@inproceedings{19511,
  abstract     = {We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits. QuaRot rotates LLMs in a way that removes outliers from the hidden state without changing the output, making quantization easier. This computational invariance is applied to the hidden state (residual) of the LLM, as well as to the activations of the feed-forward components, aspects of the attention mechanism, and to the KV cache. The result is a quantized model where all matrix multiplications are performed in 4 bits, without any channels identified for retention in higher precision. Our 4-bit quantized LLAMA2-70B model has losses of at most 0.47 WikiText-2 perplexity and retains 99% of the zero-shot performance. We also show that QuaRot can provide lossless 6 and 8 bit LLAMA-2 models without any calibration data using round-to-nearest quantization. Code is available at github.com/spcl/QuaRot.},
  author       = {Ashkboos, Saleh and Mohtashami, Amirkeivan and Croci, Maximilian L. and Li, Bo and Cameron, Pashmina and Jaggi, Martin and Alistarh, Dan-Adrian and Hoefler, Torsten and Hensman, James},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{QuaRot: Outlier-free 4-bit inference in rotated LLMs}},
  volume       = {37},
  year         = {2024},
}

@inproceedings{19512,
  abstract     = {Differential privacy with gradual expiration models the setting where data items
arrive in a stream and at a given time t the privacy loss guaranteed for a data item
seen at time (t − d) is εg(d), where g is a monotonically non-decreasing function.
We study the fundamental continual (binary) counting problem where each data
item consists of a bit, and the algorithm needs to output at each time step the sum of
all the bits streamed so far. For a stream of length T and privacy without expiration
continual counting is possible with maximum (over all time steps) additive error
O(log2
(T)/ε) and the best known lower bound is Ω(log(T)/ε); closing this gap
is a challenging open problem.
We show that the situation is very different for privacy with gradual expiration by
giving upper and lower bounds for a large set of expiration functions g. Specifically,
our algorithm achieves an additive error of O(log(T)/ε) for a large set of privacy
expiration functions. We also give a lower bound that shows that if C is the additive
error of any ε-DP algorithm for this problem, then the product of C and the privacy
expiration function after 2C steps must be Ω(log(T)/ε). Our algorithm matches
this lower bound as its additive error is O(log(T)/ε), even when g(2C) = O(1).
Our empirical evaluation shows that we achieve a slowly growing privacy loss
with significantly smaller empirical privacy loss for large values of d than a natural
baseline algorithm.},
  author       = {Andersson, Joel Daniel and Henzinger, Monika H and Pagh, Rasmus and Steiner, Teresa Anna and Upadhyay, Jalaj},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{Continual counting with gradual privacy expiration}},
  volume       = {37},
  year         = {2024},
}

@inproceedings{19515,
  abstract     = {Neural models learn data representations that lie on low-dimensional manifolds,
yet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show
that this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks.
To this end, we introduce a multi-purpose framework to the representation learning
community, which allows to: (i) compare different spaces in an interpretable way
and measure their intrinsic similarity; (ii) find correspondences between them, both
in unsupervised and weakly supervised settings, and (iii) to effectively transfer
representations between distinct spaces. We validate our framework on various
applications, ranging from stitching to retrieval tasks, and on multiple modalities,
demonstrating that Latent Functional Maps can serve as a swiss-army knife for
representation alignment},
  author       = {Fumero, Marco and Pegoraro, Marco and Maiorca, Valentino and Locatello, Francesco and Rodolà, Emanuele},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{Latent functional maps: A spectral framework for representation alignment}},
  volume       = {37},
  year         = {2024},
}

@inproceedings{19517,
  abstract     = {In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce cycle consistency of the permutations when merging n ≥ 3 models, allowing circular compositions of permutations to be computed without accumulating error along the path. We qualitatively and quantitatively motivate the need for such a constraint, showing its benefits when merging sets of models in scenarios spanning varying architectures and datasets. We finally show that, when coupled
with activation renormalization, our approach yields the best results in the task.},
  author       = {Crisostomi, Donato and Fumero, Marco and Baieri, Daniele and Bernard, Florian and Rodolà, Emanuele},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{C2M3: Cycle-consistent multi-model merging}},
  volume       = {37},
  year         = {2024},
}

