Dynamic Frequency Scaling Regarding Memory for Energy Efficiency of Embedded Systems

Memory significantly affects the power consumption of embedded systems as well as performance. CPU frequency scaling for power management could fail in optimizing the energy efficiency without considering the memory access. In this paper, we analyze the power consumption and energy efficiency of an embedded system that supports dynamic scaling of frequency for both CPU and memory access. The power consumption of the CPU and the memory is modeled to show that the memory access rate affects the energy efficiency and the CPU frequency selection. Based on the power model, a method for frequency selection is presented to optimize the power efficiency which is measured using Energy-Delay Product (EDP). The proposed method is implemented and tested on a commercial smartphone to achieve about 3.3% - 7.6% enhancement comparing with the power management policy provided by the manufacturer in terms of EDP

How can we erase states inside a black hole?

We investigate an entangled system, which is analogous to a composite system of a black hole and Hawking radiation. If Hawking radiation is well approximated by an outgoing particle generated from pair creation around the black hole, such a pair creation increases the total number of states. There should be a unitary mechanism to reduce the number of states inside the horizon for black hole evaporation. Because the infalling antiparticle has negative energy, as long as the infalling antiparticle finds its partner such that the two particles form a separable state, one can trace out such a zero energy system by maintaining unitarity. In this paper, based on some toy model calculations, we show that such a unitary tracing-out process is only possible before the Page time while it is impossible after the Page time. Hence, after the Page time, if we assume that the process is unitary and the Hawking pair forms a separable state, the internal number of states will monotonically increase, which is supported by the Almheiri-Marolf-Polchinski-Sully (AMPS) argument. In addition, the Hawking particles cannot generate randomness of the entire system; hence, the entanglement entropy cannot reach its maximum. Based on these results, we modify the correct form of the Page curve for the remnant picture. The most important conclusion is this: if we assume unitarity, semi-classical quantum field theory, and general relativity, then the black hole should violate the Bekenstein-Hawking entropy bound around the Page time at the latest; hence, the infinite production arguments for remnants might be applied for semi-classical black holes, which seems very problematic.Comment: 18 pages, 7 figure

Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images

Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a state-of-the-art risk prediction method that only uses mammograms from a single time point. We validate our approach on a dataset with 16,113 exams and further demonstrate that it effectively captures patterns of changes from prior mammograms, such as changes in breast density, resulting in improved short-term and long-term breast cancer risk prediction. Experimental results show that our model achieves a statistically significant improvement in performance over the state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p < 0.05) on held-out test sets

Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well-known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations for the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.</p

Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision

We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor generalization to diverse unseen examples. We claim that an anomaly detector equipped with a meta-learning scheme alleviates the limitation by leading the model to an initialization point for better optimization. We evaluate the performance of our framework on two challenging datasets, UCF-Crime and ShanghaiTech. The experimental results demonstrate that our algorithm boosts the capability to localize unseen abnormal events in a weakly supervised setting. Besides the technical contributions, we perform the annotation of missing labels in the UCF-Crime dataset and make our task evaluated effectively.N

Hydrogel Biomaterials for Stem Cell Microencapsulation

Stem cell transplantation has been recognized as a promising strategy to induce the regeneration of injured and diseased tissues and sustain therapeutic molecules for prolonged periods in vivo. However, stem cell-based therapy is often ineffective due to low survival, poor engraftment, and a lack of site-specificity. Hydrogels can offer several advantages as cell delivery vehicles, including cell stabilization and the provision of tissue-like environments with specific cellular signals; however, the administration of bulk hydrogels is still not appropriate to obtain safe and effective outcomes. Hence, stem cell encapsulation in uniform micro-sized hydrogels and their transplantation in vivo have recently garnered great attention for minimally invasive administration and the enhancement of therapeutic activities of the transplanted stem cells. Several important methods for stem cell microencapsulation are described in this review. In addition, various natural and synthetic polymers, which have been employed for the microencapsulation of stem cells, are reviewed in this article