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The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack. This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems., Comment: Communications of the ACM (CACM), Research and Advances, Vol 67, No.9, September 2024. ACM Link: https://dl.acm.org/doi/pdf/10.1145/3647638

Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substrate. In particular, today's computing systems for embodied AI robots are designed purely based on the interest of algorithm developers, where robot actions are divided into a discrete frame-basis. Such an execution pipeline creates high latency and energy consumption. This paper proposes Corki, an algorithm-architecture co-design framework for real-time embodied AI robot control. Our idea is to decouple LLM inference, robotic control and data communication in the embodied AI robots compute pipeline. Instead of predicting action for one single frame, Corki predicts the trajectory for the near future to reduce the frequency of LLM inference. The algorithm is coupled with a hardware that accelerates transforming trajectory into actual torque signals used to control robots and an execution pipeline that parallels data communication with computation. Corki largely reduces LLM inference frequency by up to 8.0x, resulting in up to 3.6x speed up. The success rate improvement can be up to 17.3%. Code is provided for re-implementation. https://github.com/hyy0613/Corki

Deuterium fractionation in the closest vicinity of a protostar is important in understanding its potential heritage to a planetary system. Here, we have detected the spectral line emission of CH3OH and its three deuterated species, CH2DOH, CHD2OH, and CH3OD, toward the low-mass protostellar source B335 at a resolution of 0.''03 (5 au) with Atacama Large Millimeter/submillimeter Array. They have a ring distribution within the radius of 24 au with the intensity depression at the continuum peak. We derive the column densities and abundance ratios of the above species at 6 positions in the disk/envelope system as well as the continuum peak. The D/H ratio of CH3OH is ~[0.03-0.13], which is derived by correcting the statistical weight of 3 for CH2DOH. The [CHD2OH]/[CH2DOH] ratio is derived to be higher ([0.14-0.29]). On the other hand, the [CH2DOH]/[CH3OD] ratio ([4.9-15]) is higher than the statistical ratio of 3, and is comparable to those reported for other low-mass sources. We study the physical structure on a few au scale in B335 by analyzing the CH3OH (183,15-182,16, A) and HCOOH (120,12-110,11) line emission. Velocity structures of these lines are reasonably explained as the infalling-rotating motion. The protostellar mass and the upper limit to centrifugal barrier are thus derived to be 0.03-0.07 M_{\odot} and <7 au, respectively, showing that B335 harbors a young protostar with a tiny disk structure. Such youth of the protostar may be related to the relatively high [CH2DOH]/[CH3OH] ratio.

We report the first detection in the interstellar medium of $N$-cyanomethanimine (H$_2$CNCN), the stable dimer of HCN of highest energy, and the most complex organic molecule identified in space containing the prebiotically relevant NCN backbone. We have identified a plethora of $a$-type rotational transitions with 3 $\leq J_\text{up} \leq$ 11 and $K_\text{a} \leq$ 2 that belong to this species towards the Galactic Center G+0.693-0.027 molecular cloud, the only interstellar source showing the three cyanomethanimine isomers (including the $Z$- and $E$- isomers of $C$-cyanomethanimine, HNCHCN). We have derived a total column density for H$_2$CNCN of (2.9$\, \pm \,$0.1)$\times$10$^{12}$ cm$^{-2}$, which translates into a total molecular abundance with respect to H$_2$ of (2.1$\, \pm \,$0.3)$\times$10$^{-11}$. We have also revisited the previous detection of $E$- and $Z$-HNCHCN, and found a total $C/N$-cyanomethanimine abundance ratio of 31.8$\, \pm \,$1.8 and a $Z/E$-HNCHCN ratio of 4.5$\, \pm \,$0.2. While the latter can be explained on the basis of thermodynamic equilibrium, chemical kinetics are more likely responsible for the observed $C/N$-cyanomethanimine abundance ratio, where the gas-phase reaction between methanimine (CH$_2$NH) and the cyanogen radical (CN) arises as the primary formation route., Comment: 22 pages, 8 figures, 3 tables, 2 appendix - Accepted for publication in The Astrophysical Journal

We present the first detection in space of thionylimide (HNSO) toward the Galactic Center molecular cloud G+0.693-0.027, thanks to the superb sensitivity of an ultradeep molecular line survey carried out with the Yebes 40$\,$m and IRAM 30$\,$m telescopes. This molecule is the first species detected in the interstellar medium containing, simultaneously, N, S and O. We have identified numerous $K$$_a$ = 0, 1 and 2 transitions belonging to HNSO covering from $J$$_{\rm up}$ = 2 to $J$$_{\rm up}$ = 10, including several completely unblended features. We derive a molecular column density of $N$ = (8 $\pm$ 1)$\times$10$^{13}$ cm$^{-2}$, yielding a fractional abundance relative to H$_2$ of $\sim$6$\times$10$^{-10}$, which is about $\sim$37 and $\sim$4.8 times less abundant than SO and SO2, respectively. Although there are still many unknowns in the interstellar chemistry of NSO-bearing molecules, we propose that HNSO is likely formed through the reaction of the NSO radical and atomic H on the surface of icy grains, with alternative routes also deserving exploration. Finally, HNSO appears as a promising link between N- , S- and O- interstellar chemistry and its discovery paves the route to the detection of a new family of molecules in space., Comment: Forthcoming paper in The Astrophysical Journal Letters (in press)

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LLMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.

