@article{18189, abstract = {Strongly interacting topological matter1 exhibits fundamentally new phenomena with potential applications in quantum information technology2,3. Emblematic instances are fractional quantum Hall (FQH) states4, in which the interplay of a magnetic field and strong interactions gives rise to fractionally charged quasi-particles, long-ranged entanglement and anyonic exchange statistics. Progress in engineering synthetic magnetic fields5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21 has raised the hope to create these exotic states in controlled quantum systems. However, except for a recent Laughlin state of light22, preparing FQH states in engineered systems remains elusive. Here we realize a FQH state with ultracold atoms in an optical lattice. The state is a lattice version of a bosonic ν = 1/2 Laughlin state4,23 with two particles on 16 sites. This minimal system already captures many hallmark features of Laughlin-type FQH states24,25,26,27,28: we observe a suppression of two-body interactions, we find a distinctive vortex structure in the density correlations and we measure a fractional Hall conductivity of σH/σ0 = 0.6(2) by means of the bulk response to a magnetic perturbation. Furthermore, by tuning the magnetic field, we map out the transition point between the normal and the FQH regime through a spectroscopic investigation of the many-body gap. Our work provides a starting point for exploring highly entangled topological matter with ultracold atoms29,30,31,32,33.}, author = {Leonard, Julian and Kim, Sooshin and Kwan, Joyce and Segura, Perrin and Grusdt, Fabian and Repellin, Cécile and Goldman, Nathan and Greiner, Markus}, issn = {1476-4687}, journal = {Nature}, number = {7970}, pages = {495--499}, publisher = {Springer Nature}, title = {{Realization of a fractional quantum Hall state with ultracold atoms}}, doi = {10.1038/s41586-023-06122-4}, volume = {619}, year = {2023}, } @article{18190, abstract = {Strongly correlated systems can exhibit unexpected phenomena when brought in a state far from equilibrium. An example is many-body localization, which prevents generic interacting systems from reaching thermal equilibrium even at long times1,2. The stability of the many-body localized phase has been predicted to be hindered by the presence of small thermal inclusions that act as a bath, leading to the delocalization of the entire system through an avalanche propagation mechanism3,4,5,6,7,8. Here we study the dynamics of a thermal inclusion of variable size when it is coupled to a many-body localized system. We find evidence for accelerated transport of thermal inclusion into the localized region. We monitor how the avalanche spreads through the localized system and thermalizes it site by site by measuring the site-resolved entropy over time. Furthermore, we isolate the strongly correlated bath-induced dynamics with multipoint correlations between the bath and the system. Our results have implications on the robustness of many-body localized systems and their critical behaviour.}, author = {Leonard, Julian and Kim, Sooshin and Rispoli, Matthew and Lukin, Alexander and Schittko, Robert and Kwan, Joyce and Demler, Eugene and Sels, Dries and Greiner, Markus}, issn = {1745-2481}, journal = {Nature Physics}, number = {4}, pages = {481--485}, publisher = {Springer Nature}, title = {{Probing the onset of quantum avalanches in a many-body localized system}}, doi = {10.1038/s41567-022-01887-3}, volume = {19}, year = {2023}, } @article{18207, abstract = {Comparison of myoglobin structures reveals that protein isolated from horse heart consistently adopts an alternate turn conformation in comparison to its homologues. Analysis of hundreds of high-resolution structures discounts crystallization conditions or the surrounding amino acid protein environment as explaining this difference, that is also not captured by the AlphaFold prediction. Rather, a water molecule is identified as stabilizing the conformation in the horse heart structure, which immediately reverts to the whale conformation in molecular dynamics simulations excluding that structural water.}, author = {Bronstein, Alexander and Marx, Ailie}, issn = {2045-2322}, journal = {Scientific Reports}, publisher = {Springer Nature}, title = {{Water stabilizes an alternate turn conformation in horse heart myoglobin}}, doi = {10.1038/s41598-023-32821-z}, volume = {13}, year = {2023}, } @article{18208, abstract = {The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model. We demonstrate GaUDI’s effectiveness in designing molecules for organic electronic applications by using single- and multiple-objective tasks applied to a generated dataset of 475,000 polycyclic aromatic systems. GaUDI shows improved conditional design, generating molecules with optimal properties and even going beyond the original distribution to suggest better molecules than those in the dataset. In addition to point-wise targets, GaUDI can also be guided toward open-ended targets (for example, a minimum or maximum) and in all cases achieves close to 100% validity of generated molecules.