@inproceedings{10670, abstract = {Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-time neural models and face difficulties in generalizing to domain shifts by failing to account for the causal relationships between the agent and the environment. In this paper, we propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks, specifically over their discrete-time counterparts. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments. Our results demonstrate that causal continuous-time deep models can perform robust navigation tasks, where advanced recurrent models fail. These models learn complex causal control representations directly from raw visual inputs and scale to solve a variety of tasks using imitation learning.}, author = {Vorbach, Charles J and Hasani, Ramin and Amini, Alexander and Lechner, Mathias and Rus, Daniela}, booktitle = {35th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {Virtual}, publisher = {Neural Information Processing Systems Foundation}, title = {{Causal navigation by continuous-time neural networks}}, year = {2021}, } @article{9293, abstract = {We consider planning problems for graphs, Markov Decision Processes (MDPs), and games on graphs in an explicit state space. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems with k different target sets: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in a given sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds, based on the boolean matrix multiplication (BMM) conjecture and strong exponential time hypothesis (SETH), establish (i) model-separation results showing that for the coverage problem MDPs and games on graphs are harder than graphs, and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) problem-separation results showing that for MDPs the coverage problem is harder than the sequential target problem.}, author = {Chatterjee, Krishnendu and Dvořák, Wolfgang and Henzinger, Monika H and Svozil, Alexander}, issn = {0004-3702}, journal = {Artificial Intelligence}, number = {8}, publisher = {Elsevier}, title = {{Algorithms and conditional lower bounds for planning problems}}, doi = {10.1016/j.artint.2021.103499}, volume = {297}, year = {2021}, } @inproceedings{9441, abstract = {Isomanifolds are the generalization of isosurfaces to arbitrary dimension and codimension, i.e. submanifolds of ℝ^d defined as the zero set of some multivariate multivalued smooth function f: ℝ^d → ℝ^{d-n}, where n is the intrinsic dimension of the manifold. A natural way to approximate a smooth isomanifold M is to consider its Piecewise-Linear (PL) approximation M̂ based on a triangulation 𝒯 of the ambient space ℝ^d. In this paper, we describe a simple algorithm to trace isomanifolds from a given starting point. The algorithm works for arbitrary dimensions n and d, and any precision D. Our main result is that, when f (or M) has bounded complexity, the complexity of the algorithm is polynomial in d and δ = 1/D (and unavoidably exponential in n). Since it is known that for δ = Ω (d^{2.5}), M̂ is O(D²)-close and isotopic to M, our algorithm produces a faithful PL-approximation of isomanifolds of bounded complexity in time polynomial in d. Combining this algorithm with dimensionality reduction techniques, the dependency on d in the size of M̂ can be completely removed with high probability. We also show that the algorithm can handle isomanifolds with boundary and, more generally, isostratifolds. The algorithm for isomanifolds with boundary has been implemented and experimental results are reported, showing that it is practical and can handle cases that are far ahead of the state-of-the-art. }, author = {Boissonnat, Jean-Daniel and Kachanovich, Siargey and Wintraecken, Mathijs}, booktitle = {37th International Symposium on Computational Geometry}, isbn = {978-3-95977-184-9}, issn = {1868-8969}, location = {Virtual}, pages = {17:1--17:16}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Tracing isomanifolds in Rd in time polynomial in d using Coxeter-Freudenthal-Kuhn triangulations}}, doi = {10.4230/LIPIcs.SoCG.2021.17}, volume = {189}, year = {2021}, } @inproceedings{10041, abstract = {Yao’s garbling scheme is one of the most fundamental cryptographic constructions. Lindell and Pinkas (Journal of Cryptograhy 2009) gave a formal proof of security in the selective setting where the adversary chooses the challenge inputs before seeing the garbled circuit assuming secure symmetric-key encryption (and hence one-way functions). This was followed by results, both positive and negative, concerning its security in the, stronger, adaptive setting. Applebaum et al. (Crypto 2013) showed that it cannot satisfy adaptive security as is, due to a simple incompressibility argument. Jafargholi and Wichs (TCC 2017) considered a natural adaptation of Yao’s scheme (where the output mapping is sent in the online phase, together with the