We present the first detection in space of O-protonated carbonyl sulfide (\ch{HOCS+}), in the midst of an ultradeep molecular line survey toward the G+0.693-0.027 molecular cloud. From the observation of all $K$$_a$ = 0 transitions ranging from $J$$_{lo}$ = 2 to $J$$_{lo}$ = 13 of \ch{HOCS+} covered by our survey, we derive a column density of $N$ = (9 $\pm$ 2)$\times$10$^{12}$ cm$^{-2}$, translating into a fractional abundance relative to H$_2$ of $\sim$7$\times$10$^{-11}$. Conversely, the S-protonated \ch{HSCO+} isomer remains undetected, and we derive an upper limit to its abundance with respect to H$_2$ of $\leq$3$\times$10$^{-11}$, a factor of $\geq$2.3 less abundant than \ch{HOCS+}. We obtain a \ch{HOCS+}/OCS ratio of $\sim$2.5$\times$10$^{-3}$, in good agreement with the prediction of astrochemical models. These models show that one of the main chemical routes to the interstellar formation of \ch{HOCS+} is likely the protonation of OCS, which appears to be more efficient at the oxygen end. Also, we find that high values of cosmic-ray ionisation rates (10$^{-15}$-10$^{-14}$ s$^{-1}$) are needed to reproduce the observed abundance of \ch{HOCS+}. In addition, we compare the O/S ratio across different interstellar environments. G+0.693-0.027 appears as the source with the lowest O/S ratio. We find a \ch{HOCO+}/\ch{HOCS+} ratio of $\sim$31, in accordance with other O/S molecular pairs detected toward this region and also close to the O/S solar value ($\sim$37). This fact indicates that S is not significantly depleted within this cloud due to the action of large-scale shocks, unlike in other sources where S-bearing species remain trapped on icy dust grains., Comment: Forthcoming paper in The Astrophysical Journal (in press)

Due to the cultural and governance differences of countries around the world, there currently exists a wide spectrum of AI regulation policy proposals that have created a chaos in the global AI regulatory space. Properly regulating AI technologies is extremely challenging, as it requires a delicate balance between legal restrictions and technological developments. In this article, we first present a comprehensive review of AI regulation proposals from different geographical locations and cultural backgrounds. Then, drawing from historical lessons, we develop a framework to facilitate a thorough analysis of AI regulation proposals. Finally, we perform a systematic analysis of these AI regulation proposals to understand how each proposal may fail. This study, containing historical lessons and analysis methods, aims to help governing bodies untangling the AI regulatory chaos through a divide-and-conquer manner.

We report the first detection in the interstellar medium of a C$_2$H$_5$O$_2$N isomer: $syn$-glycolamide (NH$_2$C(O)CH$_2$OH). The exquisite sensitivity at sub-mK levels of an ultra-deep spectral survey carried out with the Yebes 40m and IRAM 30m telescopes towards the G+0.693-0.027 molecular cloud have allowed us to unambiguously identify multiple transitions of this species. We derived a column density of (7.4 $\pm$ 0.7)$\times$10$^{12}$ cm$^{-2}$, which implies a molecular abundance with respect to H$_2$ of 5.5$\times$10$^{-11}$. The other C$_2$H$_5$O$_2$N isomers, including the higher-energy $anti$ conformer of glycolamide, and two conformers of glycine, were not detected. The upper limit derived for the abundance of glycine indicates that this amino acid is surely less abundant than its isomer glycolamide in the ISM. The abundances of the C$_2$H$_5$O$_2$N isomers cannot be explained in terms of thermodynamic equilibrium, and thus chemical kinetics need to be invoked. While the low abundance of glycine might not be surprising, based on the relative low abundances of acids in the ISM compared to other compounds (e.g. alcohols, aldehydes or amines), several chemical pathways can favour the formation of its isomer glycolamide. It can be formed through radical-radical reactions on the surface of dust grains. The abundances of these radicals can be significantly boosted in an environment affected by a strong ultraviolet field induced by cosmic rays, such as that expected in G+0.693-0.027. Therefore, as shown by several recent molecular detections towards this molecular cloud, it stands out as the best target to discover new species with carbon, oxygen and nitrogen with increasing chemical complexity., Comment: Accepted in The Astrophysical Journal Letters

After a quarter century since the detection of the last interstellar carboxylic acid, acetic acid (CH$_3$COOH), we report the discovery of a new one, the cis-trans form of carbonic acid (HOCOOH), toward the Galactic Center molecular cloud G+0.693-0.027. HOCOOH stands as the first interstellar molecule containing three oxygen atoms and also the third carboxylic acid detected so far in the interstellar medium. Albeit the limited available laboratory measurements (up to 65 GHz), we have also identified several pairs of unblended lines directly in the astronomical data (between 75-120 GHz), which allowed us to slightly improve the set of spectroscopic constants. We derive a column density for cis-trans HOCOOH of $N$ = (6.4 $\pm$ 0.4) $\times$ 10$^{12}$ cm$^{-2}$, which yields an abundance with respect to molecular H$_2$ of 4.7 $\times$ 10$^{-11}$. Meanwhile, the extremely low dipole moment (about fifteen times lower) of the lower-energy conformer, cis-cis HOCOOH, precludes its detection. We obtain an upper limit to its abundance with respect to H$_2$ of $\leq$ 1.2 $\times$10$^{-9}$, which suggests that cis-cis HOCOOH might be fairly abundant in interstellar space, although it is nearly undetectable by radio astronomical observations. We derive a cis-cis/cis-trans ratio $\leq$ 25, consistent with the smaller energy difference between both conformers compared with the relative stability of trans- and cis-formic acid (HCOOH). Finally, we compare the abundance of these acids in different astronomical environments, further suggesting a relationship between the chemical content found in the interstellar medium and the chemical composition of the minor bodies of the Solar System, which could be inherited during the star formation process., Comment: Accepted for publication in The Astrophysical Journal

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the technical challenges and economic impact of the digital economy, we argue that scalability is both highly necessary from a technical perspective and significantly advantageous from an economic perspective, thus is the key for the autonomy industry to achieve its full potential. Nonetheless, the current development paradigm, dubbed Autonomy 1.0, scales with the number of engineers, instead of with the amount of data or compute resources, hence preventing the autonomy industry to fully benefit from the economies of scale, especially the exponentially cheapening compute cost and the explosion of available data. We further analyze the key scalability blockers and explain how a new development paradigm, dubbed Autonomy 2.0, can address these problems to greatly boost the autonomy industry.