@inproceedings{19518,
  abstract     = {The rising footprint of machine learning has led to a focus on imposing model
sparsity as a means of reducing computational and memory costs. For deep neural
networks (DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics
inspired by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al.,
1989, Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature
information to make better pruning decisions. Yet, these results still lack a solid
theoretical understanding, and it is unclear whether they can be improved by
leveraging connections to the wealth of work on sparse recovery algorithms. In this
paper, we draw new connections between these two areas and present new sparse
recovery algorithms inspired by the OBS framework that comes with theoretical
guarantees under reasonable assumptions and have strong practical performance.
Specifically, our work starts from the observation that we can leverage curvature
information in OBS-like fashion upon the projection step of classic iterative sparse
recovery algorithms such as IHT. We show for the first time that this leads both
to improved convergence bounds under standard assumptions. Furthermore, we
present extensions of this approach to the practical task of obtaining accurate sparse
DNNs, and validate it experimentally at scale for Transformer-based models on
vision and language tasks.},
  author       = {Wu, Diyuan and Modoranu, Ionut-Vlad and Safaryan, Mher and Kuznedelev, Denis and Alistarh, Dan-Adrian},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information}},
  volume       = {37},
  year         = {2024},
}

@inproceedings{19519,
  abstract     = {There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices. Existing work focused on improved one-shot quantization techniques and weight representations; yet, purely post-training approaches are reaching diminishing returns in terms of the accuracy-vs-bit-width trade-off. State-of-the-art quantization methods such as QuIP# and AQLM include fine-tuning (part of) the compressed parameters over a limited amount of calibration data; however, such fine-tuning techniques over compressed weights often make exclusive use of straight-through estimators (STE), whose performance is not well-understood in this setting. In this work, we question the use of STE for extreme LLM compression, showing that it can be sub-optimal, and perform a systematic study of quantization-aware fine-tuning strategies for LLMs.We propose PV-Tuning - a representation-agnostic framework that generalizes and improves upon existing fine-tuning strategies, and provides convergence guarantees in restricted cases.On the practical side, when used for 1-2 bit vector quantization, PV-Tuning outperforms prior techniques for highly-performant models such as Llama and Mistral. Using PV-Tuning, we achieve the first Pareto-optimal quantization for Llama-2 family models at 2 bits per parameter.},
  author       = {Malinovskii, Vladimir and Mazur, Denis and Ilin, Ivan and Kuznedelev, Denis and Burlachenko, Konstantin and Yi, Kai and Alistarh, Dan-Adrian and Richtarik, Peter},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  isbn         = {9798331314385},
  issn         = {1049-5258},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{PV-tuning: Beyond straight-through estimation for extreme LLM compression}},
  volume       = {37},
  year         = {2024},
}

@unpublished{19520,
  abstract     = {Vertebrates exhibit a wide range of motor behaviors, ranging from swimming to complex limb-based movements. Here we take advantage of frog metamorphosis, which captures a swim-to-limb-based movement transformation during the development of a single organism, to explore changes in the underlying spinal circuits. We find that the tadpole spinal cord contains small and largely homogeneous populations of motor neurons (MNs) and V1 interneurons (V1s) at early escape swimming stages. These neuronal populations only modestly increase in number and subtype heterogeneity with the emergence of free swimming. In contrast, during frog metamorphosis and the emergence of limb movement, there is a dramatic expansion of MN and V1 interneuron number and transcriptional heterogeneity, culminating in cohorts of neurons that exhibit striking molecular similarity to mammalian motor circuits. CRISPR/Cas9-mediated gene disruption of the limb MN and V1 determinants FoxP1 and Engrailed-1, respectively, results in severe but selective deficits in tail and limb function. Our work thus demonstrates that neural diversity scales exponentially with increasing behavioral complexity and illustrates striking evolutionary conservation in the molecular organization and function of motor circuits across species.},
  author       = {Vijatovic, David and Toma, Florina Alexandra  and Harrington, Zoe P and Sommer, Christoph M and Hauschild, Robert and Trevisan, Alexandra J. and Chapman, Phillip and Julseth, Mara and Brenner-Morton, Susan and Gabitto, Mariano I. and Dasen, Jeremy S. and Bikoff, Jay B. and Sweeney, Lora Beatrice Jaeger},
  booktitle    = {bioRxiv},
  title        = {{Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis}},
  doi          = {10.1101/2024.09.20.614050},
  year         = {2024},
}