}, author = {Weiss, Tomer and Mayo Yanes, Eduardo and Chakraborty, Sabyasachi and Cosmo, Luca and Bronstein, Alexander and Gershoni-Poranne, Renana}, issn = {2662-8457}, journal = {Nature Computational Science}, number = {10}, pages = {873--882}, publisher = {Springer Nature}, title = {{Guided diffusion for inverse molecular design}}, doi = {10.1038/s43588-023-00532-0}, volume = {3}, year = {2023}, } @article{18209, abstract = {In this work, interpretable deep learning was used to identify structure–property relationships governing the HOMO–LUMO gap and the relative stability of polybenzenoid hydrocarbons (PBHs) using a ring-based graph representation. This representation was combined with a subunit-based perception of PBHs, allowing chemical insights to be presented in terms of intuitive and simple structural motifs. The resulting insights agree with conventional organic chemistry knowledge and electronic structure-based analyses and also reveal new behaviors and identify influential structural motifs. In particular, we evaluated and compared the effects of linear, angular, and branching motifs on these two molecular properties and explored the role of dispersion in mitigating the torsional strain inherent in nonplanar PBHs. Hence, the observed regularities and the proposed analysis contribute to a deeper understanding of the behavior of PBHs and form the foundation for design strategies for new functional PBHs.}, author = {Weiss, Tomer and Wahab, Alexandra and Bronstein, Alexander and Gershoni-Poranne, Renana}, issn = {1520-6904}, journal = {The Journal of Organic Chemistry}, number = {14}, pages = {9645--9656}, publisher = {American Chemical Society}, title = {{Interpretable deep-learning unveils structure–property relationships in polybenzenoid hydrocarbons}}, doi = {10.1021/acs.joc.2c02381}, volume = {88}, year = {2023}, } @inproceedings{18212, abstract = {The high memory bandwidth demand of sparse embedding layers continues to be a critical challenge in scaling the performance of recommendation models. While prior works have exploited heterogeneous memory system designs and partial embedding sum memoization techniques, they offer limited benefits. This is because prior designs either target a very small subset of embeddings to simplify their analysis or incur a high processing cost to account for all embeddings, which does not scale with the large sizes of modern embedding tables. This paper proposes GRACE-a lightweight and scalable graph-based algorithm-system co-design framework to significantly improve the embedding layer performance of recommendation models. GRACE proposes a novel Item Co-occurrence Graph (ICG) that scalably records item co-occurrences. GRACE then presents a new system-aware ICG clustering algorithm to find frequently accessed item combinations of arbitrary lengths to compute and memoize their partial sums. High-frequency partial sums are stored in a software-managed cache space to reduce memory traffic and improve the throughput of computing sparse features. We further present a cache data layout and low-cost address computation logic to efficiently lookup item embeddings and their partial sums. Our evaluation shows that GRACE significantly outperforms the state-of-the-art techniques SPACE and MERCI by 1.5x and 1.4x, respectively.}, author = {Ye, Haojie and Vedula, Sanketh and Chen, Yuhan and Yang, Yichen and Bronstein, Alexander and Dreslinski, Ronald and Mudge, Trevor and Talati, Nishil}, booktitle = {Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems}, isbn = {9781450399180}, number = {3}, pages = {282--301}, publisher = {Association for Computing Machinery}, title = {{GRACE: A scalable graph-based approach to accelerating recommendation model inference}}, doi = {10.1145/3582016.3582029}, volume = {11}, year = {2023}, } @article{18213, abstract = {What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches or node or edge attributes is available, or utilize arbitrary graph features. Such methods fare poorly in the pure form of the problem, in which only graph structures are given. Other proposals translate the problem to one of aligning node embeddings, yet, by doing so, provide only a single-scale view of the graph. In this article, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that first establishes a correspondence between functions derived from Laplacian matrix eigenvectors, which capture multiscale structural characteristics, and then exploits this correspondence to align nodes. We enhance the basic form of GRASP by altering two of its components, namely the embedding method and the assignment procedure it employs, leveraging its modular, hence adaptable design. