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1. New Species of Malleastrum (Meliaceae) from Madagascar

   https://doi.org/10.3417/2020509  

   Pérez, Vanessa; Miller, James S. (May 2020, Novon, A Journal for Botanical Nomenclature)

   Three new species of Malleastrum (Baill.) J.-F. Leroy are described from Madagascar and compared to the most similar of the 22 other accepted species in the genus. Malleastrum acuminatum V. Pérez & J. S. Mill., M. longifoliolatum V. Pérez & J. S. Mill., and M. obovatum V. Pérez & J. S. Mill. are described, differences from the most similar species are presented, and their conservation status is discussed. 

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2. L oo p D e l t a : E m b e dd i n g L o c a li t y - a w a r e O pp o r t un i s t i c D e l t a C o m p r e ss i o n i n I n li n e D e dup li c a t i o n f o r Hi g h l y E ffic i e n t D a t a R e du c t i o n

   Zhang, Yucheng; Jiang, Hong; Feng, Dan; Jiang Nan; Qui, Taorong; Huang, Wei (July 2023, USENIX annual technical conference)

   A s a c om pl e men t t o da ta d edupli cat ion , de lta c om p ress i on fu r- t he r r edu c es t h e dat a vo l u m e by c o m pr e ssi n g n o n - dup li c a t e d ata chunk s r e l a t iv e to t h e i r s i m il a r chunk s (bas e chunk s). H ow ever, ex is t i n g p o s t - d e dup li c a t i o n d e l t a c o m pr e ssi o n a p- p ro a ches fo r bac kup s t or ag e e i t h e r su ffe r f ro m t h e l ow s i m - il a r i t y b e twee n m any de te c ted c hun ks o r m i ss so me po t e n - t i a l s i m il a r c hunks , o r su ffer f r om l ow (ba ckup and r es t ore ) th r oug hpu t du e t o extr a I/ Os f or r e a d i n g b a se c hun ks o r a dd a dd i t i on a l s e r v i c e - d i s r up t ive op e r a t i on s to b a ck up s ys t em s. I n t h i s pa p e r, w e pr opo se L oop D e l t a t o a dd ress the above - m e n t i on e d prob l e m s by an e nha nced em b e ddi n g d e l t a c o m p - r e ss i on sc heme i n d e dup li c a t i on i n a non - i n t ru s ive way. T h e e nha nce d d elt a c o mpr ess ion s che m e co m b in e s f our key t e c h - ni qu e s : (1) du a l - l o c a li t y - b a s e d s i m il a r i t y t r a c k i n g to d e t ect po t e n t i a l si m il a r chun k s b y e x p l o i t i n g both l o g i c a l and ph y - s i c a l l o c a li t y, ( 2 ) l o c a li t y - a wa r e pr e f e t c h i n g to pr efe tc h ba se c hun ks to a vo i d ex t ra I/ Os fo r r e a d i n g ba s e chun ks on t h e w r i t e p at h , (3) c a che -aware fil t e r to avo i d ext r a I/Os f or b a se c hunk s on t he read p at h, a nd (4) i nver sed de l ta co mpressi on t o perf orm de lt a co mpress i o n fo r d at a chunk s t hat a re o th e r wi se f o r b i dd e n to s er ve as ba se c hunk s by r ew r i t i n g t e c hn i qu e s d e s i g n e d t o i m p r ove r es t o re pe rf o rma nc e. E x p e r i m e n t a l re su lts indi ca te t hat L oop D e l t a i ncr ea se s t he c o m pr e ss i o n r a t i o by 1 .2410 .97 t i m e s on t op of d e dup li c a - t i on , wi t hou t no t a b l y a ffe c t i n g th e ba ck up th rou ghpu t, a nd i t i m p r ove s t he res to re p er fo r m an ce b y 1.23.57 t i m e 

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3. Identification of Caribbean ciguatoxins from benthic dinoflagellates: Advances in knowledge on toxin chemistry and analysis.

   Mudge, E M; Lewis, N; Miles, C O; McCarron, P; Uhlig, S; Kryuchkov, F; Gwinn, J K; Robertson, A (November 2023, Harmful Algae UNESCO IOC)

   Mudge, E. M.; Lewis, N.; Miles, C. O.; McCarron, P.; Uhlig, S.; Kryuchkov, F.; Gwinn, J. K.; Robertson, A.(November, 2023). Identification of Caribbean ciguatoxins from benthic dinoflagellates: Advances in knowledge on toxin chemistry and analysis. Harmful Algae UNESCO IOC, 73, 10. 

