@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{22046, abstract = {We show that solutions to the Ablowitz–Ladik system converge to solutions of the cubic nonlinear Schrödinger equation for merely L2 initial data. Furthermore, we consider initial data for this lattice model that excites Fourier modes near both critical points of the discrete dispersion relation and demonstrate convergence to a decoupled system of nonlinear Schrödinger equations.}, author = {Killip, Rowan and Ouyang, Zhimeng and Visan, Monica and Wu, Lei}, issn = {1361-6544}, journal = {Nonlinearity}, keywords = {Ablowitz–Ladik, continuum limit, cubic NLS}, number = {7}, pages = {3751--3775}, publisher = {IOP Publishing}, title = {{Continuum limit for the Ablowitz–Ladik system}}, doi = {10.1088/1361-6544/acd978}, volume = {36}, year = {2023}, } @article{22067, abstract = {We consider the derivative nonlinear Schrödinger equation in one space dimension, posed both on the line and on the circle. This model is known to be completely integrable and L^2-critical with respect to scaling. We first discuss whether ensembles of orbits with L^2-equicontinuous initial data remain equicontinuous under evolution. We prove that this is true under the restriction M(q)=∫∣∣q∣∣2<4π. We conjecture that this restriction is unnecessary. Further, we prove that the problem is globally well posed for initial data in H1∕6 under the same restriction on M. Moreover, we show that this restriction would be removed by a successful resolution of our equicontinuity conjecture.}, author = {Killip, Rowan and Ntekoume, Maria and Visan, Monica}, issn = {1948-206X}, journal = {Analysis & PDE}, number = {5}, pages = {1245--1270}, publisher = {Mathematical Sciences Publishers}, title = {{On the well-posedness problem for the derivativenonlinear Schrödinger equation}}, doi = {10.2140/apde.2023.16.1245}, volume = {16}, year = {2023}, } @article{22064, abstract = {Using the two-dimensional nonlinear Schrödinger equation as a model example, we present a general method for recovering the nonlinearity of a nonlinear dispersive equation from its small-data scattering behavior. We prove that under very mild assumptions on the nonlinearity, the wave operator uniquely determines the nonlinearity, as does the scattering map. Evaluating the scattering map on well-chosen initial data, we reduce the problem to an inverse convolution problem, which we solve by means of an application of the Beurling–Lax Theorem.}, author = {Killip, Rowan and Murphy, Jason and Visan, Monica}, issn = {1088-6826}, journal = {Proceedings of the American Mathematical Society}, number = {6}, pages = {2543--2557}, publisher = {American Mathematical Society}, title = {{The scattering map determines the nonlinearity}}, doi = {10.1090/proc/16297}, volume = {151}, year = {2023}, } @article{22072, abstract = {We consider the derivative nonlinear Schrödinger equation in one spatial dimension, which is known to be completely integrable. We prove that the orbits of L^2 bounded and equicontinuous sets of initial data remain bounded and equicontinuous, not only under this flow, but also under the entire hierarchy. This allows us to remove the small-data restriction from prior conservation laws and global well-posedness results.}, author = {Harrop-Griffiths, Benjamin and Killip, Rowan and Visan, Monica}, issn = {1687-0247}, journal = {International Mathematics Research Notices}, number = {6}, pages = {4601--4642}, publisher = {Oxford University Press}, title = {{Large-data equicontinuity for the derivative NLS}}, doi = {10.1093/imrn/rnab374}, volume = {2023}, year = {2023}, } @inproceedings{14863, author = {Polesello, Andrea and Muller, Caroline J and Pasquero, Claudia and Meroni, Agostino N.}, booktitle = {EGU General Assembly 2023}, location = {Vienna, Austria & Virtual}, publisher = {European Geosciences Union}, title = {{Intensification mechanisms of tropical cyclones}}, doi = {10.5194/egusphere-egu23-6157}, year = {2023}, } @inproceedings{22125, author = {Muñoz Hermosilla, José M and De Andrés, Eva and Shahateet, Kaian and Otero, Jaime and Navarro, Francisco J.