garbled input) that circumvents this negative result, and proved that it is adaptively secure, at least for shallow circuits. In particular, they showed that for the class of circuits of depth δ , the loss in security is at most exponential in δ . The above results all concern the simulation-based notion of security. In this work, we show that the upper bound of Jafargholi and Wichs is basically optimal in a strong sense. As our main result, we show that there exists a family of Boolean circuits, one for each depth δ∈N , such that any black-box reduction proving the adaptive indistinguishability of the natural adaptation of Yao’s scheme from any symmetric-key encryption has to lose a factor that is exponential in δ√ . Since indistinguishability is a weaker notion than simulation, our bound also applies to adaptive simulation. To establish our results, we build on the recent approach of Kamath et al. (Eprint 2021), which uses pebbling lower bounds in conjunction with oracle separations to prove fine-grained lower bounds on loss in cryptographic security.}, author = {Kamath Hosdurg, Chethan and Klein, Karen and Pietrzak, Krzysztof Z and Wichs, Daniel}, booktitle = {41st Annual International Cryptology Conference}, isbn = {978-3-030-84244-4}, issn = {1611-3349}, location = {Virtual}, pages = {486--515}, publisher = {Springer Nature}, title = {{Limits on the Adaptive Security of Yao’s Garbling}}, doi = {10.1007/978-3-030-84245-1_17}, volume = {12826}, year = {2021}, } @inproceedings{11453, abstract = {Neuronal computations depend on synaptic connectivity and intrinsic electrophysiological properties. Synaptic connectivity determines which inputs from presynaptic neurons are integrated, while cellular properties determine how inputs are filtered over time. Unlike their biological counterparts, most computational approaches to learning in simulated neural networks are limited to changes in synaptic connectivity. However, if intrinsic parameters change, neural computations are altered drastically. Here, we include the parameters that determine the intrinsic properties, e.g., time constants and reset potential, into the learning paradigm. Using sparse feedback signals that indicate target spike times, and gradient-based parameter updates, we show that the intrinsic parameters can be learned along with the synaptic weights to produce specific input-output functions. Specifically, we use a teacher-student paradigm in which a randomly initialised leaky integrate-and-fire or resonate-and-fire neuron must recover the parameters of a teacher neuron. We show that complex temporal functions can be learned online and without backpropagation through time, relying on event-based updates only. Our results are a step towards online learning of neural computations from ungraded and unsigned sparse feedback signals with a biologically inspired learning mechanism.}, author = {Braun, Lukas and Vogels, Tim P}, booktitle = {35th Conference on Neural Information Processing Systems}, isbn = {9781713845393}, issn = {1049-5258}, location = {Virtual, Online}, pages = {16437--16450}, publisher = {Neural Information Processing Systems Foundation}, title = {{Online learning of neural computations from sparse temporal feedback}}, volume = {20}, year = {2021}, } @inproceedings{11452, abstract = {We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.}, author = {Alimisis, Foivos and Davies, Peter and Vandereycken, Bart and Alistarh, Dan-Adrian}, booktitle = {35th Conference on Neural Information Processing Systems}, isbn = {9781713845393}, issn = {1049-5258}, location = {Virtual, Online}, pages = {2823--2834}, publisher = {Neural Information Processing Systems Foundation}, title = {{Distributed principal component analysis with limited communication}}, volume = {4}, year = {2021}, } @article{9331, abstract = {Quantum entanglement has been generated and verified in cold-atom experiments and used to make atom-interferometric measurements below the shot-noise limit. However, current state-of-the-art cold-atom devices exploit separable (i.e., unentangled) atomic states. This perspective piece asks the question: can entanglement usefully improve cold-atom sensors, in the sense that it gives new sensing capabilities unachievable with current state-of-the-art devices? We briefly review the state-of-the-art in precision cold-atom sensing, focusing on clocks and inertial sensors, identifying the potential benefits entanglement could bring to these devices, and the challenges that need to be overcome to realize these benefits. We survey demonstrated methods of generating metrologically useful entanglement in cold-atom systems, note their relative strengths and weaknesses, and assess their prospects for near-to-medium term quantum-enhanced cold-atom sensing.}, author = {Szigeti, Stuart S. and Hosten, Onur and Haine, Simon A.