This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health is fundamental to human capital and social and economic development. The core motivation for the AICOM project is the fact that over 80% of the people in the least developed countries (LDCs) own a mobile phone, even though less than 40% of these people have internet access. Hence, through enabling AI-based disease diagnostics and screening capability on affordable mobile phones without connectivity will be a critical first step to addressing healthcare access problems. The technologies developed in the AICOM project achieve exactly this goal, and we have demonstrated the effectiveness of AICOM on monkeypox screening tasks. We plan to continue expanding and open-sourcing the AICOM platform, aiming for it to evolve into an universal AI doctor for the Underserved and Hard-to-Reach.

The ${\rm H_2NC}$ radical is the high-energy metastable isomer of ${\rm H_2CN}$ radical, which has been recently detected for the first time in the interstellar medium towards a handful of cold galactic sources, besides a warm galaxy in front of the PKS 1830-211 quasar. These detections have shown that the ${\rm H_2CN}$/${\rm H_2NC}$ isomeric ratio, likewise the HCN/HNC ratio, might increase with the kinetic temperature ($T_{\rm kin}$), but the shortage of them in warm sources still prevents us to confirm this hypothesis and shed light about their chemistry. In this work, we present the first detection of ${\rm H_2CN}$ and ${\rm H_2NC}$ towards a warm galactic source, the G+0.693-0.027 molecular cloud (with $T_{\rm kin} > 70 \, {\rm K}$), using IRAM 30m observations. We have detected multiple hyperfine components of the $N_{K_\text{a}K_\text{c}} = 1_{01} - 0_{00}$ and $2_{02} - 1_{01}$ transitions. We derived molecular abundances with respect to ${\rm H_2}$ of (6.8$\pm$1.3)$\times 10^{-11}$ for ${\rm H_2CN}$ and of (3.1$\pm$0.7)$\times 10^{-11}$ for ${\rm H_2NC}$, and a ${\rm H_2CN}$/${\rm H_2NC}$ abundance ratio of 2.2$\pm$0.5. These detections confirm that the ${\rm H_2CN}$/${\rm H_2NC}$ ratio is $\gtrsim$2 for sources with $T_{\rm kin} > 70 \, {\rm K}$, larger than the $\sim$1 ratios previously found in colder cores ($T_{\rm kin}\sim10 \, {\rm K}$). This isomeric ratio dependence with temperature cannot be fully explained with the currently proposed gas-phase formation and destruction pathways. Grain surface reactions, including the ${\rm H_2NC} \rightarrow {\rm H_2CN}$ isomerization, deserve consideration to explain the higher isomeric ratios and ${\rm H_2CN}$ abundances observed in warm sources, where the molecules can be desorbed into the gas phase through thermal and/or shock-induced mechanisms., Comment: 12 pages, 5 figures, 3 tables, 2 appendix - Accepted for publication in Monthly Notices of the Royal Astronomical Society

While Artificial Intelligence (AI) technologies are progressing fast, compliance costs have become a huge financial burden for AI startups, which are already constrained on research & development budgets. This situation creates a compliance trap, as many AI startups are not financially prepared to cope with a broad spectrum of regulatory requirements. Particularly, the complex and varying regulatory processes across the globe subtly give advantages to well-established and resourceful technology firms over resource-constrained AI startups [1]. The continuation of this trend may phase out the majority of AI startups and lead to giant technology firms' monopolies of AI technologies. To demonstrate the reality of the compliance trap, from a field deployment perspective, we delve into the details of compliance costs of AI commercial operations.

As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs. Prior work primarily focuses on metrics such as Failures In Time (FIT) rate and the Silent Data Corruption (SDC) rate, which quantify how often a device fails. Instead, this paper focuses on quantifying the DNN accuracy given that a transient error has occurred, which tells us how well a network behaves when a transient error occurs. We call this metric Resiliency Accuracy (RA). We show that existing RA formulation is fundamentally inaccurate, because it incorrectly assumes that software variables (model weights/activations) have equal faulty probability under hardware transient faults. We present an algorithm that captures the faulty probabilities of DNN variables under transient faults and, thus, provides correct RA estimations validated by hardware. To accelerate RA estimation, we reformulate RA calculation as a Monte Carlo integration problem, and solve it using importance sampling driven by DNN specific heuristics. Using our lightweight RA estimation method, we show that transient faults lead to far greater accuracy degradation than what todays DNN resiliency tools estimate. We show how our RA estimation tool can help design more resilient DNNs by integrating it with a Network Architecture Search framework.

Under the Autonomous Mobile Clinics (AMCs) initiative, we are developing, open sourcing, and standardizing health AI technologies to enable healthcare access in least developed countries (LDCs). We deem AMCs as the next generation of health care delivery platforms, whereas health AI engines are applications on these platforms, similar to how various applications expand the usage scenarios of smart phones. Facing the recent global monkeypox outbreak, in this article, we introduce AICOM-MP, an AI-based monkeypox detector specially aiming for handling images taken from resource-constrained devices. Compared to existing AI-based monkeypox detectors, AICOM-MP has achieved state-of-the-art (SOTA) performance. We have hosted AICOM-MP as a web service to allow universal access to monkeypox screening technology. We have also open sourced both the source code and the dataset of AICOM-MP to allow health AI professionals to integrate AICOM-MP into their services. Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.

Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more efficient compared to the on-vehicle-only autonomous driving paradigm. Our real-world deployment data indicates that the effectiveness of IVCAD is constrained by reliability and performance of commercial communication networks. This paper targets this exact problem, and proposes INTERNEURON, a middleware to achieve high communication reliability between intelligent roads and autonomous vehicles, in the context of IVCAD. Specifically, INTERNEURON dynamically matches IVCAD applications and the underlying communication technologies based on varying communication performance and quality needs. Evaluation results confirm that INTERNEURON reduces deadline violations by more than 95\%, significantly improving the reliability of IVCAD systems.

Factor graph is a graph representing the factorization of a probability distribution function, and has been utilized in many autonomous machine computing tasks, such as localization, tracking, planning and control etc. We are developing an architecture with the goal of using factor graph as a common abstraction for most, if not, all autonomous machine computing tasks. If successful, the architecture would provide a very simple interface of mapping autonomous machine functions to the underlying compute hardware. As a first step of such an attempt, this paper presents our most recent work of developing a factor graph accelerator for LiDAR-Inertial Odometry (LIO), an essential task in many autonomous machines, such as autonomous vehicles and mobile robots. By modeling LIO as a factor graph, the proposed accelerator not only supports multi-sensor fusion such as LiDAR, inertial measurement unit (IMU), GPS, etc., but solves the global optimization problem of robot navigation in batch or incremental modes. Our evaluation demonstrates that the proposed design significantly improves the real-time performance and energy efficiency of autonomous machine navigation systems. The initial success suggests the potential of generalizing the factor graph architecture as a common abstraction for autonomous machine computing, including tracking, planning, and control etc., Comment: ICCAD 2022

One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines., Comment: EMSOFT 2022

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communication platforms. We extensively characterize the IAAD system in the context of real-world deployment scenarios and observe that the network condition that fluctuates along the road is currently the main technical roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed "inter-frame fusion" and "planning fusion" to complement the current state-of-the-art "intra-frame fusion". We demonstrate that each fusion method has its own benefit and constraint.

Nitriles play a key role as molecular precursors in prebiotic experiments based on the RNA-world scenario for the origin of life. These chemical compounds could have been partially delivered to the young Earth from extraterrestrial objects, stressing the importance of establishing the reservoir of nitriles in the interstellar medium. We report here the detection towards the molecular cloud G+0.693-0.027 of several nitriles, including cyanic acid (HOCN), and three C$_4$H$_3$N isomers (cyanoallene, CH$_2$CCHCN; propargyl cyanide, HCCCH$_2$CN; and cyanopropyne (CH$_3$CCCN), and the tentative detections of cyanoformaldehyde (HCOCN), and glycolonitrile (HOCH$_2$CN). We have also performed the first interstellar search of cyanoacetaldehyde (HCOCH$_2$CN), which was not detected. Based on the derived molecular abundances of the different nitriles in G+0.693-0.027 and other interstellar sources, we have discussed their formation mechanisms in the ISM. We propose that the observed HOCN abundance in G+0.693-0.027 is mainly due to surface chemistry and subsequent shock-induced desorption, while HCOCN might be mainly formed through gas-phase chemistry. In the case of HOCH$_2$CN, several grain-surface routes from abundant precursors could produce it. The derived abundances of the three C$_4$H$_3$N isomers in G+0.693-0.027 are very similar, and also similar to those previously reported in the dark cold cloud TMC-1. This suggests that the three isomers are likely formed through gas-phase chemistry from common precursors, possibly unsaturated hydrocarbons (CH$_3$CCH and CH$_2$CCH$_2$) that react with the cyanide radical (CN). The rich nitrile feedstock found towards G+0.693-0.027 confirms that interstellar chemistry is able to synthesize in space molecular species that could drive the prebiotic chemistry of the RNA-world., Comment: Accepted in Frontiers in Astronomy and Space Sciences

Robotic computing has reached a tipping point, with a myriad of robots (e.g., drones, self-driving cars, logistic robots) being widely applied in diverse scenarios. The continuous proliferation of robotics, however, critically depends on efficient computing substrates, driven by real-time requirements, robotic size-weight-and-power constraints, cybersecurity considerations, and dynamically changing scenarios. Within all platforms, FPGA is able to deliver both software and hardware solutions with low power, high performance, reconfigurability, reliability, and adaptivity characteristics, serving as the promising computing substrate for robotic applications. This paper highlights the current progress, design techniques, challenges, and open research challenges in the domain of robotic computing on FPGAs., Comment: 2022 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June 13-15, 2022, Incheon, Korea

Theories on the origins of life propose that early cell membranes were synthesized from amphiphilic molecules simpler than phospholipids such as fatty alcohols. The discovery in the interstellar medium (ISM) of ethanolamine, the simplest phospholipid head group, raises the question whether simple amphiphilic molecules are also synthesized in space. We investigate whether precursors of fatty alcohols are present in the ISM. For this, we have carried out a spectral survey at 7, 3, 2 and 1 mm toward the Giant Molecular Cloud G+0.693-0.027 located in the Galactic Center using the IRAM 30m and Yebes 40m telescopes. Here, we report the detection in the ISM of the primary alcohol n-propanol (in both conformers Ga-n-C3H7OH and Aa-n-C3H7OH), a precursor of fatty alcohols. The derived column densities of n-propanol are (5.5+-0.4)x10^13 cm^-2 for the Ga conformer and (3.4+-0.3)x10^13 cm^-2 for the Aa conformer, which imply molecular abundances of (4.1+-0.3)x10^-10 for Ga-n-C3H7OH and of (2.5+-0.2)x10^-10 for Aa-n-C3H7OH. We also searched for the AGa conformer of n-butanol (AGa-n-C4H9OH) without success yielding an upper limit to its abundance of <4.1x10^-11. The inferred CH3OH:C2H5OH:C3H7OH:C4H9OH abundance ratios go as 1:0.04:0.006:<0.0004 toward G+0.693-0.027, i.e. they decrease roughly by one order of magnitude for increasing complexity. We also report the detection of both syn and anti conformers of vinyl alcohol, with column densities of (1.11+-0.08)x10^14 cm^-2 and (1.3+-0.4)x10^13 cm^-2, and abundances of (8.2+-0.6)x10^-10 and (9.6+-3.0)x10^-11, respectively. The detection of n-propanol, together with the recent discovery of ethanolamine in the ISM, opens the possibility that precursors of lipids according to theories of the origin of life, could have been brought to Earth from outer space., Comment: 15 pages, 10 figures, accepted for A&A