}, booktitle = {EGU General Assembly 2023}, location = {Vienna, Austria & Virtual}, publisher = {European Geosciences Union}, title = {{A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard}}, doi = {10.5194/egusphere-egu23-5510}, year = {2023}, } @article{14756, abstract = {We prove the r-spin cobordism hypothesis in the setting of (weak) 2-categories for every positive integer r: the 2-groupoid of 2-dimensional fully extended r-spin TQFTs with given target is equivalent to the homotopy fixed points of an induced Spin 2r -action. In particular, such TQFTs are classified by fully dualisable objects together with a trivialisation of the rth power of their Serre automorphisms. For r=1, we recover the oriented case (on which our proof builds), while ordinary spin structures correspond to r=2. To construct examples, we explicitly describe Spin 2r​-homotopy fixed points in the equivariant completion of any symmetric monoidal 2-category. We also show that every object in a 2-category of Landau–Ginzburg models gives rise to fully extended spin TQFTs and that half of these do not factor through the oriented bordism 2-category.}, author = {Carqueville, Nils and Szegedy, Lorant}, issn = {1663-487X}, journal = {Quantum Topology}, keywords = {Geometry and Topology, Mathematical Physics}, number = {3}, pages = {467--532}, publisher = {EMS Press}, title = {{Fully extended r-spin TQFTs}}, doi = {10.4171/qt/193}, volume = {14}, year = {2023}, } @article{13179, abstract = {Writing concurrent code that is both correct and efficient is notoriously difficult. Thus, programmers often prefer to use synchronization abstractions, which render code simpler and easier to reason about. Despite a wealth of work on this topic, there is still a gap between the rich semantics provided by synchronization abstractions in modern programming languages—specifically, fair FIFO ordering of synchronization requests and support for abortable operations—and frameworks for implementing it correctly and efficiently. Supporting such semantics is critical given the rising popularity of constructs for asynchronous programming, such as coroutines, which abort frequently and are cheaper to suspend and resume compared to native threads. This paper introduces a new framework called CancellableQueueSynchronizer (CQS), which enables simple yet efficient implementations of a wide range of fair and abortable synchronization primitives: mutexes, semaphores, barriers, count-down latches, and blocking pools. Our main contribution is algorithmic, as implementing both fairness and abortability efficiently at this level of generality is non-trivial. Importantly, all our algorithms, including the CQS framework and the primitives built on top of it, come with formal proofs in the Iris framework for Coq for many of their properties. These proofs are modular, so it is easy to show correctness for new primitives implemented on top of CQS. From a practical perspective, implementation of CQS for native threads on the JVM improves throughput by up to two orders of magnitude over Java’s AbstractQueuedSynchronizer, the only practical abstraction offering similar semantics. Further, we successfully integrated CQS as a core component of the popular Kotlin Coroutines library, validating the framework’s practical impact and expressiveness in a real-world environment. In sum, CancellableQueueSynchronizer is the first framework to combine expressiveness with formal guarantees and solid practical performance. Our approach should be extensible to other languages and families of synchronization primitives.}, author = {Koval, Nikita and Khalanskiy, Dmitry and Alistarh, Dan-Adrian}, issn = {2475-1421}, journal = {Proceedings of the ACM on Programming Languages}, publisher = {Association for Computing Machinery}, title = {{CQS: A formally-verified framework for fair and abortable synchronization}}, doi = {10.1145/3591230}, volume = {7}, year = {2023}, } @inbook{19983, abstract = {The sinkless orientation problem plays a key role in understanding the foundations of distributed computing. The problem can be used to separate two fundamental models of distributed graph algorithms, LOCAL and SLOCAL: the locality of sinkless orientation is Ω(log n) in the deterministic LOCAL model and O(log log n) in the deterministic SLOCAL model. Both of these results are known by prior work, but here we give new simple, self-contained proofs for them.