}, issn = {0003-6951}, journal = {Applied Physics Letters}, number = {14}, publisher = {AIP Publishing}, title = {{Improving cold-atom sensors with quantum entanglement: Prospects and challenges}}, doi = {10.1063/5.0050235}, volume = {118}, year = {2021}, } @article{9168, abstract = {Interspecific crossing experiments have shown that sex chromosomes play a major role in reproductive isolation between many pairs of species. However, their ability to act as reproductive barriers, which hamper interspecific genetic exchange, has rarely been evaluated quantitatively compared to Autosomes. This genome-wide limitation of gene flow is essential for understanding the complete separation of species, and thus speciation. Here, we develop a mainland-island model of secondary contact between hybridizing species of an XY (or ZW) sexual system. We obtain theoretical predictions for the frequency of introgressed alleles, and the strength of the barrier to neutral gene flow for the two types of chromosomes carrying multiple interspecific barrier loci. Theoretical predictions are obtained for scenarios where introgressed alleles are rare. We show that the same analytical expressions apply for sex chromosomes and autosomes, but with different sex-averaged effective parameters. The specific features of sex chromosomes (hemizygosity and absence of recombination in the heterogametic sex) lead to reduced levels of introgression on the X (or Z) compared to autosomes. This effect can be enhanced by certain types of sex-biased forces, but it remains overall small (except when alleles causing incompatibilities are recessive). We discuss these predictions in the light of empirical data comprising model-based tests of introgression and cline surveys in various biological systems.}, author = {Fraisse, Christelle and Sachdeva, Himani}, issn = {1943-2631}, journal = {Genetics}, number = {2}, publisher = {Genetics Society of America}, title = {{The rates of introgression and barriers to genetic exchange between hybridizing species: Sex chromosomes vs autosomes}}, doi = {10.1093/genetics/iyaa025}, volume = {217}, year = {2021}, } @article{9793, abstract = {Astrocytes extensively infiltrate the neuropil to regulate critical aspects of synaptic development and function. This process is regulated by transcellular interactions between astrocytes and neurons via cell adhesion molecules. How astrocytes coordinate developmental processes among one another to parse out the synaptic neuropil and form non-overlapping territories is unknown. Here we identify a molecular mechanism regulating astrocyte-astrocyte interactions during development to coordinate astrocyte morphogenesis and gap junction coupling. We show that hepaCAM, a disease-linked, astrocyte-enriched cell adhesion molecule, regulates astrocyte competition for territory and morphological complexity in the developing mouse cortex. Furthermore, conditional deletion of Hepacam from developing astrocytes significantly impairs gap junction coupling between astrocytes and disrupts the balance between synaptic excitation and inhibition. Mutations in HEPACAM cause megalencephalic leukoencephalopathy with subcortical cysts in humans. Therefore, our findings suggest that disruption of astrocyte self-organization mechanisms could be an underlying cause of neural pathology.}, author = {Baldwin, Katherine T. and Tan, Christabel X. and Strader, Samuel T. and Jiang, Changyu and Savage, Justin T. and Elorza-Vidal, Xabier and Contreras, Ximena and Rülicke, Thomas and Hippenmeyer, Simon and Estévez, Raúl and Ji, Ru-Rong and Eroglu, Cagla}, issn = {1097-4199}, journal = {Neuron}, number = {15}, pages = {2427--2442.e10}, publisher = {Elsevier}, title = {{HepaCAM controls astrocyte self-organization and coupling}}, doi = {10.1016/j.neuron.2021.05.025}, volume = {109}, year = {2021}, } @article{10363, abstract = {Erythropoietin enhances oxygen delivery and reduces hypoxia-induced cell death, but its pro-thrombotic activity is problematic for use of erythropoietin in treating hypoxia. We constructed a fusion protein that stimulates red blood cell production and neuroprotection without triggering platelet production, a marker for thrombosis. The protein consists of an anti-glycophorin A nanobody and an erythropoietin mutant (L108A). The mutation reduces activation of erythropoietin receptor homodimers that induce erythropoiesis and thrombosis, but maintains the tissue-protective signaling. The binding of the nanobody element to glycophorin A rescues homodimeric erythropoietin receptor activation on red blood cell precursors. In a cell proliferation assay, the fusion protein is active at 10−14 M, allowing an estimate of the number of receptor–ligand complexes needed for signaling. This fusion protein stimulates erythroid cell proliferation in vitro and in mice, and shows neuroprotective activity in vitro. Our erythropoietin fusion protein presents a novel molecule for treating hypoxia.