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This paper introduces the concept of Language- Guided World Models (LWMs)—probabilistic models that can simulate environments by read- ing texts. Agents equipped with these models provide humans with more extensive and effi- cient control, allowing them to simultaneously alter agent behaviors in multiple tasks via nat- ural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our exper- iments reveal the lack of generalizability of the state-of-the-art Transformer model, as it of- fers marginal improvements in simulation qual- ity over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the perfor- mance of a model with an oracle semantic pars- ing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to re- vise plans based on their language feedback. 

In this paper, we introduce the Warehouse Augmented Reality Program (WARP), its functionality, practicality, and potential use cases in education. We build this application on the backbone of WebXR. Using this application programming interface (API), we create an interactive web tool that displays a life-sized warehouse in augmented reality (AR) in front of users that can be viewed on a smartphone or a tablet. AR is a technology that displays virtual objects in the real world on a digital device’s screen, allowing users to interact with virtual objects and locations while moving about a real-world environment. This tool can enhance warehousing education by making it immersive and more interactive. In addition, the tool can make warehousing operations more efficient and warehouse design less costly. We highlight how our tool can be applicable and beneficial to education and industry. We demonstrate how this tool can be integrated into a problem-based learning (PBL) assignment about warehouse layout design and order picking. The PBL activity involves comparing two different warehouse layouts (fishbone and traditional) by completing a set of order picking tasks in AR warehouse environments. The task is to perform single item picking over thirty orders and comparing the average order picking time per layout. Then, we use the results of these human subject experiments for validating the realism of the warehouse layouts generated by the tool by comparing the empirical completion times with the analytical results from the literature. We also administer a system usability scale (SUS) survey and collect feedback from industry experts. 

ABSTRACT Climate change is the most serious challenge facing humanity. Microbes produce and consume three major greenhouse gases—carbon dioxide, methane, and nitrous oxide—and some microbes cause human, animal, and plant diseases that can be exacerbated by climate change. Hence, microbial research is needed to help ameliorate the warming trajectory and cascading effects resulting from heat, drought, and severe storms. We present a brief summary of what is known about microbial responses to climate change in three major ecosystems: terrestrial, ocean, and urban. We also offer suggestions for new research directions to reduce microbial greenhouse gases and mitigate the pathogenic impacts of microbes. These include performing more controlled studies on the climate impact on microbial processes, system interdependencies, and responses to human interventions, using microbes and their carbon and nitrogen transformations for useful stable products, improving microbial process data for climate models, and taking the One Health approach to study microbes and climate change. 

Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information. 

Abstract Data collected so far by the Pierre Auger Observatory have enabled major advances in ultra-high energy cosmic ray physics and demonstrated that improved determination of masses of primary cosmic-ray particles, preferably on an event-by-event basis, is necessary for understanding their origin and nature. Improvement in primary mass measurements was the main motivation for the upgrade of the Pierre Auger Observatory, called AugerPrime. As part of this upgrade, scintillator detectors are added to the existing water-Cherenkov surface detector stations. By making use of the differences in detector response to the electromagnetic particles and muons between scintillator and water-Cherenkov detectors, the electromagnetic and muonic components of cosmic-ray air showers can be disentangled. Since the muonic component is sensitive to the primary mass, such combination of detectors provides a powerful way to improve primary mass composition measurements over the original Auger surface detector design. In this paper, the so-called Scintillator Surface Detectors are discussed, including their design characteristics, production process, testing procedure and deployment in the field. 

Photosynthesis can be challenging for instructors to teach and uninteresting for students to learn, but this shouldn't be the case. An activity developed by middle-school educators and university scientists lets students see how red light emitted from sunlit plants is captured by satellites to measure global photosynthesis. In plants, most of the absorbed light energy is channeled into photosynthesis, and the tiny amount that is emitted as red fluorescence is not visible by naked eye but is detectable by satellites. When chlorophyll is removed from plants into a solution – uncoupled from the photosynthetic apparatus – chlorophyll still is green and absorbs light, but the absorbed light energy has nowhere to go, and a large red glow is visible. In a readily accessible 1-hour middle-school classroom activity, students extract chlorophyll from spinach using rubbing alcohol (91% isopropyl alcohol) and then observe the abundant red fluorescence upon illumination with a flashlight. This simple observation of the red glow (fluorescence) from chlorophyll provides a terrific anchor for teaching photosynthesis in a biological, agricultural and global ecology context, thereby inspiring students to better appreciate the fascinating world of plants. 

Abstract The modulation of low-energy galactic cosmic rays reflects interplanetary magnetic field variations and can provide useful information on solar activity. An array of ground-surface detectors can reveal the secondary particles, which originate from the interaction of cosmic rays with the atmosphere. In this work, we present an investigation of the low-threshold rate (scaler) time series recorded in 16 yr of operation by the Pierre Auger Observatory surface detectors in Malargüe, Argentina. Through an advanced spectral analysis, we detected highly statistically significant variations in the time series with periods ranging from the decadal to the daily scale. We investigate their origin, revealing a direct connection with solar variability. Thanks to their intrinsic very low noise level, the Auger scalers allow a thorough and detailed investigation of the galactic cosmic-ray flux variations in the heliosphere at different timescales and can, therefore, be considered a new proxy of solar variability. 

Abstract Ultrahigh-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 2022 December 31, with a total exposure of 135,000 km²sr yr. The strongest indication for an excess that we find, with a posttrial significance of 3.1σ, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array Collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable integrated exposure over these regions, our results there are in good agreement with the expectations from an isotropic distribution. 

Abstract Diffuse photons of energy above 0.1 PeV, produced through the interactions between cosmic rays and either interstellar matter or background radiation fields, are powerful tracers of the distribution of cosmic rays in the Galaxy. Furthermore, the measurement of a diffuse photon flux would be an important probe to test models of super-heavy dark matter decaying into gamma-rays. In this work, we search for a diffuse photon flux in the energy range between 50 PeV and 200 PeV using data from the Pierre Auger Observatory. For the first time, we combine the air-shower measurements from a 2 km²surface array consisting of 19 water-Cherenkov surface detectors, spaced at 433 m, with the muon measurements from an array of buried scintillators placed in the same area. Using 15 months of data, collected while the array was still under construction, we derive upper limits to the integral photon flux ranging from 13.3 to 13.8 km^-2sr^-1yr^-1above tens of PeV. We extend the Pierre Auger Observatory photon search program towards lower energies, covering more than three decades of cosmic-ray energy. This work lays the foundation for future diffuse photon searches: with the data from the next 10 years of operation of the Observatory, this limit is expected to improve by a factor of ∼20.