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and graph types, and performs competitively with respect to the best non-scalable ones. We include in our study another modular graph alignment algorithm, CONE, which is also adaptable thanks to its modular nature, and show it can manage graphs with skewed power-law degree distributions.}, author = {Hermanns, Judith and Skitsas, Konstantinos and Tsitsulin, Anton and Munkhoeva, Marina and Kyster, Alexander and Nielsen, Simon and Bronstein, Alexander and Mottin, Davide and Karras, Panagiotis}, issn = {1556-472X}, journal = {ACM Transactions on Knowledge Discovery from Data}, number = {4}, publisher = {Association for Computing Machinery}, title = {{GRASP: Scalable graph alignment by spectral corresponding functions}}, doi = {10.1145/3561058}, volume = {17}, year = {2023}, } @article{18214, abstract = {Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is to maintain specific graph properties relevant to the downstream task while minimizing the graph's size. Graph algorithms often suffer from long execution time due to the irregularity and the large real-world graph size. Graph sparsification can be applied to greatly reduce the run time of graph algorithms by substituting the full graph with a much smaller sparsified graph, without significantly degrading the output quality. However, the interaction between numerous sparsifiers and graph properties is not widely explored, and the potential of graph sparsification is not fully understood. In this work, we cover 16 widely-used graph metrics, 12 representative graph sparsification algorithms, and 14 real-world input graphs spanning various categories, exhibiting diverse characteristics, sizes, and densities. We developed a framework to extensively assess the performance of these sparsification algorithms against graph metrics, and provide insights to the results. Our study shows that there is no one sparsifier that performs the best in preserving all graph properties, e.g. sparsifiers that preserve distance-related graph properties (eccentricity) struggle to perform well on Graph Neural Networks (GNN). This paper presents a comprehensive experimental study evaluating the performance of sparsification algorithms in preserving essential graph metrics. The insights inform future research in incorporating matching graph sparsification to graph algorithms to maximize benefits while minimizing quality degradation. Furthermore, we provide a framework to facilitate the future evaluation of evolving sparsification algorithms, graph metrics, and ever-growing graph data.}, author = {Chen, Yuhan and Ye, Haojie and Vedula, Sanketh and Bronstein, Alexander and Dreslinski, Ronald and Mudge, Trevor and Talati, Nishil}, issn = {2150-8097}, journal = {Proceedings of the VLDB Endowment}, number = {3}, pages = {427--440}, publisher = {Association for Computing Machinery}, title = {{Demystifying graph sparsification algorithms in graph properties preservation}}, doi = {10.14778/3632093.3632106}, volume = {17}, year = {2023}, } @inproceedings{18215, abstract = {We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time frame constraints of typical 5G mmWave networks. AARL can handle a variety of network topologies, network loads, and interference models, it can also adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. We show that for each topology, we compare the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual Profit Maximizer Algorithm). The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot. In addition, the quality of the scheduling decisions made by AARL outperforms those made by RPMA.}, author = {Gahtan, Barak and Cohen, Reuven and Bronstein, Alexander and Kedar, Gil}, booktitle = {14th International Conference on Network of the Future}, isbn = {9798350338089}, issn = {2833-0072}, location = {Izmir, Turkiye}, pages = {71--79}, publisher = {IEEE}, title = {{Using deep reinforcement learning for mmWave real-time scheduling}}, doi = {10.1109/nof58724.2023.10302794}, year = {2023}, } @article{18216, abstract = {Protein structure, both at the global and local level, dictates function. Proteins fold from chains of amino acids, forming secondary structures, α-helices and β-strands, that, at least for globular proteins, subsequently fold into a three-dimensional structure. Here, we show that a Ramachandran-type plot focusing on the two dihedral angles separated by the peptide bond, and entirely contained within an amino acid pair, defines a local structural unit. We further demonstrate the usefulness of this cross-peptide-bond Ramachandran plot by showing that it captures β-turn conformations in coil regions, that traditional Ramachandran plot outliers fall into occupied regions of our plot, and that thermophilic proteins prefer specific amino acid pair conformations. Further, we demonstrate experimentally that the effect of a point mutation on backbone conformation and protein stability depends on the amino acid pair context, i.e., the identity of the adjacent amino acid, in a manner predictable by our method.