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system., Comment: 5 pages, 1 figure

We present the first detection of ($Z$)-1,2-ethenediol, (CHOH)$_2$, the enol form of glycolaldehyde, in the interstellar medium towards the G+0.693-0.027 molecular cloud located in the Galactic Center. We have derived a column density of (1.8$\pm$0.1)$\times$10$^{13}$ cm$^{-2}$, which translates into a molecular abundance with respect to molecular hydrogen of 1.3$\times$10$^{-10}$. The abundance ratio between glycolaldehyde and ($Z$)-1,2-ethenediol is $\sim$5.2. We discuss several viable formation routes through chemical reactions from precursors such as HCO, H$_2$CO, CHOH or CH$_2$CHOH. We also propose that this species might be an important precursor in the formation of glyceraldehyde (HOCH$_2$CHOHCHO) in the interstellar medium through combination with the hydroxymethylene (CHOH) radical., Comment: Accepted for publication in The Astrophysical Journal Letters

We report the first detection of the phosphorus monoxide ion (PO$^{+}$) in the interstellar medium. Our unbiased and very sensitive spectral survey towards the G+0.693$-$0.027 molecular cloud covers four different rotational transitions of this molecule, two of which ($J$=1$-$0 and $J$=2$-$1) appear free of contamination from other species. The fit performed, assuming Local Thermodynamic Equilibrium conditions, yields a column density of $N$=(6.0$\pm$0.7)$\times$10$^{11}$ cm$^{-2}$. The resulting molecular abundance with respect to molecular hydrogen is 4.5$\times$10$^{-12}$. The column density of PO$^{+}$ normalised by the cosmic abundance of P is larger than those of NO$^{+}$ and SO$^{+}$, normalised by N and S, by factors of 3.6 and 2.3, respectively. The $N$(PO$^{+}$)/$N$(PO) ratio is 0.12$\pm$0.03, more than one order of magnitude higher than those of $N$(SO$^{+}$)/$N$(SO) and $N$(NO$^{+}$)/$N$(NO). These results indicate that P is more efficiently ionised in the ISM than N and S. We have performed new chemical models that confirm that the PO$^+$ abundance is strongly enhanced in shocked regions with high values of cosmic-ray ionisation rates (10$^{-15}-$10$^{-14}$ s$^{-1}$), as occurs in the G+0.693$-$0.027 molecular cloud. The shocks sputter the interstellar icy grain mantles, releasing into the gas phase most of their P content, mainly in the form of PH$_3$, which is converted into atomic P, and then ionised efficiently by cosmic rays, forming P$^+$. Further reactions with O$_2$ and OH produce PO$^{+}$. The cosmic-ray ionisation of PO might also contribute significantly, which would explain the high $N$(PO$^{+}$)/$N$(PO) observed. The relatively high gas-phase abundance of PO$^{+}$ with respect to other P-bearing species stresses the relevance of this species in the interstellar chemistry of P., Comment: Accepted in Frontiers in Astronomy and Space Sciences

Simultaneous Localization and Mapping (SLAM) estimates agents' trajectories and constructs maps, and localization is a fundamental kernel in autonomous machines at all computing scales, from drones, AR, VR to self-driving cars. In this work, we present an energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization. We exploit SLAM-specific data locality, sparsity, reuse, and parallelism, and achieve >5x performance improvement over the state-of-the-art. Especially, our design is reconfigurable at runtime according to the environment to save power while sustaining accuracy and performance., Comment: First three authors contributed equally. 2 pages, 6 figures, IEEE Custom Integrated Circuits Conference (CICC), April 24-27, 2022, Newport Beach, CA, USA

As a startup company in the autonomous driving space, we have undergone four years of painful experiences dealing with a broad spectrum of regulatory requirements. Compared to the software industry norm, which spends 13% of their overall budget on compliances, we were forced to spend 42% of our budget on compliances. Our situation is not alone and, in a way, reflects the dilemma of the artificial intelligence (AI) regulatory landscape. The root cause is the lack of AI expertise in the legislative and executive branches, leading to a lack of standardization for the industry to follow. In this article, we share our first-hand experiences and advocate for the establishment of an FDA-like agency to regulate AI properly., Comment: Communications of the ACM

Autonomous driving is of great interest in both research and industry. The high cost has been one of the major roadblocks that slow down the development and adoption of autonomous driving in practice. This paper, for the first-time, shows that it is possible to run level-4 (i.e., fully autonomous driving) software on a single off-the-shelf card (Jetson AGX Xavier) for less than $1k, an order of magnitude less than the state-of-the-art systems, while meeting all the requirements of latency. The success comes from the resolution of some important issues shared by existing practices through a series of measures and innovations. The study overturns the common perceptions of the computing resources required by level-4 autonomous driving, points out a promising path for the industry to lower the cost, and suggests a number of research opportunities for rethinking the architecture, software design, and optimizations of autonomous driving., Comment: under conference review