}, author = {Lee, Jungmin and Vernet, Andyna and Gruber, Nathalie and Kready, Kasia M. and Burrill, Devin R. and Way, Jeffrey C. and Silver, Pamela A.}, issn = {1741-0134}, journal = {Protein Engineering, Design and Selection}, publisher = {Oxford University Press}, title = {{Rational engineering of an erythropoietin fusion protein to treat hypoxia}}, doi = {10.1093/protein/gzab025}, volume = {34}, year = {2021}, } @article{10809, abstract = {Thermoelectric materials are engines that convert heat into an electrical current. Intuitively, the efficiency of this process depends on how many electrons (charge carriers) can move and how easily they do so, how much energy those moving electrons transport, and how easily the temperature gradient is maintained. In terms of material properties, an excellent thermoelectric material requires a high electrical conductivity σ, a high Seebeck coefficient S (a measure of the induced thermoelectric voltage as a function of temperature gradient), and a low thermal conductivity κ. The challenge is that these three properties are strongly interrelated in a conflicting manner (1). On page 722 of this issue, Roychowdhury et al. (2) have found a way to partially break these ties in silver antimony telluride (AgSbTe2) with the addition of cadmium (Cd) cations, which increase the ordering in this inherently disordered thermoelectric material.}, author = {Liu, Yu and Ibáñez, Maria}, issn = {1095-9203}, journal = {Science}, number = {6530}, pages = {678--679}, publisher = {American Association for the Advancement of Science}, title = {{Tidying up the mess}}, doi = {10.1126/science.abg0886}, volume = {371}, year = {2021}, } @article{8317, abstract = {When can a polyomino piece of paper be folded into a unit cube? Prior work studied tree-like polyominoes, but polyominoes with holes remain an intriguing open problem. We present sufficient conditions for a polyomino with one or several holes to fold into a cube, and conditions under which cube folding is impossible. In particular, we show that all but five special “basic” holes guarantee foldability.}, author = {Aichholzer, Oswin and Akitaya, Hugo A. and Cheung, Kenneth C. and Demaine, Erik D. and Demaine, Martin L. and Fekete, Sándor P. and Kleist, Linda and Kostitsyna, Irina and Löffler, Maarten and Masárová, Zuzana and Mundilova, Klara and Schmidt, Christiane}, issn = {1879-081X}, journal = {Computational Geometry: Theory and Applications}, publisher = {Elsevier}, title = {{Folding polyominoes with holes into a cube}}, doi = {10.1016/j.comgeo.2020.101700}, volume = {93}, year = {2021}, } @unpublished{10912, abstract = {Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.}, author = {Lombardi, Fabrizio and Pepic, Selver and Shriki, Oren and Tkačik, Gašper and De Martino, Daniele}, publisher = {arXiv}, title = {{Quantifying the coexistence of neuronal oscillations and avalanches}}, doi = {10.48550/ARXIV.2108.06686}, year = {2021}, } @unpublished{10080, abstract = {Hippocampal and neocortical neural activity is modulated by the position of the individual in space. While hippocampal neurons provide the basis for a spatial map, prefrontal cortical neurons generalize over environmental features. Whether these generalized representations result from a bidirectional interaction with, or are mainly derived from hippocampal spatial representations is not known. By examining simultaneously recorded hippocampal and medial prefrontal neurons, we observed that prefrontal spatial representations show a delayed coherence with hippocampal ones. We also identified subpopulations of cells in the hippocampus and medial prefrontal cortex that formed functional cross-area couplings; these resembled the optimal connections predicted by a probabilistic model of spatial information transfer and generalization. Moreover, cross-area couplings were strongest and had the shortest delay preceding spatial decision-making. Our results suggest that generalized spatial coding in the medial prefrontal cortex is inherited from spatial representations in the hippocampus, and that the routing of information can change dynamically with behavioral demands.}, author = {Nardin, Michele and Käfer, Karola and Csicsvari, Jozsef L}, booktitle = {bioRxiv}, title = {{The generalized spatial representation in the prefrontal cortex is inherited from the hippocampus}}, doi = {10.1101/2021.09.30.462269}, year = {2021}, } @article{9905, abstract = {Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.