}, author = {Rosenberg, Aviv A. and Yehishalom, Nitsan and Marx, Ailie and Bronstein, Alexander}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {44}, publisher = {National Academy of Sciences}, title = {{An amino-domino model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural units}}, doi = {10.1073/pnas.2301064120}, volume = {120}, year = {2023}, } @inproceedings{18217, abstract = {A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks. Existing systems typically perform discrete gestures such as pointing or grasping, by employing electromyography (EMG) or ultrasound (US) technologies to analyze muscle states. While estimating finger gestures has been done in the past by detecting prominent gestures, we are interested in detection, or inference, done in the context of fine motions that evolve over time. Examples include motions occurring when performing fine and dexterous tasks such as keyboard typing or piano playing. We consider this task as an important step towards higher adoption rates of robotic prostheses among arm amputees, as it has the potential to dramatically increase functionality in performing daily tasks. To this end, we present an end-to-end robotic system, which can successfully infer fine finger motions. This is achieved by modeling the hand as a robotic manipulator and using it as an intermediate representation to encode muscles' dynamics from a sequence of US images. We evaluated our method by collecting data from a group of subjects and demonstrating how it can be used to replay music played or text typed. To the best of our knowledge, this is the first study demonstrating these downstream tasks within an end-to-end system.}, author = {Zadok, Dean and Salzman, Oren and Wolf, Alon and Bronstein, Alexander}, booktitle = {2023 IEEE International Conference on Robotics and Automation}, location = {London, United Kingdom}, publisher = {IEEE}, title = {{Towards predicting fine finger motions from ultrasound images via kinematic representation}}, doi = {10.1109/icra48891.2023.10160601}, volume = {27}, year = {2023}, } @inproceedings{18218, abstract = {Deep neural networks are known to be susceptible to adversarial perturbations – small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model’s output on a set of inputs. Universal perturbations present a more realistic case of adversarial attacks, as awareness of the model’s exact input is not required. In addition, the universal attack setting raises the subject of generalization to unseen data, where given a set of inputs, the universal perturbations aim to alter the model’s output on out-of-sample data. In this work, we study physical passive patch adversarial attacks on visual odometry-based autonomous navigation systems. A visual odometry system aims to infer the relative camera motion between two corresponding viewpoints, and is frequently used by vision-based autonomous navigation systems to estimate their state. For such navigation systems, a patch adversarial perturbation poses a severe security issue, as it can be used to mislead a system onto some collision course. To the best of our knowledge, we show for the first time that the error margin of a visual odometry model can be significantly increased by deploying patch adversarial attacks in the scene. We provide evaluation on synthetic closed-loop drone navigation data and demonstrate that a comparable vulnerability exists in real data. A reference implementation of the proposed method and the reported experiments is provided at https://github.com/patchadversarialattacks/patchadversarialattacks.}, author = {Nemcovsky, Yaniv and Jacoby, Matan and Bronstein, Alexander and Baskin, Chaim}, booktitle = {16th Asian Conference on Computer Vision}, isbn = {9783031262920}, issn = {1611-3349}, location = {Macao, China}, pages = {518--534}, publisher = {Springer Nature}, title = {{Physical passive patch adversarial attacks on visual odometry systems}}, doi = {10.1007/978-3-031-26293-7_31}, volume = {13847}, year = {2023}, } @article{18219, abstract = {Nowadays, many of the images captured are ‘observed’ by machines only and not by humans, e.g., in autonomous systems. High-level machine vision models, such as object recognition or semantic segmentation, assume images are transformed into some canonical image space by the camera Image Signal Processor (ISP). However, the camera ISP is optimized for producing visually pleasing images for human observers and not for machines. Therefore, one may spare the ISP compute time and apply vision models directly to RAW images. Yet, it has been shown that training such models directly on RAW images results in a performance drop. To mitigate this drop, we use a RAW and RGB image pairs dataset, which can be easily acquired with no human labeling. We then train a model that is applied directly to the RAW data by using knowledge distillation such that the model predictions for RAW images will be aligned with the predictions of an off-the-shelf pre-trained model for processed RGB images. Our experiments show that our performance on RAW images for object classification and semantic segmentation is significantly better than models trained on labeled RAW images. It also reasonably matches the predictions of a pre-trained model on processed RGB images, while saving the ISP compute overhead.