Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees. These features are used to measure the word-and phrase-wise similarity between two sentences. Since sentences may be of arbitrary length, the resulting matrix of similarity measures is of variable size. We introduce a novel dynamic pooling layer which computes a fixed-sized representation from the variable-sized matrices. The pooled representation is then used as input to a classifier. Our method outperforms other state-of-the-art approaches on the challenging MSRP paraphrase corpus.

With its army short of ammunition and troops to break the deadlock on the battlefield, Ukraine has increasingly taken the fight behind Russian lines, attacking warships, railways and airfields in an attempt to diminish Moscow's military operations. Most recently, that campaign has focused on oil infrastructure, hitting refineries deep in Russian territory and driving home the country's vulnerability to such attacks. On Tuesday and Wednesday, Ukrainian drones hit four Russian refineries, officials on both sides said, adding to a series of recent attacks that have set fire to depots, fuel tanks and other oil infrastructure across Russia. Since the beginning of the year, Ukraine has claimed responsibility for nearly a dozen such assaults, and local Russian authorities have reported five more.

Various macroeconomic and institutional factors hinder FDI inflows, including corruption, trade openness, access to finance, and political instability. Existing research mostly focuses on country-level data, with limited exploration of firm-level data, especially in developing countries. Recognizing this gap, recent calls for research emphasize the need for qualitative data analysis to delve into FDI determinants, particularly at the regional level. This paper proposes a novel methodology, based on text mining and social network analysis, to get information from more than 167,000 online news articles to quantify regional-level (sub-national) attributes affecting FDI ownership in African companies. Our analysis extends information on obstacles to industrial development as mapped by the World Bank Enterprise Surveys. Findings suggest that regional (sub-national) structural and institutional characteristics can play an important role in determining foreign ownership.

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

In recent years, atmospheric research has received increasing attention from environmental experts and the public because atmospheric phenomena such as El Nino, global warming, ozone depletion, and drought that may have negative effects on the Earth's climate and ecosystem are occurring more often (Walther et al. 2002; Karl and Trenberth 2003; Trenberth et al. 2014). In order to model the status quo and predict the trend of atmospheric phenomena and events, researchers need to retrieve data from various relevant domains, such as chemical components of aerosols and gases, the terrestrial surface, energy consumption, the hydrosphere, the biosphere, etc. (Schneider, 2006; Fowler et al., 2009; Guilyardi et al, 2009; Ramanathan et al., 2011; Katul et al., 2012). In complex earth system modeling, the data and services for atmospheric study present the characteristics of being distributed, collaborative and adaptive (Plale et al., 2006). The massive volume, rapid velocity and wide variety of data has led to a new era of atmospheric research that consists of accessing and integrating big data from distributed sources, conducting collaborative analysis in an interactive way, providing intelligent services for data management, and integration and visualization to foster discovery of hidden or new knowledge. One of the most important ways to support these activities is to establish a national or international spatial data infrastructure and geospatial cyberinfrastructure on which the data and computational resources can be easily shared, the spatial analysis tool can be executed on-the-fly and the scientific results can be effectively visualized (Yang et al., 2008; Li et al., 2011). Technically, a geospatial cyberinfrastructure (GCI) is an architecture that effectively utilizes geo-referenced data to connect people, information and computers based on the standardized data access protocols, high speed internet, high-performance computing facilities (HPC) and service-oriented data management (Yang et al., 2010). Since the concept's official introduction by the National Science Foundation (NSF) in its 2003 blue ribbon report, cyberinfrastructure research has attracted much attention from the atmospheric science domain because of its promise of bringing paradigm change for

This paper describes the hardware design and flight demonstration of a small quadrotor with imaging sensors for urban mapping, hazard avoidance, and target tracking research. The vehicle is equipped with five cameras, including two pairs of fisheye stereo cameras that enable a nearly omnidirectional view and a two-axis gimbaled camera. An onboard NVIDIA Jetson Orin Nano computer running the Robot Operating System software is used for data collection. An autonomous tracking behavior was implemented to coordinate the motion of the quadrotor and gimbaled camera to track a moving GPS coordinate. The data collection system was demonstrated through a flight test that tracked a moving GPS-tagged vehicle through a series of roads and parking lots. A map of the environment was reconstructed from the collected images using the Direct Sparse Odometry (DSO) algorithm. The performance of the quadrotor was also characterized by acoustic noise, communication range, battery voltage in hover, and maximum speed tests.