Amines, in particular primary amines (R-NH$_2$) are closely related to the primordial synthesis of amino acids since they share the same structural backbone. However, only limited number of amines has been identified in the ISM which prevents us from studying their chemistry as well as their relation to pre-biotic species that could lead to the emergence of life. In this letter, we report the first interstellar detection of vinylamine (C$_2$H$_3$NH$_2$) and tentative detection of ethylamine (C$_2$H$_5$NH$_2$) towards the Galactic Centre cloud G+0.693-0.027. The derived abundance with respect to H$_2$ is (3.3$\pm$0.4)$\times$10$^{-10}$ and (1.9$\pm$0.5)$\times$10$^{-10}$, respectively. The inferred abundance ratios of C$_2$H$_3$NH$_2$ and C$_2$H$_5$NH$_2$ with respect to methylamine (CH$_3$NH$_2$) are $\sim$0.02 and $\sim$0.008 respectively. The derived abundance of C$_2$H$_3$NH$_2$, C$_2$H$_5$NH$_2$ and several other NH$_2$-bearing species are compared to those obtained towards high-mass and low-mass star-forming regions. Based on recent chemical and laboratory studies, possible chemical routes for the interstellar synthesis of C$_2$H$_3$NH$_2$ and C$_2$H$_5$NH$_2$ are discussed., Comment: 8 pages, 4 figures, 1 table, article accepted in Astrophysical Journal Letters

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software., Comment: Please note that this may be a special case in that Professor Gao sadly passed away on September 12th, just as we had put the finishing touches on this submission

On-surface synthesis (OSS) involving relatively high energy barriers remains challenging due to a typical dilemma: firm molecular anchor is required to prevent molecular desorption upon the reaction, whereas sufficient lateral mobility is crucial for subsequent coupling and assembly. By locking the molecular precursors on the substrate then unlocking them during the reaction, we present a strategy to address this challenge. High-yield synthesis based on well-defined decarboxylation, intermediate transition and hexamerization is demonstrated, resulting in an extended and ordered network exclusively composed of the newly-synthesized macrocyclic compound. Thanks to the steric hindrance of its maleimide group, we attain a preferential selection of the coupling. This work unlocks a promising path to enrich the reaction types and improve the coupling selectivity hence the structual homogeneity of the final product for OSS.

Transition metal carbides have sparked unprecedented enthusiasm as high-performance catalysts in recent years. Still, the catalytic properties of copper (Cu) carbide remain unexplored. By introducing subsurface carbon (C) to Cu(111), displacement reaction of proton in carboxyl acid group with single Cu atom is demonstrated at the atomic scale and room temperature. Its occurrence is attributed to the C-doping induced local charge of surface Cu atoms (up to +0.30 e/atom), which accelerates the rate of on-surface deprotonation via reduction of the corresponding energy barrier, thus enabling the instant displacement of a proton with a Cu atom when the molecules land on the surface. Such well-defined and robust Cu$^{\delta +}$ surface based on the subsurface C doping offers a novel catalytic platform for on-surface synthesis., Comment: Angewandte Chemie International Edition, Wiley-VCH Verlag, 2021

Connected and autonomous vehicles (CAVs) are promising due to their potential safety and efficiency benefits and have attracted massive investment and interest from government agencies, industry, and academia. With more computing and communication resources are available, both vehicles and edge servers are equipped with a set of camera-based vision sensors, also known as Visual IoT (V-IoT) techniques, for sensing and perception. Tremendous efforts have been made for achieving programmable communication, computation, and control. However, they are conducted mainly in the silo mode, limiting the responsiveness and efficiency of handling challenging scenarios in the real world. To improve the end-to-end performance, we envision that future CAVs require the co-design of communication, computation, and control. This paper presents our vision of the end-to-end design principle for CAVs, called 4C, which extends the V-IoT system by providing a unified communication, computation, and control co-design framework. With programmable communications, fine-grained heterogeneous computation, and efficient vehicle controls in 4C, CAVs can handle critical scenarios and achieve energy-efficient autonomous driving. Finally, we present several challenges to achieving the vision of the 4C framework., Comment: 7 pages, 4 figures, accepted by IEEE Wireless Communication Magazine

After decades of uninterrupted progress and growth, information technology has so evolved that it can be said we are entering the age of autonomous machines, but there exist many roadblocks in the way of making this a reality. In this article, we make a preliminary attempt at recognizing and categorizing the technical and non-technical challenges of autonomous machines; for each of the ten areas we have identified, we review current status, roadblocks, and potential research directions. It is hoped that this will help the community define clear, effective, and more formal development goalposts for the future., Comment: to appear in IEEE Computer Magazine

Cell membranes are a key element of life because they keep the genetic material and metabolic machinery together. All present cell membranes are made of phospholipids, yet the nature of the first membranes and the origin of phospholipids are still under debate. We report here the first detection in space of ethanolamine, NH$_2$CH$_2$CH$_2$OH, which forms the hydrophilic head of the simplest and second most abundant phospholipid in membranes. The molecular column density of ethanolamine in interstellar space is $N$=(1.51$\pm$0.07)$\times$10$^{13}$ cm$^{-2}$, implying a molecular abundance with respect to H$_2$ of (0.9-1.4)$\times$10$^{-10}$. Previous studies reported its presence in meteoritic material but they suggested that it is synthesized in the meteorite itself by decomposition of amino acids. However, we find that the proportion of the molecule with respect to water in the interstellar medium is similar to the one found in the meteorite (10$^{-6}$). These results indicate that ethanolamine forms efficiently in space and, if delivered onto early Earth, it could have contributed to the assembling and early evolution of primitive membranes., Comment: Accepted in PNAS (Proceedings of the National Academy of Sciences of the United States of America)