}, author = {Schwartz, Eli and Bronstein, Alexander and Giryes, Raja}, issn = {2644-1322}, journal = {IEEE Open Journal of Signal Processing}, pages = {12--20}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{ISP Distillation}}, doi = {10.1109/ojsp.2023.3239819}, volume = {4}, year = {2023}, } @article{18621, abstract = {During neural development, cellular adhesion is crucial for interactions among and between neurons and surrounding tissues. This function is mediated by conserved cell adhesion molecules, which are tightly regulated to allow for coordinated neuronal outgrowth. Here, we show that the proprotein convertase KPC-1 (homolog of mammalian furin) regulates the Menorin adhesion complex during development of PVD dendritic arbors in Caenorhabditis elegans. We found a finely regulated antagonistic balance between PVD-expressed KPC-1 and the epidermally expressed putative cell adhesion molecule MNR-1 (Menorin). Genetically, partial loss of mnr-1 suppressed partial loss of kpc-1, and both loss of kpc-1 and transgenic overexpression of mnr-1 resulted in indistinguishable phenotypes in PVD dendrites. This balance regulated cell-surface localization of the DMA-1 leucine-rich transmembrane receptor in PVD neurons. Lastly, kpc-1 mutants showed increased amounts of MNR-1 and decreased amounts of muscle-derived LECT-2 (Chondromodulin II), which is also part of the Menorin adhesion complex. These observations suggest that KPC-1 in PVD neurons directly or indirectly controls the abundance of proteins of the Menorin adhesion complex from adjacent tissues, thereby providing negative feedback from the dendrite to the instructive cues of surrounding tissues.}, author = {Ramirez, Nelson and Belalcazar, Helen M. and Rahman, Maisha and Trivedi, Meera and Tang, Leo T. H. and Bülow, Hannes E.}, issn = {1477-9129}, journal = {Development}, number = {18}, publisher = {The Company of Biologists}, title = {{Convertase-dependent regulation of membrane-tethered and secreted ligands tunes dendrite adhesion}}, doi = {10.1242/dev.201208}, volume = {150}, year = {2023}, } @misc{18634, abstract = {There are 4 tar.xz files with the result of the model for the paper: A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard. }, author = {Muñoz Hermosilla, José M}, publisher = {Zenodo}, title = {{A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen}}, doi = {10.5281/ZENODO.8005257}, year = {2023}, } @article{12287, abstract = {We present criteria for establishing a triangulation of a manifold. Given a manifold M, a simplicial complex A, and a map H from the underlying space of A to M, our criteria are presented in local coordinate charts for M, and ensure that H is a homeomorphism. These criteria do not require a differentiable structure, or even an explicit metric on M. No Delaunay property of A is assumed. The result provides a triangulation guarantee for algorithms that construct a simplicial complex by working in local coordinate patches. Because the criteria are easily verified in such a setting, they are expected to be of general use.}, author = {Boissonnat, Jean-Daniel and Dyer, Ramsay and Ghosh, Arijit and Wintraecken, Mathijs}, issn = {1432-0444}, journal = {Discrete & Computational Geometry}, keywords = {Computational Theory and Mathematics, Discrete Mathematics and Combinatorics, Geometry and Topology, Theoretical Computer Science}, pages = {156--191}, publisher = {Springer Nature}, title = {{Local criteria for triangulating general manifolds}}, doi = {10.1007/s00454-022-00431-7}, volume = {69}, year = {2023}, } @article{12313, abstract = {Let P be a nontorsion point on an elliptic curve defined over a number field K and consider the sequence {Bn}n∈N of the denominators of x(nP). We prove that every term of the sequence of the Bn has a primitive divisor for n greater than an effectively computable constant that we will explicitly compute. This constant will depend only on the model defining the curve.