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Tidal disruption events (TDEs) can potentially probe low-mass black holes in host galaxies that might not adhere to bulge or stellar-dispersion relationships. At least initially, TDEs can also reveal super-Eddington accretion. X-ray spectroscopy can potentially constrain black hole masses, and reveal ionized outflows associated with super-Eddington accretion. Our analysis of XMM-Newton X-ray observations of the TDE AT2021ehb, around 300 days post-disruption, reveals a soft spectrum and can be fit with a combination of multi-color disk blackbody and power-law components. Using two independent disk models with properties suited to TDEs, we estimate a black hole mass at $M \simeq 10^{5.5}~M_{\odot}$, indicating AT2021ehb may expose the elusive low-mass end of the nuclear black hole population. These models offer simple yet robust characterization; more complicated models are not required, but provide important context and caveats in the limit of moderately sensitive data. If disk reflection is included, the disk flux is lower and inferred black hole masses are $\sim$ 0.35 dex higher. Simple wind formulations imply an extremely fast $v_{\mathrm{out}} = -0.2~c$ outflow and obviate a disk continuum component. Assuming a unity filling factor, such a wind implies an instantaneous mass outflow rate of $\dot{M} \simeq 5~M_{\odot}~{\rm yr}^{-1}$. Such a high rate suggests that the filling factor for the Ultra Fast Outflow (UFO) must be extremely low, and/or the UFO phase is ephemeral. We discuss the strengths and limitations of our analysis and avenues for future observations of TDEs., Comment: 19 pages, 4 figures

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://www.synapse.org/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner.

As SRAM-based caches are hitting a scaling wall, manufacturers are integrating DRAM-based caches into system designs to continue increasing cache sizes. While DRAM caches can improve the performance of memory systems, existing DRAM cache designs suffer from high miss penalties, wasted data movement, and interference between misses and demand requests. In this paper, we propose TDRAM, a novel DRAM microarchitecture tailored for caching. TDRAM enhances HBM3 by adding a set of small low-latency mats to store tags and metadata on the same die as the data mats. These mats enable fast parallel tag and data access, on-DRAM-die tag comparison, and conditional data response based on comparison result (reducing wasted data transfers) akin to SRAM caches mechanism. TDRAM further optimizes the hit and miss latencies by performing opportunistic early tag probing. Moreover, TDRAM introduces a flush buffer to store conflicting dirty data on write misses, eliminating turnaround delays on data bus. We evaluate TDRAM using a full-system simulator and a set of HPC workloads with large memory footprints showing TDRAM provides at least 2.6$\times$ faster tag check, 1.2$\times$ speedup, and 21% less energy consumption, compared to the state-of-the-art commercial and research designs.

In dusty plasma environments, the spontaneous growth of nanoparticles from reactive gases has been extensively studied for over three decades, primarily focusing on hydrocarbons and silicate particles. Here, we introduce the growth of titanium dioxide, a wide band gap semiconductor, as dusty plasma nanoparticles. The resultant particles exhibited a spherical morphology and reached a maximum homogeneous radius of 230 $\pm$ 17 nm after an elapsed time of 70 seconds. The particle grew linearly and the growth displayed a cyclic behavior; that is, upon reaching their maximum radius, the largest particles fell out of the plasma, and a new growth cycle immediately followed. The particles were collected after being grown for different amounts of time and imaged using scanning electron microscopy. Further characterization was carried out using energy dispersive X-ray spectroscopy, X-ray diffraction and Raman spectroscopy to elucidate the chemical composition and crystalline properties of the maximally sized particles. Initially, the as-grown particles after 70 seconds exhibited an amorphous structure. However, annealing treatments at temperatures of 400 $^\circ$C and 800 $^\circ$C induced crystallization, yielding anatase and rutile phases, respectively. Notably, annealing at 600 $^\circ$C resulted in a mixed phase of anatase and rutile. These findings open new avenues for a rapid and controlled growth technique of titanium dioxide as dusty plasma., Comment: 8 pages, 5 figures

RowHammer attacks are a growing security and reliability concern for DRAMs and computer systems as they can induce many bit errors that overwhelm error detection and correction capabilities. System-level solutions are needed as process technology and circuit improvements alone are unlikely to provide complete protection against RowHammer attacks in the future. This paper introduces RAMPART, a novel approach to mitigating RowHammer attacks and improving server memory system reliability by remapping addresses in each DRAM in a way that confines RowHammer bit flips to a single device for any victim row address. When RAMPART is paired with Single Device Data Correction (SDDC) and patrol scrub, error detection and correction methods in use today, the system can detect and correct bit flips from a successful attack, allowing the memory system to heal itself. RAMPART is compatible with DDR5 RowHammer mitigation features, as well as a wide variety of algorithmic and probabilistic tracking methods. We also introduce BRC-VL, a variation of DDR5 Bounded Refresh Configuration (BRC) that improves system performance by reducing mitigation overhead and show that it works well with probabilistic sampling methods to combat traditional and victim-focused mitigation attacks like Half-Double. The combination of RAMPART, SDDC, and scrubbing enables stronger RowHammer resistance by correcting bit flips from one successful attack. Uncorrectable errors are much less likely, requiring two successful attacks before the memory system is scrubbed., Comment: 16 pages, 13 figures. A version of this paper will appear in the Proceedings of MEMSYS23