that current dialogue summarization models have flaws that may not be well exposed by frequently used metrics such as ROUGE. In our paper, we re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models for the first time. Some noteworthy trends which are different from the conventional summarization tasks are identified. We will release DialSummEval, a multi-faceted dataset of human judgments containing the outputs of 14 models on SAMSum.
Seismic Event Detection from Volcanic Regions using Feature Extraction and Deep Learning

Volcanic eruptions are usually accompanied by seismic events with a variety of temporal signatures: volcano-tectonic earthquakes, low-frequency earthquakes, tremors, and explosions. The detection of these events in seismograph data has been done manually for several decades now. This process produces accurate results but is time and labor-intensive. A variety of machine-learning methods have been developed over the past decade to automate elements of this task. Few of these methods incorporate expert knowledge about the process; rather, they train a model with a large labeled data set and then simply work with the raw seismograph data. Our goal is to build and train a model that combines supervised machine learning and expert knowledge to aid in this process. The main technical challenge here is feature engineering: identification of task-specific, salient, higher-level attributes that can be extracted from the raw data and used as inputs to the model. A well-chosen feature set can greatly improve both training and deployment. Through this research, we describe a neural network that identifies the arrival times of the P and S waves of small earthquakes preceding the 2012 New Zealand Mount Tongariro/Te Maari craters eruption. We utilize seismic waveform and analyst-reviewed phase arrival data, which are available through GeoNet New Zealand. The initial implementation involves training a neural network that contains convolutional and fully connected layers on the raw waveform data. The subsequent implementation involves training a similar neural network model, without the convolutional layers, using a set of engineered features. We compare the performance of both models to determine the efficiency and effectiveness of using a feature set as opposed to using raw waveform data.