A new mononuclear low-spin nickel(II) dithiolato complex, [NiL 2 ] (1), reacts with copper iodide to form the heterooctanuclear cluster [{Ni(L) 2 } 2 (CuI) 6 ] (2) with four trigonal-planar CuI 2 S and two tetrahedral CuI 2 S 2 sites; anagostic interactions between the nickel(II) ions and aromatic protons have been demonstrated by variable-temperature NMR studies to pertain in solution.Nickel thiolato complexes, including (hetero-)multinuclear [NiFe], [NiCu], [NiZn] and [NiNi] units, are of interest in the context of their rich redox chemistry 1,2 and structural diversity in supramolecular architectures. 3,4 Furthermore, they are important as synthetic models 1,3,5-8 for environmentally and industrially significant enzymes like hydrogenases, superoxide dismutases and CODH/ACS. It is noteworthy that the bifunctional enzyme CODH/ACS has an important role in the global carbon cycle as the C-cluster, an Ni-Fe-S centre, of this enzyme reduces carbon dioxide to carbon monoxide and the A-cluster assembles acetyl-CoA from a methyl group (Chart 1), coenzyme-A and the CO generated by the C-cluster. 9 The A-cluster is a complex metallocofactor, containing an Fe 4 S 4 group connected by cysteine bridging to a dinuclear [M p Ni d ] site, where the proximal metal M p is predominantly Cu in the as-isolated enzyme from native Moorella thermoacetica. 10 However, [NiNi] and [ZnNi] forms are also known to have been isolated and well studied. [11][12][13][14] The distal nickel Ni d is in a square-planar (NiN 2 S 2 ) geometry derived from two backbone carboxamido nitrogens and two Cys-S thiolates. The Ni d is bridged through the two Cys-S donors to the proximal metal M p that is in a tetrahedral coordination environment. A fourth nonprotein ligand is bound to the M p in addition to the Cys-S bridging to the Fe 4 S 4 to complete the coordination sphere. The focus of our attention is to study the chemistry involving the synthesis and reactivity of nickel thiolate complexes in relation with the structure and function of protein active sites, as described above. 1,3,[5][6][7][8] Herein, we report the synthesis of a new bidentate S 2 thioether-thiolate ligand (L),w the mononuclear low-spin nickel complex (1)w and the hetero-octanuclear nickel(II) copper(I) cluster complex [{Ni(L) 2 } 2 (CuI) 6 ] (2),z which contains two NiS 4 units, four trigonal-planar CuI 2 S and two tetrahedral CuI 2 S 2 sites.The reaction of Ni(acac) 2 with two equivalents of the thiouronium chloride salt of the ligand, in the presence of two equivalents of tetramethylammonium hydroxide, led to an immediate color change to deep brown and the new low-spin square-planar complex [Ni(L) 2 ] (1) was isolated as flocculent reddish-brown crystals in high yield (Scheme 1). Equimolar solutions of Ni(L) 2 (1) in dichloromethane and copper(I) iodide in acetonitrile were mixed under argon and stirred for an hour to yield a dark brown precipitate. A saturated solution of this product in absolute ethanol was left for slow evaporation under argon atmosphere and dark brown crys...

Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters’ preferences. In practice, voters do not provide a full ranking; instead, each voter provides their favorite K candidates, potentially in ranked order. The election organizer must choose K and an aggregation rule. We provide a theoretical framework to make these choices. Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election recovers the asymptotic outcome. Given the voter choice distribution, the election planner can thus identify the rate optimal mechanism. Earlier work in this area provides coarse order-of-magnitude guaranties which are not sufficient to make such choices. Our framework further resolves questions of when randomizing between multiple mechanisms may improve learning for arbitrary voter noise models. Finally, we use data from 5 large participatory budgeting elections that we organized across several US cities, along with other ranking data, to demonstrate the utility of our methods. In particular, we find that historically such elections have set K too low and that picking the right mechanism can be the difference between identifying the ultimate winner with only a 80% probability or a 99.9% probability after 400 voters.

In 2020 the tragic murder of George Floyd at the hands of law enforcement ignited and intensified nationwide protests, demanding changes in police funding and allocation. This happened during a budgeting feedback exercise where residents of Austin, Texas were invited to share opinions on the budgets of various city service areas, including the Police Department, on an online platform designed by our team. Daily responses increased by a hundredfold and responses registered after the "exogenous shock" overwhelmingly advocated for reducing police funding. This opinion shift far exceeded what we observed in 14 other Participatory Budgeting elections on our Participatory Budgeting Platform, and can't be explained by shifts in the respondent demographics. Analysis of the results from an Austin budgetary feedback exercise in 2021 and a follow-up survey indicates that the opinion shift from 2020 persisted, with the opinion gap on police funding widening. We conclude that there was an actual change of opinion regarding police funding. This study not only sheds light on the enduring impact of the 2020 events and protests on public opinion, but also showcases the value of analysis of clustered opinions as a tool in the evaluation toolkit of survey organizers.

The wide adoption of digital technologies in the cultural heritage sector has promoted the emergence of new, distributed ways of working, communicating, and investigating cultural products and services. In particular, collaborative online platforms and crowdsourcing mechanisms have been widely adopted in the effort to solicit input from the community and promote engagement. In this work, we provide an extensive analysis of the Wiki Loves Monuments initiative, an annual, international photography contest in which volunteers are invited to take pictures of the built cultural heritage and upload them to Wikimedia Commons. We explore the geographical, temporal, and topical dimensions across the 2010-2021 editions. We first adopt a set of CNNs-based artificial systems that allow the learning of deep scene features for various scene recognition tasks, exploring cross-country (dis)similarities. To overcome the rigidity of the framework based on scene descriptors, we train a deep convolutional neural network model to label a photo with its country of origin. The resulting model captures the best representation of a heritage site uploaded in a country and it allows the domain experts to explore the complexity of cross-national architectural styles. Finally, as a validation step, we explore the link between architectural heritage and intangible cultural values, operationalized using the framework developed within the World Value Survey research program. We observe that cross-country cultural similarities match to a fair extent the interrelations emerging in the architectural domain. We think this study contributes to highlighting the richness and the potential of the Wikimedia data and tools ecosystem to act as a scientific object for art historians, iconologists, and archaeologists.

We consider the problem of allocating divisible items among multiple agents, and consider the setting where any agent is allowed to introduce diversity constraints on the items they are allocated. We motivate this via settings where the items themselves correspond to user ad slots or task workers with attributes such as race and gender on which the principal seeks to achieve demographic parity. We consider the following question: When an agent expresses diversity constraints into an allocation rule, is the allocation of other agents hurt significantly? If this happens, the cost of introducing such constraints is disproportionately borne by agents who do not benefit from diversity. We codify this via two desiderata capturing robustness. These are no negative externality -other agents are not hurt -and monotonicity -the agent enforcing the constraint does not see a large increase in value. We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules. We finally perform an empirical simulation on real-world data that models ad allocations to show that this gap between Nash Welfare and other rules persists in the wild.