}, author = {Balliu, Alkida and Korhonen, Janne and Kuhn, Fabian and Lievonen, Henrik and Olivetti, Dennis and Pai, Shreyas and Paz, Ami and Rybicki, Joel and Schmid, Stefan and Studený, Jan and Suomela, Jukka and Uitto, Jara}, booktitle = {Symposium on Simplicity in Algorithms}, location = {Florence, Italy}, pages = {175--191}, publisher = {Society for Industrial and Applied Mathematics}, title = {{Sinkless Orientation Made Simple}}, doi = {10.1137/1.9781611977585.ch17}, year = {2023}, } @article{13443, abstract = {The ages of solar-like stars have been at the center of many studies such as exoplanet characterization or Galactic-archeology. While ages are usually computed from stellar evolution models, relations linking ages to other stellar properties, such as rotation and magnetic activity, have been investigated. With the large catalog of 55,232 rotation periods, Prot, and photometric magnetic activity index, Sph from Kepler data, we have the opportunity to look for such magneto-gyro-chronology relations. Stellar ages are obtained with two stellar evolution codes that include treatment of angular momentum evolution, hence using Prot as input in addition to classical atmospheric parameters. We explore two different ways of predicting stellar ages on three subsamples with spectroscopic observations: solar analogs, late-F and G dwarfs, and K dwarfs. We first perform a Bayesian analysis to derive relations between Sph and ages between 1 and 5 Gyr, and other stellar properties. For late-F and G dwarfs, and K dwarfs, the multivariate regression favors the model with Prot and Sph with median differences of 0.1% and 0.2%, respectively. We also apply Machine Learning techniques with a Random Forest algorithm to predict ages up to 14 Gyr with the same set of input parameters. For late-F, G and K dwarfs together, predicted ages are on average within 5.3% of the model ages and improve to 3.1% when including Prot. These are very promising results for a quick age estimation for solar-like stars with photometric observations, especially with current and future space missions.}, author = {Mathur, Savita and Claytor, Zachary R. and Santos, Ângela R. G. and García, Rafael A. and Amard, Louis and Bugnet, Lisa Annabelle and Corsaro, Enrico and Bonanno, Alfio and Breton, Sylvain N. and Godoy-Rivera, Diego and Pinsonneault, Marc H. and van Saders, Jennifer}, issn = {1538-4357}, journal = {The Astrophysical Journal}, keywords = {Space and Planetary Science, Astronomy and Astrophysics}, number = {2}, publisher = {IOP Publishing}, title = {{Magnetic activity evolution of solar-like stars. I. Sph–age relation derived from Kepler observations}}, doi = {10.3847/1538-4357/acd118}, volume = {952}, year = {2023}, } @phdthesis{12885, abstract = {High-performance semiconductors rely upon precise control of heat and charge transport. This can be achieved by precisely engineering defects in polycrystalline solids. There are multiple approaches to preparing such polycrystalline semiconductors, and the transformation of solution-processed colloidal nanoparticles is appealing because colloidal nanoparticles combine low cost with structural and compositional tunability along with rich surface chemistry. However, the multiple processes from nanoparticle synthesis to the final bulk nanocomposites are very complex. They involve nanoparticle purification, post-synthetic modifications, and finally consolidation (thermal treatments and densification). All these properties dictate the final material’s composition and microstructure, ultimately affecting its functional properties. This thesis explores the synthesis, surface chemistry and consolidation of colloidal semiconductor nanoparticles into dense solids. In particular, the transformations that take place during these processes, and their effect on the material’s transport properties are evaluated. }, author = {Calcabrini, Mariano}, isbn = {978-3-99078-028-2}, issn = {2663-337X}, pages = {82}, publisher = {Institute of Science and Technology Austria}, title = {{Nanoparticle-based semiconductor solids: From synthesis to consolidation}}, doi = {10.15479/at:ista:12885}, year = {2023}, }