Partial Selfish Mining for More Profits

Mining attacks aim to gain an unfair share of extra rewards in the blockchain mining. Selfish mining can preserve discovered blocks and strategically release them, wasting honest miners' computing resources and getting higher profits. Previous mining attacks either conceal the mined whole blocks (hiding or discarding), or release them completely in a particular time slot (e.g., causing a fork). In this paper, we extend the mining attack's strategy space to partial block sharing, and propose a new and feasible Partial Selfish Mining (PSM) attack. We show that by releasing partial block data publicly and attracting rational miners to work on attacker's private branch, attackers and these attracted miners can gain an unfair share of mining rewards. We then propose Advanced PSM (A-PSM) attack that can further improve attackers' profits to be no less than the selfish mining. Both theoretical and experimental results show that PSM attackers can be more profitable than selfish miners under a certain range of mining power and network conditions. A-PSM attackers can gain even higher profits than both selfish mining and honest mining with attracted rational miners

Delving into Crispness: Guided Label Refinement for Crisp Edge Detection

Learning-based edge detection usually suffers from predicting thick edges. Through extensive quantitative study with a new edge crispness measure, we find that noisy human-labeled edges are the main cause of thick predictions. Based on this observation, we advocate that more attention should be paid on label quality than on model design to achieve crisp edge detection. To this end, we propose an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors. Essentially, it seeks for a subset of over-detected Canny edges that best align human labels. We show that several existing edge detectors can be turned into a crisp edge detector through training on our refined edge maps. Experiments demonstrate that deep models trained with refined edges achieve significant performance boost of crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without relying on non-maximal suppression. We further conduct experiments and show the superiority of our crisp edge detection for optical flow estimation and image segmentation.Comment: Accepted by TI

Performance Analysis and Optimization of Compressed Air Energy Storage Integrated with Latent Thermal Energy Storage

Recovering compression waste heat using latent thermal energy storage (LTES) is a promising method to enhance the round-trip efficiency of compressed air energy storage (CAES) systems. In this study, a systematic thermodynamic model coupled with a concentric diffusion heat transfer model of the cylindrical packed-bed LTES is established for a CAES system, and the numerical simulation model is validated by experimental data in the reference. Based on the numerical model, the charging–discharging performance of LTES and CAES systems is evaluated under different layouts of phase change materials (PCMs) in LTES, and the optimal layout of PCM is specified as a three-stage layout, since the exergy efficiency of LTES and round-trip efficiency are improved by 8.2% and 6.9% compared with a one-stage layout. Then, the proportion of three PCMs is optimized using response surface methods. The optimization results indicate that the exergy efficiency of LTES and round-trip efficiency of the CAES system are expected to be 80.9% and 73.3% under the PCM proportion of 0.48:0.3:0.22 for three stages, which are 7.0% and 13.1% higher than the original three-stage PCMs with equal proportions

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation

The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation [preprint]

The YAP and TAZ paralogues are transcriptional co-activators recruited to target sites, primarily by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB and STAT3, key factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, co-occupy many target sites in vivo via AP-1 and (to a lesser extent) STAT3 sequence motifs, and stimulate transcriptional activation by AP-1 proteins. A few target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes. YAP/TAZ, JUNB, and STAT3 directly regulate a common set of target genes that overlap, but are distinct from, those regulated by YAP/TAZ and TEADs. The set of genes regulated by YAP/TAZ, STAT3, and JUNB is associated with poor survival in breast cancer patients with the triple-negative form of the disease

Mitochondrial EF4 links respiratory dysfunction and cytoplasmic translation in Caenorhabditis elegans

AbstractHow animals coordinate cellular bioenergetics in response to stress conditions is an essential question related to aging, obesity and cancer. Elongation factor 4 (EF4/LEPA) is a highly conserved protein that promotes protein synthesis under stress conditions, whereas its function in metazoans remains unknown. Here, we show that, in Caenorhabditis elegans, the mitochondria-localized CeEF4 (referred to as mtEF4) affects mitochondrial functions, especially at low temperature (15°C). At worms' optimum growing temperature (20°C), mtef4 deletion leads to self-brood size reduction, growth delay and mitochondrial dysfunction. Transcriptomic analyses show that mtef4 deletion induces retrograde pathways, including mitochondrial biogenesis and cytoplasmic translation reorganization. At low temperature (15°C), mtef4 deletion reduces mitochondrial translation and disrupts the assembly of respiratory chain supercomplexes containing complex IV. These observations are indicative of the important roles of mtEF4 in mitochondrial functions and adaptation to stressful conditions

Energetic stability, structural transition, and thermodynamic properties of ZnSnO[sub 3]

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98679/1/ApplPhysLett_98_091914.pd

Microstructural ordering of nanofibers in flow-directed assembly

Fabrication of highly ordered and dense nanofibers assemblies is of key importance for high-performance and multi-functional material and device applications. In this work, we design an experimental approach in silico, where shear flow and solvent evaporation are applied to tune the alignment, overlap of nanofibers, and density of the assemblies. Microscopic dynamics of the process is probed by dissipative particle dynamics simulations, where hydrodynamic and thermal fluctuation effects are fully modeled. We find that microstructural ordering of the assembled nanofibers can be established within a specific range of the Peclet numbers and evaporation rates, while the properties of nanofibers and their interaction are crucial for the local stacking order. The underlying mechanisms are elucidated by considering the competition between hydrodynamic coupling and thermal fluctuation. Based on these understandings, a practical design of flow channels for nanofiber assembly with promising mechanical performance is outlined.Accepted manuscriptSupporting documentatio