}, author = {Verzobio, Matteo}, issn = {0030-8730}, journal = {Pacific Journal of Mathematics}, number = {2}, pages = {331--351}, publisher = {Mathematical Sciences Publishers}, title = {{Some effectivity results for primitive divisors of elliptic divisibility sequences}}, doi = {10.2140/pjm.2023.325.331}, volume = {325}, year = {2023}, } @article{12329, abstract = {In this article, we develop two independent and new approaches to model epidemic spread in a network. Contrary to the most studied models, those developed here allow for contacts with different probabilities of transmitting the disease (transmissibilities). We then examine each of these models using some mean field type approximations. The first model looks at the late-stage effects of an epidemic outbreak and allows for the computation of the probability that a given vertex was infected. This computation is based on a mean field approximation and only depends on the number of contacts and their transmissibilities. This approach shares many similarities with percolation models in networks. The second model we develop is a dynamic model which we analyze using a mean field approximation which highly reduces the dimensionality of the system. In particular, the original system which individually analyses each vertex of the network is reduced to one with as many equations as different transmissibilities. Perhaps the greatest contribution of this article is the observation that, in both these models, the existence and size of an epidemic outbreak are linked to the properties of a matrix which we call the R-matrix. This is a generalization of the basic reproduction number which more precisely characterizes the main routes of infection.}, author = {Gómez, Arturo and Oliveira, Goncalo}, issn = {2045-2322}, journal = {Scientific Reports}, publisher = {Springer Nature}, title = {{New approaches to epidemic modeling on networks}}, doi = {10.1038/s41598-022-19827-9}, volume = {13}, year = {2023}, } @article{12330, abstract = {The design and implementation of efficient concurrent data structures has seen significant attention. However, most of this work has focused on concurrent data structures providing good worst-case guarantees, although, in real workloads, objects are often accessed at different rates. Efficient distribution-adaptive data structures, such as splay-trees, are known in the sequential case; however, they often are hard to translate efficiently to the concurrent case. We investigate distribution-adaptive concurrent data structures, and propose a new design called the splay-list. At a high level, the splay-list is similar to a standard skip-list, with the key distinction that the height of each element adapts dynamically to its access rate: popular elements “move up,” whereas rarely-accessed elements decrease in height. We show that the splay-list provides order-optimal amortized complexity bounds for a subset of operations, while being amenable to efficient concurrent implementation. Experiments show that the splay-list can leverage distribution-adaptivity for performance, and can outperform the only previously-known distribution-adaptive concurrent design in certain workloads.}, author = {Aksenov, Vitalii and Alistarh, Dan-Adrian and Drozdova, Alexandra and Mohtashami, Amirkeivan}, issn = {1432-0452}, journal = {Distributed Computing}, pages = {395--418}, publisher = {Springer Nature}, title = {{The splay-list: A distribution-adaptive concurrent skip-list}}, doi = {10.1007/s00446-022-00441-x}, volume = {36}, year = {2023}, } @article{12331, abstract = {High carrier mobility is critical to improving thermoelectric performance over a broad temperature range. However, traditional doping inevitably deteriorates carrier mobility. Herein, we develop a strategy for fine tuning of defects to improve carrier mobility. To begin, n-type PbTe is created by compensating for the intrinsic Pb vacancy in bare PbTe. Excess Pb2+ reduces vacancy scattering, resulting in a high carrier mobility of ∼3400 cm2 V–1 s–1. Then, excess Ag is introduced to compensate for the remaining intrinsic Pb vacancies. We find that excess Ag exhibits a dynamic doping process with increasing temperatures, increasing both the carrier concentration and carrier mobility throughout a wide temperature range; specifically, an ultrahigh carrier mobility ∼7300 cm2 V–1 s–1 is obtained for Pb1.01Te + 0.002Ag at 300 K. Moreover, the dynamic doping-induced high carrier concentration suppresses the bipolar thermal conductivity at high temperatures. The final step is using iodine to optimize the carrier concentration to ∼1019 cm–3. Ultimately, a maximum ZT value of ∼1.5 and a large average ZTave value of ∼1.0 at 300–773 K are obtained for Pb1.01Te0.998I0.002 + 0.002Ag. These findings demonstrate that fine tuning of defects with <0.5% impurities can remarkably enhance carrier mobility and improve thermoelectric performance.}, author = {Wang, Siqi and Chang, Cheng and Bai, Shulin and Qin, Bingchao and Zhu, Yingcai and Zhan, Shaoping and Zheng, Junqing and Tang, Shuwei and Zhao, Li Dong}, issn = {1520-5002}, journal = {Chemistry of Materials}, number = {2}, pages = {755--763}, publisher = {American Chemical Society}, title = {{Fine tuning of defects enables high carrier mobility and enhanced thermoelectric performance of n-type PbTe}}, doi = {10.1021/acs.chemmater.2c03542}, volume = {35}, year = {2023}, }