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Expander hierarchies for normalized cuts on graphs
No full textExpander decompositions of graphs have significantly advanced the understanding of many classical graph problems and led to numerous fundamental theoretical results. However, their adoption in practice has been hindered due to their inherent intricacies and large hidden factors in their asymptotic running times. Here, we introduce the first practically efficient algorithm for computing expander decompositions and their hierarchies and demonstrate its effectiveness and utility by incorporating it as the core component in a novel solver for the normalized cut graph clustering objective.
Our extensive experiments on a variety of large graphs show that our expander-based algorithm outperforms state-of-the-art solvers for normalized cut with respect to solution quality by a large margin on a variety of graph classes such as citation, e-mail, and social networks or web graphs while remaining competitive in running time
An empirical study of consensus protocols’ DoS resilience
No full textWith the proliferation of blockchain technology in high-value sectors, consensus protocols are becoming critical infrastructures. The rapid innovation cycle in Byzantine fault tolerant (BFT) consensus protocols has culminated in HotStuff, which provides linear message complexity in the partially synchronous setting. To achieve this, HotStuff leverages a leader that collects, aggregates, and broadcasts the messages of other validators. This paper analyzes the security implications of such approaches in practice, from the perspective of liveness and availability.
By implementing attacks in a globally-distributed testbed, we show that state-of-the-art leader-based protocols are vulnerable to denial-of-service (DoS) attacks on the leader. Our attacks, demonstrated on committees of up to 64 validators, manage to disrupt liveness within seconds, using only a few tens of Mbps of attack bandwidth per validator. Crucially, the cost and effectiveness of the attacks are independent of the committee size. Based on the outcome of these experiments, we then propose and test effective mitigations. Our findings show that advancements in both protocol design and network-layer defenses can greatly improve the practical resilience of BFT consensus protocols
Seascape genetic study on Laminaria digitata underscores the critical role of sampling schemes
No full textUnderstanding connectivity patterns exhibited by endangered species living in fragmented habitats is fundamental to improving management and conservation actions. Such improvements can be particularly pressing at the trailing edges of these habitats, where populations are facing the greatest challenges from climate change, and appear even more crucial if the species is commercially harvested. Seascape genetics have been increasingly used to meet these needs. In this study, we examined connectivity patterns among 32 populations of the oarweed kelp Laminaria digitata located at the species’ southern range limit. The distance (or sampling gap) between neighboring populations ranged from a few km to a few 100s of km. By genotyping 11 microsatellite markers, we aimed to (1) refine analyses of population structure; (2) test whether on-shelf islands are genetically more differentiated than mainland populations; (3) evaluate the relative importance of various abiotic conditions in shaping the genetic structure; and (4) evaluate if the relative importance of each environmental factor varied according to sampling schemes. Our analyses revealed a positive relationship between connectivity links and genetic diversity: populations with high levels of connectivity were genetically enriched while isolated populations showed signs of genetic erosion. The genetically impoverished populations corresponded to the southernmost populations as well as populations along the northern coast of Brittany (Locquirec, Saint-Malo Bay) and the northernmost population in Pas-de-Calais. By performing distance-based redundancy analysis on various sampling schemes, geographic distance appeared as the dominant factor influencing connectivity between populations separated by great distances, while hydrodynamic processes were the main factor when analyzing at a final spatial resolution
High-performance thermoelectric cooler fabricated vith extrusion-based 3D printing materials
No full textA thermoelectric cooler is a solid-state device that transfers heat from one side to another when an electrical current passes through it. This technology is appealing because it can provide precise and localized cooling and heating without using hazardous liquids or gases commonly found in traditional vapor compression refrigeration. These devices are compact, customizable in size, work in any orientation, operate noiselessly, and require minimal maintenance. Even though thermoelectric coolers could be transformative for many advanced thermal management applications, their widespread adoption is hindered by the low efficiency of the thermoelectric materials and costly manufacturing processes.
In this work, we use extrusion-based 3D printing techniques to fabricate high-performance thermoelectric materials using nanomaterial-based ink. The ink formulation is optimized to ensure structural integrity and particle interfacial bonding during annealing, providing p- and n-type materials with record-high zT values of 1.46 and 1.35 at room temperature, respectively. Moreover, we integrate the printed materials into a 32-pair device and achieve a significant cooling temperature gradient of 50 °C and a coefficient of performance of 3.8, comparable to best-performing thermoelectric coolers, avoiding material waste, and the energy-intense and inefficient steps, such as high-temperature synthesis, pressure-assisted sintering, and cutting and dicing ingots, commonly used in conventional manufacturing processes
Structure‐activity relationship study of neuroprotective complex I inhibitor CP2
No full textBackground: We identified small molecule tricyclic pyrone compound CP2 as a mild mitochondrial complex I (MCI) inhibitor that induces neuroprotection in multiple mouse models of AD. One of the major concerns while targeting mitochondria is the production of reactive oxygen species (ROS). CP2 consists of two diastereoisomers, D1 and D2, with distinct activity and toxicity profiles. This study was designed to understand how structure of D1 and D2 affects their binding to MCI and the consequential impact on ROS production.