The sheer number of nodes continues to increase in todays supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of todays clusters are limited by the slowest nodes, it more important than ever to identify slow nodes, improve their performance if it can be done, and assure minimal usage of slower nodes during performance critical runs. This is an ongoing maintenance task that occurs on a regular basis and, therefore, it is important to minimize the impact upon its users by assessing and addressing slow performing nodes and mitigating their consequences while minimizing down time. These issues can be solved, in large part, through a systematic application of fast running hardware assessment tests, the application of Machine Learning, and making use of performance data to increase efficiency of large clusters. Proxy applications utilizing both MPI and OpenMP were developed to produce data as a substitute for long runtime applications to evaluate node performance. Machine learning is applied to identify underperforming nodes, and policies are being discussed to both minimize the impact of underperforming nodes and increase the efficiency of the system. In this paper, I will describe the process used to produce quickly performing proxy tests, consider various methods to isolate the outliers, and produce ordered lists for use in scheduling to accomplish this task.

Pre-training image representations from the raw text about images enables zero-shot vision transfer to downstream tasks. Through pre-training on millions of samples collected from the internet, multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results that often reach competitiveness with fully supervised methods without the need for task-specific training. Besides the encouraging performance on classification accuracy, it is reported that these models close the robustness gap by matching the performance of supervised models trained on ImageNet under natural distribution shift. Because robustness is critical to real-world applications, especially safety-critical ones, in this paper, we present a comprehensive evaluation based on a large-scale robustness benchmark covering 7 natural, 3 synthetic distribution shifts, and 11 adversarial attacks. We use CLIP as a pilot study. We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark, especially under synthetic distribution shift and adversarial attacks. Furthermore, data overlap analysis suggests that the observed robustness under natural distribution shifts could be attributed, at least in part, to data overlap. In summary, our evaluation shows a comprehensive evaluation of robustness is necessary; and there is a significant need to improve the robustness of zero-shot multimodal models.

Open information extraction (OIE) aims to extract surface relations and their corresponding arguments from natural language text, irrespective of domain. This paper presents an innovative OIE model, APRCOIE, tailored for Chinese text. Diverging from previous models, our model generates extraction patterns autonomously. The model defines a new pattern form for Chinese OIE and proposes an automated pattern generation methodology. In that way, the model can handle a wide array of complex and diverse Chinese grammatical phenomena. We design a preliminary filter based on tensor computing to conduct the extraction procedure efficiently. To train the model, we manually annotated a large-scale Chinese OIE dataset. In the comparative evaluation, we demonstrate that APRCOIE outperforms state-of-the-art Chinese OIE models and significantly expands the boundaries of achievable OIE performance. The code of APRCOIE and the annotated dataset are released on GitHub (https://github.com/jialin666/APRCOIE_v1)

Programming by demonstration (PbD) is a simple and efficient way to program robots without explicit robot programming. PbD enables unskilled operators to easily demonstrate and guide different robots to execute task. In this paper we present comparison of demonstration methods with comprehensive user study. Each participant had to demonstrate drawing simple pattern with human demonstration using virtual marker and kinesthetic teaching with robot manipulator. To evaluate differences between demonstration methods, we conducted user study with 24 participants which filled out NASA raw task load index (rTLX) and system usability scale (SUS). We also evaluated similarity of the executed trajectories to measure difference between demonstrated and ideal trajectory. We concluded study with finding that human demonstration using a virtual marker is on average 8 times faster, superior in terms of quality and imposes 2 times less overall workload than kinesthetic teaching.