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4. Barriers for the performance of graph neural networks (GNN) in discrete random structures

   https://doi.org/10.1073/pnas.2314092120  

   Gamarnik, David (November 2023, Proceedings of the National Academy of Sciences)

   Recently, graph neural network (GNN)-based algorithms were proposed to solve a variety of combinatorial optimization problems [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber,Nat. Mach. Intell.4, 367–377 (2022)]. GNN was tested in particular on randomly generated instances of these problems. The publication [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber,Nat. Mach. Intell.4, 367–377 (2022)] stirred a debate whether the GNN-based method was adequately benchmarked against best prior methods. In particular, critical commentaries [M. C. Angelini, F. Ricci-Tersenghi,Nat. Mach. Intell.5, 29–31 (2023)] and [S. Boettcher,Nat. Mach. Intell.5, 24–25 (2023)] point out that a simple greedy algorithm performs better than the GNN. We do not intend to discuss the merits of arguments and counterarguments in these papers. Rather, in this note, we establish a fundamental limitation for running GNN on random instances considered in these references, for a broad range of choices of GNN architecture. Specifically, these barriers hold when the depth of GNN does not scale with graph size (we note that depth 2 was used in experiments in [M. J. Schuetz, J. K. Brubaker, H. G. Katzgraber,Nat. Mach. Intell.4, 367–377 (2022)]), and importantly, these barriers hold regardless of any other parameters of GNN architecture. These limitations arise from the presence of the overlap gap property (OGP) phase transition, which is a barrier for many algorithms, including importantly local algorithms, of which GNN is an example. At the same time, some algorithms known prior to the introduction of GNN provide best results for these problems up to the OGP phase transition. This leaves very little space for GNN to outperform the known algorithms, and based on this, we side with the conclusions made in [M. C. Angelini, F. Ricci-Tersenghi,Nat. Mach. Intell.5, 29–31 (2023)] and [S. Boettcher,Nat. Mach. Intell.5, 24–25 (2023)]. 

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5. Expedition 397 methods

   https://doi.org/10.14379/iodp.proc.397.102.2024  

   Abrantes, F; Hodell, DA; Alvarez_Zarikian, CA; Brooks, HL; Clark, WB; Dauchy-Tric, LFB; dos_Santos_Rocha, V; Flores, J-A; Herbert, TD; Hines, SKV; et al (June 2024, Proceedings of the International Ocean Discovery Program Expedition reports)

   This chapter provides an overview of the procedures and methods employed for coring operations and in the shipboard laboratories of the R/V JOIDES Resolution during International Ocean Discovery Program (IODP) Expedition 397. The laboratory information applies only to shipboard work described in the Expedition Report section of the Expedition 397 Proceedings of the International Ocean Discovery Program volume that includes the shipboard sample registry, imaging and analytical instruments, core description tools, and the Laboratory Information Management System (LIMS) database. The shipboard workflow followed standard IODP procedures (as previously described by, e.g., Huber et al., 2019; Winckler et al., 2021; Planke et al., 2023), with revisions and refinements as described in this chapter. Methods used by investigators for shore-based analyses of Expedition 397 data will be documented in separate publications. All shipboard scientists contributed to this volume with the following primary responsibilities (authors are listed in alphabetical order; see Expedition 397 scientists for contact information): Summary chapter: Expedition 397 Scientists Methods and site chapters: Background and objectives: F. Abrantes, D. Hodell Operations: C.A. Alvarez Zarikian, K. Grigar Lithostratigraphy: H.L. Brooks, J.M. Link, J. McManus, C. Pallone, X. Pang, E. Salgueiro, V. dos Santos Rocha, J. Yu Biostratigraphy: C.A. Alvarez Zarikian, W. Clark, J.-A. Flores, M. Peral, K. Verma Paleomagnetism: L. Dauchy-Tric, C. Xuan Geochemistry: S. Hines, B. Mitsunaga, L. Nana Yobo, J. Wu Physical properties and downhole measurements: H.-H.M. Huang, H. Ikeda, J. Kuroda, S. Sanchez Stratigraphic correlation: T. Herbert, H.-H.M. Huang, S. Kaboth-Bahr This introductory section provides an overview of drilling and coring operations, core handling, curatorial conventions, depth scale terminology, and the sequence of shipboard analyses. Subsequent sections of this chapter document specific laboratory instruments and methods in detail. 

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