}, author = {Rella, Simon and Kulikova, Yuliya A. and Dermitzakis, Emmanouil T. and Kondrashov, Fyodor}, issn = {2045-2322}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Nature}, title = {{Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains}}, doi = {10.1038/s41598-021-95025-3}, volume = {11}, year = {2021}, } @phdthesis{9418, abstract = {Deep learning is best known for its empirical success across a wide range of applications spanning computer vision, natural language processing and speech. Of equal significance, though perhaps less known, are its ramifications for learning theory: deep networks have been observed to perform surprisingly well in the high-capacity regime, aka the overfitting or underspecified regime. Classically, this regime on the far right of the bias-variance curve is associated with poor generalisation; however, recent experiments with deep networks challenge this view. This thesis is devoted to investigating various aspects of underspecification in deep learning. First, we argue that deep learning models are underspecified on two levels: a) any given training dataset can be fit by many different functions, and b) any given function can be expressed by many different parameter configurations. We refer to the second kind of underspecification as parameterisation redundancy and we precisely characterise its extent. Second, we characterise the implicit criteria (the inductive bias) that guide learning in the underspecified regime. Specifically, we consider a nonlinear but tractable classification setting, and show that given the choice, neural networks learn classifiers with a large margin. Third, we consider learning scenarios where the inductive bias is not by itself sufficient to deal with underspecification. We then study different ways of ‘tightening the specification’: i) In the setting of representation learning with variational autoencoders, we propose a hand- crafted regulariser based on mutual information. ii) In the setting of binary classification, we consider soft-label (real-valued) supervision. We derive a generalisation bound for linear networks supervised in this way and verify that soft labels facilitate fast learning. Finally, we explore an application of soft-label supervision to the training of multi-exit models.}, author = {Bui Thi Mai, Phuong}, issn = {2663-337X}, pages = {125}, publisher = {Institute of Science and Technology Austria}, title = {{Underspecification in deep learning}}, doi = {10.15479/AT:ISTA:9418}, year = {2021}, } @inproceedings{9416, abstract = {We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of two max-margin classifiers: one corresponding to the positive data subset and one corresponding to the negative data subset. The proof is based on the recently introduced concept of extremal sectors, for which we prove a number of properties in the context of orthogonal separability. In particular, we prove stationarity of activation patterns from some time onwards, which enables a reduction of the ReLU network to an ensemble of linear subnetworks.}, author = {Bui Thi Mai, Phuong and Lampert, Christoph}, booktitle = {9th International Conference on Learning Representations}, location = {Virtual}, title = {{The inductive bias of ReLU networks on orthogonally separable data}}, year = {2021}, } @unpublished{10579, abstract = {We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a "token" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.}, author = {Kavcic, Bor and Tkačik, Gašper}, booktitle = {arXiv}, title = {{Token-driven totally asymmetric simple exclusion process}}, doi = {10.48550/arXiv.2112.13558}, year = {2021}, } @article{15279, abstract = {We formulate and prove an analog of Poonen’s finite-field Bertini theorem with Taylor conditions that holds in the Grothendieck ring of varieties. This gives a broad generalization of the work of Vakil and Wood, who treated the case of smooth hypersurface sections, and is made possible by the use of motivic Euler products to write down candidate motivic probabilities. As applications, we give motivic analogs of many results in arithmetic statistics that have been proven using Poonen’s sieve, including work of Bucur and Kedlaya on complete intersections and Erman and Wood on semiample Bertini theorems.}, author = {Bilu, Margaret and Howe, Sean}, issn = {1944-7833}, journal = {Algebra & Number Theory}, keywords = {Algebra and Number Theory}, number = {9}, pages = {2195--2259}, publisher = {Mathematical Sciences Publishers}, title = {{Motivic Euler products in motivic statistics}}, doi = {10.2140/ant.2021.15.2195}, volume = {15}, year = {2021}, } @unpublished{12076, abstract = {We find an asymptotic formula for the number of primitive vectors $(z_1,\ldots,z_4)\in (\mathbb{Z}_{\neq 0})^4$ such that $z_1,\ldots, z_4$ are all squareful and bounded by $B$, and $z_1+\cdots + z_4 = 0$. Our result agrees in the power of $B$ and $\log B$ with the Campana-Manin conjecture of Pieropan, Smeets, Tanimoto and V\'{a}rilly-Alvarado.}, author = {Shute, Alec L}, booktitle = {arXiv}, title = {{Sums of four squareful numbers}}, doi = {10.48550/arXiv.2104.06966}, year = {2021}, }