The chemical compounds carrying the thiol group (-SH) have been considered essential in recent prebiotic studies regarding the polymerization of amino acids. We have searched for this kind of compounds toward the Galactic Centre quiescent cloud G+0.693-0.027. We report the first detection in the interstellar space of the trans-isomer of monothioformic acid (t-HC(O)SH) with an abundance of $\sim\,$1$\,\times\,$10$^{-10}$. Additionally, we provide a solid confirmation of the gauche isomer of ethyl mercaptan (g-C$_2$H$_5$SH) with an abundance of $\sim\,$3$\,\times\,$10$^{-10}$, and we also detect methyl mercaptan (CH$_3$SH) with an abundance of $\sim\,$5$\,\times\,$10$^{-9}$. Abundance ratios were calculated for the three SH-bearing species and their OH-analogues, revealing similar trends between alcohols and thiols with increasing complexity. Possible chemical routes for the interstellar synthesis of t-HC(O)SH, CH$_3$SH and C$_2$H$_5$SH are discussed, as well as the relevance of these compounds in the synthesis of prebiotic proteins in the primitive Earth., Comment: 16 pages, 6 figures, article accepted in Astrophysical Journal Letters; typos corrected

Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings. Among all stereo matching algorithms, Efficient Large-scale Stereo (ELAS) offers one of the best tradeoffs between efficiency and accuracy. However, due to the inherent iterative process and unpredictable memory access pattern, ELAS can only run at 1.5-3 fps on high-end CPUs and difficult to achieve real-time performance on low-power platforms. In this paper, we propose an energy-efficient architecture for real-time ELAS-based stereo matching on FPGA platform. Moreover, the original computational-intensive and irregular triangulation module is reformed in a regular manner with points interpolation, which is much more hardware-friendly. Optimizations, including memory management, parallelism, and pipelining, are further utilized to reduce memory footprint and improve throughput. Compared with Intel i7 CPU and the state-of-the-art CPU+FPGA implementation, our FPGA realization achieves up to 38.4x and 3.32x frame rate improvement, and up to 27.1x and 1.13x energy efficiency improvement, respectively., Comment: Equal contributions from first two authors. Accepted by IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June 6-9, 2021

In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target. In this paper, based on the observation that the visual frontend is a major performance and energy consumption bottleneck, we present our design and implementation of an energy-efficient hardware architecture for ORB (Oriented-Fast and Rotated- BRIEF) based localization system on FPGAs. To support our multi-sensor autonomous machine localization system, we present hardware synchronization, frame-multiplexing, and parallelization techniques, which are integrated in our design. Compared to Nvidia TX1 and Intel i7, our FPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and 34.6x power reduction, respectively., Comment: To appear in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), June 6-9, 2021, Virtual

Time synchronization is a critical task in robotic computing such as autonomous driving. In the past few years, as we developed advanced robotic applications, our synchronization system has evolved as well. In this paper, we first introduce the time synchronization problem and explain the challenges of time synchronization, especially in robotic workloads. Summarizing these challenges, we then present a general hardware synchronization system for robotic computing, which delivers high synchronization accuracy while maintaining low energy and resource consumption. The proposed hardware synchronization system is a key building block in our future robotic products., Comment: IEEE RTAS 2021 Brief Industry Paper

The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:infrastructure-augmented autonomous driving (IAAD), infrastructure-guided autonomous driving (IGAD), and infrastructure-planned autonomous driving (IPAD).

In the past few years, we have observed a huge supply-demand gap for autonomous driving engineers. The core problem is that autonomous driving is not one single technology but rather a complex system integrating many technologies, and no one single academic department can provide comprehensive education in this field. We advocate to create a cross-disciplinary program to expose students with technical background in computer science, computer engineering, electrical engineering, as well as mechanical engineering. On top of the cross-disciplinary technical foundation, a capstone project that provides students with hands-on experiences of working with a real autonomous vehicle is required to consolidate the technical foundation., Comment: to appear in IEEE Computer Magazine

To date, about two dozen low-mass embedded protostars exhibit rich spectra with lines of complex organic molecule (COM). These protostars seem to possess different enrichment in COMs. However, the statistics of COM abundance in low-mass protostars are limited by the scarcity of observations. This study introduces the Perseus ALMA Chemistry Survey (PEACHES), which aims at unbiasedly characterizing the chemistry of COMs toward the embedded (Class 0/I) protostars in the Perseus molecular cloud. Of 50 embedded protostars surveyed, 58% of them have emission from COMs. A 56%, 32%, and 40% of the protostars have CH$_3$OH, CH$_3$OCHO, and N-bearing COMs, respectively. The detectability of COMs depends neither on the averaged continuum brightness temperature, a proxy of the H$_2$ column density, nor on the bolometric luminosity and the bolometric temperature. For the protostars with detected COMs, CH$_3$OH has a tight correlation with CH$_3$CN, spanning more than two orders of magnitude in column densities normalized by the continuum brightness temperature, suggesting a chemical relation between CH$_3$OH and CH$_3$CN and a large chemical diversity in the PEACHES samples at the same time. A similar trend with more scatter is also found between all identified COMs, hinting at a common chemistry for the sources with COMs. The correlation between COMs is insensitive to the protostellar properties, such as the bolometric luminosity and the bolometric temperature. The abundance of larger COMs (CH$_3$OCHO and CH$_3$OCH$_3$) relative to that of smaller COMs (CH$_3$OH and CH$_3$CN) increases with the inferred gas column density, hinting at an efficient production of complex species in denser envelopes., Comment: 55 pages, 9 tables, 24 figures. Updated after publication in ApJ. Figure 18 appears as a figure set in ApJ

