And now, for the first papers of 2018! It’s a slow start with only four papers (or technical three “standard property testing” papers, and one non-standard paper).

**Adaptive Boolean Monotonicity Testing in Total Influence Time**, by Deeparnab Chakrabarty and C. Seshadhri (arXiv ECCC). The problem of testing monotonicity of Boolean functions \(f:\{0,1\}^n \to \{0,1\}\) has seen a lot of progress recently. After the breakthrough results of Khot-Minzer-Safra giving a \(\widetilde{O}(\sqrt{n})\) non-adaptive tester, Blais-Belovs proved the first polynomial lower bound for adaptive testers, recently improved to \(O(n^{1/3})\) by Chen, Waingarten, and Xi. The burning question: does adaptivity help? This result shows gives an adaptive tester that runs in \(O(\mathbf{I}(f))\), the total influence of \(f\). Thus, we can beat these lower bounds (and the non-adaptive complexity) for low influence functions.

**Adaptive Lower Bound for Testing Monotonicity on the Line**, by Aleksandrs Belovs (arXiv). More monotonicity testing! But this time on functions \(f:[n] \to [r]\). Classic results on property testing show that monotonicity can be tested in \(O(\varepsilon^{-1}\log n)\) time. A recent extension of these ideas by Pallavoor-Raskhodnikova-Varma replace the \(\log n\) with \(\log r\), an improvement for small ranges. This paper proves an almost matching lower bound of \((\log r)/(\log\log r)\). The main construction can be used to give a substantially simpler proof of an \(\Omega(d\log n)\) lower bound for monotonicity testing on hypergrids \(f:[n]^d \to \mathbb{N}\). The primary contribution is giving explicit lower bound constructions and avoiding Ramsey-theoretical arguments previously used for monotonicity lower bounds.

**Earthmover Resilience and Testing in Ordered Structures**, by Omri Ben-Eliezer and Eldar Fischer (arXiv). While there has been much progress on understanding the constant-time testability of graphs, the picture is not so clear for *ordered* structures (such as strings/matrices). There are a number of roadblocks (unlike the graph setting): there are no canonical testers for, say, string properties, there are testable properties that are not tolerant testable, and Szemeredi-type regular partitions may not exist for such properties. The main contribution of this paper is to find a natural, useful condition on ordered properties such that the above roadblocks disappear hold, and thus we have strong testability results. The paper introduces the notion of Earthmover Resilient properties (ER). Basically, a graph properties is a property of symmetric matrices that is invariant under permutation of base elements (rows/columns). An ER property is one that is invariant under mild perturbations of the base elements. The natural special cases of ER properties are those over strings and matrices, and it is includes all graph properties as well as image properties studied in this context. There are a number of characterization results. Most interestingly, for ER properties of images (binary matrices) and edge-colored ordered graphs, the following are equivalent: existence of canonical testers, tolerant testability, and regular reducibility.

**Nondeterminisic Sublinear Time Has Measure 0 in P**, by John Hitchcock and Adewale Sekoni (arXiv). Not your usual property testing paper, but on sublinear (non-deterministic) time nonetheless. Consider the complexity class of \(NTIME(n^\delta)\), for \(\delta < 1\). This paper shows that this complexity class is a "negligible" fraction of \(P\). (The analogous result was known for \(\alpha < 1/11\) by Cai-Sivakumar-Strauss.) This requires a technical concept of measure for languages and complexity classes. While I don’t claim to understand the details, the math boils down to understanding the following process. Consider some language \(\mathcal{L}\) and a martingale betting process that repeatedly tries to guess the membership of strings \(x_1, x_2, \ldots\) in a well-defined order. If one can define such a betting process with a limited computational resource that has unbounded gains, then \(\mathcal{L}\) has measure 0 with respect to that (limited) resource.