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1. Latitudinal variation in thermal performance of the common coral, Pocillopora spp

   https://doi.org/10.1242/jeb.247090  

   Edmunds, P J; Combosch, D; Torrado, H; Sakai, K; Sinniger, F; Burgess, S C (May 2024, Journal of Experimental Biology)

   Understanding how tropical corals respond to temperatures is important to evaluating their capacity to persist in a warmer future. We studied the common Pacific coral Pocillopora over 44° of latitude, and used populations at three islands with different thermal regimes to compare their responses to temperature using thermal performance curves (TPCs) for respiration and gross photosynthesis. Corals were sampled in the local autumn from Moorea, Guam, and Okinawa where mean (± s.d.) annual seawater temperature is 28.0±0.9°C, 28.9±0.7°C, and 25.1±3.4°C, respectively. TPCs for respiration were similar among latitudes, the thermal optimum (Topt) was above the local maximum temperature at all three islands, and maximum respiration was lowest at Okinawa. TPCs for gross photosynthesis were wider, implying greater thermal eurytopy, with a higher Topt in Moorea versus Guam and Okinawa. Topt was above the maximum temperature in Moorea, but was similar to daily temperatures over 13% of the year in Okinawa, and 53% of the year in Guam. There was greater annual variation in daily temperatures in Okinawa than Guam or Moorea, which translated to large variation in the supply of metabolic energy and photosynthetically fixed carbon at higher latitudes. Despite these trends, the differences in TPCs for Pocillopora were not profoundly different across latitudes, reducing the likelihood that populations of these corals could better match their phenotypes to future more extreme temperatures through migration. Any such response would place a premium on high metabolic plasticity and tolerance of large seasonal variations in energy budgets. 

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2. Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection

   https://doi.org/10.1088/2634-4386/ad49ce  

   Alam, Muhammad Sabbir; Misba, Walid Al; Atulasimha, Jayasimha (June 2024, Neuromorphic Computing and Engineering)

   Abstract Anomaly detection in real-time using autoencoders implemented on edge devices is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We further propose nanoscale ferromagnetic racetracks with engineered notches hosting magnetic domain walls (DW) as exemplary non-volatile memory-based autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses to write different magnetoresistance states. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are typically known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point synaptic weights that are extremely memory intensive. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying significant reduction in operation energy for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data. 

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3. XENONnT analysis: Signal reconstruction, calibration, and event selection

   https://doi.org/10.1103/PhysRevD.111.062006  

   Aprile, E; Aalbers, J; Abe, K; Ahmed_Maouloud, S; Althueser, L; Andrieu, B; Angelino, E; Angevaare, J R; Antón_Martin, D; Arneodo, F; et al (March 2025, Physical Review D)

   The XENONnT experiment, located at the INFN Laboratori Nazionali del Gran Sasso, Italy, features a 5.9 tonne liquid xenon time projection chamber surrounded by an instrumented neutron veto, all of which is housed within a muon veto water tank. Because of extensive shielding and advanced purification to mitigate natural radioactivity, an exceptionally low background level of $(15.8\pm 1.3)\mathrm{events}/(\mathrm{tonne}·\mathrm{year}·\mathrm{keV})$in the (1,30) keV region is reached in the inner part of the time projection chamber. XENONnT is, thus, sensitive to a wide range of rare phenomena related to dark matter and neutrino interactions, both within and beyond the Standard Model of particle physics, with a focus on the direct detection of dark matter in the form of weakly interacting massive particles. From May 2021 to December 2021, XENONnT accumulated data in rare-event search mode with a total exposure of one $\mathrm{tonne}·\mathrm{year}$. This paper provides a detailed description of the signal reconstruction methods, event selection procedure, and detector response calibration, as well as an overview of the detector performance in this time frame. This work establishes the foundational framework for the “blind analysis” methodology we are using when reporting XENONnT physics results. Published by the American Physical Society2025 

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4. bayesTPC: Bayesian inference for thermal performance curves in R

   https://doi.org/10.1111/2041-210X.70004  

   Sorek, Sean; Smith, Jr., John_W; Huxley, Paul_J; Johnson, Leah_R (March 2025, Methods in Ecology and Evolution)

   Abstract Reliable predictions of ectotherm responses to climatic warming are important because many of these organisms perform important roles that can directly impact human society.Thermal performance curves (TPCs) provide useful information on the physiological constraints that limit the capacity of these temperature‐sensitive organisms to exist and grow.NLS pipelines for fitting TPCs are widely available, but these approaches rely on assumptions that can yield unreliable parameter estimates.We presentbayesTPC, anRpackage for fitting TPCs to trait responses using thenimblelanguage and machinery as the underlying engine for Markov Chain Monte Carlo.bayesTPCaims to support the adoption of Bayesian approaches in thermal physiology, and promote TPC fitting that adequately quantifies uncertainty. 

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5. Characterization of a 220Rn source for low-energy electronic recoil calibration of the XENONnT detector

   https://doi.org/10.1088/1748-0221/18/11/P11009  

   Jörg, Florian; Li, Shengchao; Schreiner, Jochen; Simgen, Hardy; Lang, Rafael F (November 2023, Journal of Instrumentation)

   Low-background liquid xenon detectors are utilized in the investigation of rare events, including dark matter and neutrinoless double beta decay. For their calibration, gaseous 220Rn can be used. After being introduced into the xenon, its progeny isotope 212Pb induces homogeneously distributed, low-energy (<30 keV) electronic recoil interactions. We report on the characterization of such a source for use in the XENONnT experiment. It consists of four commercially available 228Th sources with an activity of 55 kBq. These sources provide a high 220Rn emanation rate of about 8 kBq. We find no indication for the release of the long-lived 228Th above 1.7 mBq. Though an unexpected 222Rn emanation rate of about 3.6 mBq is observed, this source is still in line with the requirements for the XENONnT experiment. 

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