Method: The X-ray crystallography and cryo-electron microscopy (cryo-EM) at global resolution of 3.25-3.27Å were employed to identify the molecular structure of D1 and D2 and the D1 binding to the isolated ovine MCI. The assessment of the MCI inhibition and the extent of ROS generation were done in isolated MCI and human neuroblastoma MC65 cells using flow cytometry, a Seahorse extracellular flux analyzer, and the kinetic studies.
Result: In the closed conformation of MCI, D1 selectively binds to the deep Quinone-site (Qd) but not to the shallow Q-site (Qs), sharing the same binding pocket as rotenone. In the open MCI state, D1 exclusively binds to the Qs in contrast to rotenone, which binds Qd and Qs in both closed and open states. At the same concentrations, D1 inhibits respiration to a greater extent compared to D2 (5:1 ratio) and produces higher level of ROS.
Conclusion:Cryo-EM unambiguously identified binding of D1 to both the Qd and Qs sites, contingent upon the conformational state of MCI. In contrast to rotenone, D1 binds Qd only in the closed conformation during catalytic cycle, leading to mild inhibition. Superimposing X-ray crystallography data of D1 and D2 onto cryo-EM data suggests that the orientation of the methyl group in D2 induces a flatter conformation, resulting in lower binding affinity to MCI, which correlates with lower inhibition and toxicity compared to D1. At physiologically relevant concentrations, CP2 (D1:D2 = 1:1) demonstrates low MCI inhibition yielding negligible ROS levels. This observation provides new insight into the absence of toxicity associated with CP2 treatment in vivo, further highlighting feasibility for the development of safe and efficacious MCI inhibitors
PMLR
No full textLeveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression before it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99% sparsity without accuracy loss, effectively removing the memory overhead of fullmatrix preconditioners such as GGT and M-FAC
Advances in Neural Information Processing Systems
No full textCausal representation learning aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect of new interventions or more robust classification. A plethora of methods have been developed, each tackling carefully crafted problem settings that lead to different types of identifiability. The folklore is that these different settings are important, as they are often linked to different rungs of Pearl's causal hierarchy, although not all neatly fit. Our main contribution is to show that many existing causal representation learning approaches methodologically align the representation to known data symmetries. Identification of the variables is guided by equivalence classes across different "data pockets" that are not necessarily causal. This result suggests important implications, allowing us to unify many existing approaches in a single method that can mix and match different assumptions, including non-causal ones, based on the invariances relevant to our application. It also significantly benefits applicability, which we demonstrate by improving treatment effect estimation on real-world high-dimensional ecological data. Overall, this paper clarifies the role of causality assumptions in the discovery of causal variables and shifts the focus to preserving data symmetries
Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method
No full textWe study the singularities of the moduli space of degree e maps from smooth genus g curves to an arbitrary smooth hypersurface of low degree. For e large compared to g, we show that these moduli spaces have at worst terminal singularities. Our main approach is to study the jet schemes of these moduli spaces by developing a suitable form of the circle method
The shape of word embeddings: Quantifying non-isometry with topological data analysis
No full textWord embeddings represent language vocabularies as clouds of d-dimensional points. We investigate how information is conveyed by the general shape of these clouds, instead of representing the semantic meaning of each token. Specifically, we use the notion of persistent homology from topological data analysis (TDA) to measure the distances between language pairs from the shape of their unlabeled embeddings. These distances quantify the degree of non-isometry of the embeddings. To distinguish whether these differences are random training errors or capture real information about the languages, we use the computed distance matrices to construct language phylogenetic trees over 81 Indo-European languages. Careful evaluation shows that our reconstructed trees exhibit strong and statistically-significant similarities to the reference
Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity
No full textProduction of hydrogen at large scale requires development of non-noble, inexpensive, and high-performing catalysts for constructing water-splitting devices. Herein, we report the synthesis of Zn-doped NiO heterostructure (ZnNiO) catalysts at room temperature via a coprecipitation method followed by drying (at 80 °C, 6 h) and calcination at an elevated temperature of 400 °C for 5 h under three distinct conditions, namely, air, N2, and vacuum. The vacuum-synthesized catalyst demonstrates a low overpotential of 88 mV at −10 mA cm–2 and a small Tafel slope of 73 mV dec–1 suggesting relatively higher charge transfer kinetics for hydrogen evolution reactions (HER) compared with the specimens synthesized under N2 or O2 atmosphere. It also demonstrates an oxygen evolution (OER) overpotential of 260 mV at 10 mA cm–2 with a low Tafel slope of 63 mV dec–1. In a full-cell water-splitting device, the vacuum-synthesized ZnNiO heterostructure demonstrates a cell voltage of 1.94 V at 50 mA cm–2 and shows remarkable stability over 24 h at a high current density of 100 mA cm–2. It is also demonstrated in this study that Zn-doping, surface, and interface engineering in transition-metal oxides play a crucial role in efficient electrocatalytic water splitting. Also, the results obtained from density functional theory (DFT + U = 0–8 eV), where U is the on-site Coulomb repulsion parameter also known as Hubbard U, based electronic structure calculations confirm that Zn doping constructively modifies the electronic structure, in both the valence band and the conduction band, and found to be suitable in tailoring the carrier’s effective masses of electrons and holes. The decrease in electron’s effective masses together with large differences between the effective masses of electrons and holes is noticed, which is found to be mainly responsible for achieving the best water-splitting performance from a 9% Zn-doped NiO sample prepared under vacuum