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe. A critical challenge to our -- and any -- autonomous machine is accurate and efficient localization under resource constraints, which has fueled specialized localization accelerators recently. Prior acceleration efforts are point solutions in that they each specialize for a specific localization algorithm. In real-world commercial deployments, however, autonomous machines routinely operate under different environments and no single localization algorithm fits all the environments. Simply stacking together point solutions not only leads to cost and power budget overrun, but also results in an overly complicated software stack. This paper demonstrates our new software-hardware co-designed framework for autonomous machine localization, which adapts to different operating scenarios by fusing fundamental algorithmic primitives. Through characterizing the software framework, we identify ideal acceleration candidates that contribute significantly to the end-to-end latency and/or latency variation. We show how to co-design a hardware accelerator to systematically exploit the parallelisms, locality, and common building blocks inherent in the localization framework. We build, deploy, and evaluate an FPGA prototype on our next-generation self-driving cars. To demonstrate the flexibility of our framework, we also instantiate another FPGA prototype targeting drones, which represent mobile autonomous machines. We achieve about 2x speedup and 4x energy reduction compared to widely-deployed, optimized implementations on general-purpose platforms.

PerceptIn develops and commercializes autonomous vehicles for micromobility around the globe. This paper makes a holistic summary of PerceptIn's development and operating experiences. This paper provides the business tale behind our product, and presents the development of the computing system for our vehicles. We illustrate the design decision made for the computing system, and show the advantage of offloading localization workloads onto an FPGA platform., Comment: ASP-DAC 2021 Invited Paper

Autonomous Driving is now the promising future of transportation. As one basis for autonomous driving, High Definition Map (HD map) provides high-precision descriptions of the environment, therefore it enables more accurate perception and localization while improving the efficiency of path planning. However, an extremely large amount of map data needs to be transmitted during driving, thus posing great challenge for real-time and safety requirements for autonomous driving. To this end, we first demonstrate how the existing data distribution mechanism can support HD map services. Furthermore, considering the constraints of vehicle power, vehicle speed, base station bandwidth, etc., we propose a HD map data distribution mechanism on top of Vehicle-to-Infrastructure (V2I) data transmission. By this mechanism, the map provision task is allocated to the selected RSU nodes and transmits proportionate HD map data cooperatively. Their works on map data loading aims to provide in-time HD map data service with optimized in-vehicle energy consumption. Finally, we model the selection of RSU nodes into a partial knapsack problem and propose a greedy strategy-based data transmission algorithm. Experimental results confirm that within limited energy consumption, the proposed mechanism can ensure HD map data service by coordinating multiple RSUs with the shortest data transmission time.

Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work., Comment: To appear in IEEE Circuits and Systems Magazine (CAS-M), 2021

Most business decisions are made with analysis, but some are judgment calls not susceptible to analysis due to time or information constraints. In this article, we present a real-life case study of critical business decision making of PerceptIn, an autonomous driving technology startup. In early years of PerceptIn, PerceptIn had to make a decision on the design of computing systems for its autonomous vehicle products. By providing details on PerceptIn's decision process and the results of the decision, we hope to provide some insights that can be beneficial to entrepreneurs and engineering managers in technology startups., Comment: to appear in IEEE Engineering Management Review

Compressing massive LiDAR point clouds in real-time is critical to autonomous machines such as drones and self-driving cars. While most of the recent prior work has focused on compressing individual point cloud frames, this paper proposes a novel system that effectively compresses a sequence of point clouds. The idea to exploit both the spatial and temporal redundancies in a sequence of point cloud frames. We first identify a key frame in a point cloud sequence and spatially encode the key frame by iterative plane fitting. We then exploit the fact that consecutive point clouds have large overlaps in the physical space, and thus spatially encoded data can be (re-)used to encode the temporal stream. Temporal encoding by reusing spatial encoding data not only improves the compression rate, but also avoids redundant computations, which significantly improves the compression speed. Experiments show that our compression system achieves 40x to 90x compression rate, significantly higher than the MPEG's LiDAR point cloud compression standard, while retaining high end-to-end application accuracies. Meanwhile, our compression system has a compression speed that matches the point cloud generation rate by today LiDARs and out-performs existing compression systems, enabling real-time point cloud transmission.

One of the proposed scenarios for the origin of life is the primordial RNA world, which considers that RNA molecules were likely responsible for the storage of genetic information and the catalysis of biochemical reactions in primitive cells, before the advent of proteins and DNA. In the last decade, experiments in the field of prebiotic chemistry have shown that RNA nucleotides can be synthesized from relatively simple molecular precursors, most of which have been found in space. An important exception is hydroxylamine, NH$_2$OH, which, despite several observational attempts, it has not been detected in space yet. Here we present the first detection of NH$_2$OH in the interstellar medium towards the quiescent molecular cloud G+0.693-0.027 located in the Galactic Center. We have targeted the three groups of transitions from the $J$=2$-$1, 3$-$2, and 4$-$3 rotational lines, detecting 5 transitions that are unblended or only slightly blended. The derived molecular abundance of NH$_2$OH is (2.1$\pm$0.9)$\times$10$^{-10}$. From the comparison of the derived abundance of NH$_2$OH and chemically related species, with those predicted by chemical models and measured in laboratory experiments, we favor the formation of NH$_2$OH in the interstellar medium via hydrogenation of NO on dust grain surfaces, with possibly a contribution of ice mantle NH$_3$ oxidation processes. Further laboratory studies and quantum chemical calculations are needed to completely rule out the formation of NH$_2$OH in the gas phase., Comment: Accepted in the Astrophysical Journal Letters