N-(5-Ethylsulfanyl-1,3,4-thiadiazol-2-yl)-2-(4,5,6,7-tetrahydrothieno[3,2-c]pyri­din-5-yl)acetamide

In the title compound, C13H16N4OS3, a thienopyridine­derivative, the tetra­hydro­pyridine ring exhibits a half-chair conformation, and the folded conformation of the mol­ecule is defined by the N—C—C—N torsion angle of −78.85 (16)°. The crystal packing features inter­molecular C—H⋯N, N—H⋯N and C—H⋯O hydrogen bonds

N-(2-Chloro­pyrimidin-4-yl)-N,2-di­methyl-2H-indazol-6-amine

In the title compound, C13H12ClN5, which is a derivative of the anti­tumor agent pazopanib {systematic name: 5-[[4-[(2,3-di­methyl-2H-indazol-6-yl)methylamino]-2-pyrimidinyl]amino]-2-methylbenzolsulfonamide}, the indazole and pyrim­idine fragments form a dihedral angle of 62.63 (5)°. In the crystal, pairs of mol­ecules related by twofold rotational symmetry are linked into dimers through π–π inter­actions between the indazole ring systems [centroid–centroid distance = 3.720 (2) Å]. Weak inter­molecular C—H⋯N hydrogen bonds further assemble these dimers into columns propagated in [001]

1-[4-(3-{[5-(4-Chloro­phen­yl)furan-2-yl]methyl­idene­amino}-2,5-dioxoimidazol­idin-1-yl)but­yl]-4-methyl­piperazine-1,4-diium dichloride hemihydrate

The title compound, C23H30ClN5O3 2+·2Cl−·0.5H2O, was synthesized by N-alkyl­ation of 1-({[5-(4-chloro­phen­yl)-2-furan­yl]methyl­ene}amino)-2,4-imidazolidinedione with 1-bromo-4-chloro­butane, and N-methyl­piperazine. In the crystal, the cations, anions and water mol­ecules are linked by O—H⋯Cl and N—H⋯Cl hydrogen bonds

1-[4-(4-Nitro­phen­yl)piperazin-1-yl]-2-(4,5,6,7-tetra­hydro­thieno[3,2-c]pyridin-5-yl)ethanone

The title compound, C19H22N4O3S, comprises a thienopyridine moiety which is characteristic for anti­platelet agents of the clopidogrel class of compounds. In the crystal, inversion dimers are formed through pairs of C—H⋯O inter­actions. The benzene ring plane and the nitro plane are almost coplanar, with a dihedral angle of 0.83 (2)°. The piperazine ring adopts a chair conformation

Intensity-free Integral-based Learning of Marked Temporal Point Processes

In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) $p^*(m,t)$ for inter-event time $t$ and mark $m$, conditioned on the history. The majority of existing studies predefine intensity functions. Their utility is challenged by specifying the intensity function's proper form, which is critical to balance expressiveness and processing efficiency. Recently, there are studies moving away from predefining the intensity function -- one models $p^*(t)$ and $p^*(m)$ separately, while the other focuses on temporal point processes (TPPs), which do not consider marks. This study aims to develop high-fidelity $p^*(m,t)$ for discrete events where the event marks are either categorical or numeric in a multi-dimensional continuous space. We propose a solution framework IFIB (\underline{I}ntensity-\underline{f}ree \underline{I}ntegral-\underline{b}ased process) that models conditional joint PDF $p^*(m,t)$ directly without intensity functions. It remarkably simplifies the process to compel the essential mathematical restrictions. We show the desired properties of IFIB and the superior experimental results of IFIB on real-world and synthetic datasets. The code is available at \url{https://github.com/StepinSilence/IFIB}

Explainable History Distillation by Marked Temporal Point Process

Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this paper, we build a machine learning system to automatically generate explanations of happened events from history by \gls{ca} based on the \acrfull{tpp}. Specifically, we propose a new task called \acrfull{ehd}. This task requires a model to distill as few events as possible from observed history. The target is that the event distribution conditioned on left events predicts the observed future noticeably worse. We then regard distilled events as the explanation for the future. To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}. This work fills the gap between our task and existing works, which only spot the difference between factual and counterfactual worlds after applying a predefined modification to the environment. Experiment results on Retweet and StackOverflow datasets prove that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines and can reveal the rationale underpinning real-world processes

Bayesian Criterion for Re-randomization

Re-randomization has gained popularity as a tool for experiment-based causal inference due to its superior covariate balance and statistical efficiency compared to classic randomized experiments. However, the basic re-randomization method, known as ReM, and many of its extensions have been deemed sub-optimal as they fail to prioritize covariates that are more strongly associated with potential outcomes. To address this limitation and design more efficient re-randomization procedures, a more precise quantification of covariate heterogeneity and its impact on the causal effect estimator is in a great appeal. This work fills in this gap with a Bayesian criterion for re-randomization and a series of novel re-randomization procedures derived under such a criterion. Both theoretical analyses and numerical studies show that the proposed re-randomization procedures under the Bayesian criterion outperform existing ReM-based procedures significantly in effectively balancing covariates and precisely estimating the unknown causal effect