Zooming in to Massive Star Birth

We present high resolution (0.2", 1000 AU) 1.3 mm ALMA observations of massive infrared dark cloud clump, G028.37+00.07-C1, thought to harbor the early stages of massive star formation. Using $\rm N_2D^+$(3-2) we resolve the previously identified C1-S core, separating the bulk of its emission from two nearby protostellar sources. C1-S is thus identified as a massive ($\sim50\:M_\odot$), compact ($\sim0.1\:$pc diameter) starless core, e.g., with no signs of outflow activity. Being highly deuterated, this is a promising candidate for a pre-stellar core on the verge of collapse. An analysis of its dynamical state indicates a sub-virial velocity dispersion compared to a trans-Alfv\'enic turbulent core model. However, virial equilibrium could be achieved with sub-Alfv\'enic conditions involving $\sim2\:$mG magnetic field strengths.Comment: 19 pages, 15 figures, 4 tables, accepted by Ap

Cerebellar encoding of multiple candidate error cues in the service of motor learning

or learning to occur through trial and error, the nervous system must effectively detect and encode performance errors. To examine this process, we designed a set of oculomotor learning tasks with more than one visual object providing potential error cues, as would occur in a natural visual scene. A task-relevant visual target and a task-irrelevant visual background both influenced vestibulo-ocular reflex learning in rhesus monkeys. Thus, motor learning does not identify a single error cue based on behavioral relevance, but can be simultaneously influenced by more than one cue. Moreover, the relative weighting ofthe differentcues could vary. Ifthe speed ofthe visual target's motion on the retina was low (≪1°/s), background motion dominated learning, but if target speed was high, the effects of the background were suppressed. The target and background motion had similar, nonlinear effects on the putative neural instructive signals carried by cerebellar climbing fibers, but with a stronger influence ofthe backgroundon the climbing fibers than on learning. In contrast, putative neuralinstructive signals carriedby the simple spikes of Purkinje cells were influenced solely by the motion of the visual target. Because they are influenced by different cues during training, joint control of learning by the climbing fibers and Purkinje cells may expand the learning capacity of the cerebellar circuit

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common method for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.Comment: To appear as a Spotlight presentation at NIPS 201

Pricing Carbon Emissions in China

The purpose of the paper is to provide a clear mechanism for determining carbon emissions pricing in China as a guide to how carbon emissions might be mitigated to reduce fossil fuel pollution. The Chinese Government has promoted the development of clean energy, including hydroelectric power, wind power, and solar energy generation. In order to involve companies in carbon emissions control, a series of regional and provincial carbon markets have been established since 2013. Since China’s carbon market was established in 2013 and mainly run domestically, and not necessarily using market principles, there has been almost no research on China’s carbon price and volatility. This paper provides an introduction to China’s regional and provincial carbon markets, proposes how to establish a national market for pricing carbon emissions, discusses how and when these markets might be established, how they might perform, and the subsequent prices for China’s regional and national carbon markets. Power generation in manufacturing consumes more than other industries, with more than 40% of total coal consumption. Apart from manufacturing, the northern China heating system also relies on fossil fuels, mainly coal, which causes serious pollution. In order to understand the regional markets well, it is necessary to analyze the energy structure in these regions. Coal is the primary energy source in China, so that provinces that rely heavily on coal receive a greater number of carbon emissions permits from the Chinese Government. In order to establish a national carbon market for China, a detailed analysis of eight important regional markets will be presented. The four largest energy markets, namely Guangdong, Shanghai, Shenzhen and Hubei, traded around 82% of the total volume and 85% of the total value of the seven markets in 2017, as the industry structure of the western area is different from that of the eastern area. The China National Development and Reform Commission has proposed a national carbon market, which can attract investors and companies to participate in carbon emissions trading. This important issue will be investigated in the paper

Establishing National Carbon Emission Prices for China

The purpose of the paper is to establish national carbon emissions prices for the People’s Republic of China, which is one of the world’s largest producers of carbon emissions. Several measures have been undertaken to address climate change in China, including the establishment of a carbon trading system. Since 2013, eight regional carbon emissions markets have been established, namely Beijing, Shanghai, Guangdong, Shenzhen, Tianjin, Chongqing, Hubei and Fujian. The Central Government announced a national carbon emissions market, with power generation as the first industry to be considered. However, as carbon emissions prices in the eight regional markets are very different, for a variety of administrative reasons, it is essential to create a procedure for establishing a national carbon emissions price. The regional markets are pioneers, and their experience will play important roles in establishing a national carbon emissions market, with national prices based on regional prices, turnovers and volumes. The paper considers two sources of regional data for China’s carbon allowances, which are based on primary and secondary data sources, and compares their relative strengths and weaknesses. The paper establishes national carbon emissions prices based on the primary and secondary regional prices, for the first time, and compares both national prices and regional prices against each other. The carbon emission prices in Hubei, Guangdong, Shenzhen and Tianjin are highly correlated with the national prices based on the primary and secondary sources. Establishing national carbon emissions prices should be very helpful for the national carbon emissions market that is under construction in China, as well as for other regions and countries worldwide

Isolation and Cryopreservation of Neonatal Rat Cardiomyocytes

Cell culture has become increasingly important in cardiac research, but due to the limited proliferation of cardiomyocytes, culturing cardiomyocytes is difficult and time consuming. The most commonly used cells are neonatal rat cardiomyocytes (NRCMs), which require isolation every time cells are needed. The birth of the rats can be unpredictable. Cryopreservation is proposed to allow for cells to be stored until needed, yet freezing/thawing methods for primary cardiomyocytes are challenging due to the sensitivity of the cells. Using the proper cryoprotectant, dimethyl sulfoxide (DMSO), cryopreservation was achieved. By slowly extracting the DMSO while thawing the cells, cultures were obtained with viable NRCMs. NRCM phenotype was verified using immunocytochemistry staining for α-sarcomeric actinin. In addition, cells also showed spontaneous contraction after several days in culture. Cell viability after thawing was acceptable at 40–60%. In spite of this, the methods outlined allow one to easily cryopreserve and thaw NRCMs. This gives researchers a greater amount of flexibility in planning experiments as well as reducing the use of animals