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In this letter we consider the problem of identifying parameters of a particular class of Markov chains, called Bernoulli Autoregressive ( BAR) processes. The structure of any BAR model is encoded by a directed graph. Incoming edges to a node in the graph indicate that the state of the node at a particular time instant is influenced by the states of the corresponding parental nodes in the previous time instant. The associated edge weights determine the corresponding level of influence from each parental node. In the simplest setup, the Bernoulli parameter of a particular node’s state variable is a convex combination of the parental node states in the previous time instant and an additional Bernoulli noise random variable. This letter focuses on the problem of edge weight identification using Maximum Likelihood( ML) estimation and proves that the ML estimator is strongly consistent for two variants of the BAR model. We additionally derive closed-form estimators for the aforementioned two variants and prove their strong consistency.

Human trajectory prediction is critical for autonomous platforms like self-driving cars or social robots. We present a latent belief energy-based model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The low dimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multimodality of pedestrian trajectory distributions. LB-EBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LB-EBM yields a belief vector which is used to make a path plan, which then in turn helps to predict a long-range trajectory. The effectiveness of LB-EBM and the two-step approach are supported by strong empirical results. Our model is able to make accurate, multi-modal, and social compliant trajectory predictions and improves over prior state-of-the-arts performance on the Stanford Drone trajectory prediction benchmark by 10:9% and on the ETH-UCY benchmark by 27:6%.

For the first ∼3 yrs after the binary neutron star merger event GW 170817, the radio and X-ray radiation has been dominated by emission from a structured relativistic off-axis jet propagating into a low-density medium withn< 0.01 cm⁻³. We report on observational evidence for an excess of X-ray emission atδt> 900 days after the merger. WithL_x≈ 5 × 10³⁸erg s⁻¹at 1234 days, the recently detected X-ray emission represents a ≥3.2σ(Gaussian equivalent) deviation from the universal post-jet-break model that best fits the multiwavelength afterglow at earlier times. In the context ofJetFitafterglow models, current data represent a departure with statistical significance ≥3.1σ, depending on the fireball collimation, with the most realistic models showing excesses at the level of ≥3.7σ. A lack of detectable 3 GHz radio emission suggests a harder broadband spectrum than the jet afterglow. These properties are consistent with the emergence of a new emission component such as synchrotron radiation from a mildly relativistic shock generated by the expanding merger ejecta, i.e., a kilonova afterglow. In this context, we present a set of ab initio numerical relativity binary neutron star (BNS) merger simulations that show that an X-ray excess supports the presence of a high-velocity tail in the mergermore »ejecta, and argues against the prompt collapse of the merger remnant into a black hole. Radiation from accretion processes on the compact-object remnant represents a viable alternative. Neither a kilonova afterglow nor accretion-powered emission have been observed before, as detections of BNS mergers at this phase of evolution are unprecedented.

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ABSTRACT Posttranslational modification of a protein, either alone or in combination with other modifications, can control properties of that protein, such as enzymatic activity, localization, stability, or interactions with other molecules. N -ε-Lysine acetylation is one such modification that has gained attention in recent years, with a prevalence and significance that rival those of phosphorylation. This review will discuss the current state of the field in bacteria and some of the work in archaea, focusing on both mechanisms of N -ε-lysine acetylation and methods to identify, quantify, and characterize specific acetyllysines. Bacterial N -ε-lysine acetylation depends on both enzymatic and nonenzymatic mechanisms of acetylation, and recent work has shed light into the regulation of both mechanisms. Technological advances in mass spectrometry have allowed researchers to gain insight with greater biological context by both (i) analyzing samples either with stable isotope labeling workflows or using label-free protocols and (ii) determining the true extent of acetylation on a protein population through stoichiometry measurements. Identification of acetylated lysines through these methods has led to studies that probe the biological significance of acetylation. General and diverse approaches used to determine the effect of acetylation on a specific lysine will be covered.