@unpublished{19545,
  abstract     = {We prove the Eigenstate Thermalisation Hypothesis for Wigner matrices
uniformly in the entire spectrum, in particular near the spectral edges, with a
bound on the fluctuation that is optimal for any observable. This complements
earlier works of Cipolloni et. al. (Comm. Math. Phys. 388, 2021; Forum Math.,
Sigma 10, 2022) and Benigni et. al. (Comm. Math. Phys. 391, 2022; arXiv:
2303.11142) that were restricted either to the bulk of the spectrum or to
special observables. As a main ingredient, we prove a new multi-resolvent local
law that optimally accounts for the edge scaling.},
  author       = {Cipolloni, Giorgio and Erdös, László and Henheik, Sven Joscha},
  booktitle    = {arXiv},
  title        = {{Eigenstate thermalisation at the edge for Wigner matrices}},
  doi          = {10.48550/arXiv.2309.05488},
  year         = {2024},
}

@unpublished{19547,
  abstract     = {For correlated real symmetric or complex Hermitian random matrices, we prove
that the local eigenvalue statistics at any cusp singularity are universal.
Since the density of states typically exhibits only square root edge or cubic
root cusp singularities, our result completes the proof of the
Wigner-Dyson-Mehta universality conjecture in all spectral regimes for a very
general class of random matrices. Previously only the bulk and the edge
universality were established in this generality [arXiv:1804.07744], while cusp
universality was proven only for Wigner-type matrices with independent entries
[arXiv:1809.03971, arXiv:1811.04055]. As our main technical input, we prove an
optimal local law at the cusp using the Zigzag strategy, a recursive tandem of
the characteristic flow method and a Green function comparison argument.
Moreover, our proof of the optimal local law holds uniformly in the spectrum,
thus also re-establishing universality of the local eigenvalue statistics in
the previously studied bulk [arXiv:1705.10661] and edge [arXiv:1804.07744]
regimes.},
  author       = {Erdös, László and Henheik, Sven Joscha and Riabov, Volodymyr},
  booktitle    = {arXiv},
  title        = {{Cusp universality for correlated random matrices}},
  doi          = {10.48550/arXiv.2410.06813},
  year         = {2024},
}

@unpublished{19551,
  abstract     = {We introduce a notion of a \emph{local gap} for interacting many-body quantum lattice systems and prove the validity of response theory and Kubo's formula for localized perturbations in such settings.
On a high level, our result shows that the usual spectral gap condition, concerning the system as a whole, is not a necessary condition for understanding local properties of the system.
More precisely, we say that an equilibrium state ρ0 of a Hamiltonian H0 is locally gapped in Λgap⊂Λ, whenever the Liouvillian −i[H0,⋅] is almost invertible on local observables supported in Λgap when tested in ρ0.
To put this into context, we provide other alternative notions of a local gap and discuss their relations.
The validity of response theory is based on the construction of \emph{non-equilibrium almost stationary states} (NEASSs).
By controlling locality properties of the NEASS construction, we show that response theory holds to any order, whenever the perturbation \(\epsilon V\) acts in a region which is further than |logϵ| away from the non-gapped region Λ∖Λgap.},
  author       = {Henheik, Sven Joscha and Wessel, Tom},
  booktitle    = {arXiv},
  title        = {{Response theory for locally gapped systems}},
  doi          = {10.48550/arXiv.2410.10809},
  year         = {2024},
}

@misc{19800,
  abstract     = {This archive contains all the code and data necessary to reproduce the results presented in the 
"Mapping the attractor landscape of Boolean networks" paper.},
  author       = {trinh, Van Giang and Park, Kyu Hyong and Pastva, Samuel and Rozum, Jordan},
  publisher    = {Zenodo},
  title        = {{Mapping the attractor landscape of Boolean networks}},
  doi          = {10.5281/ZENODO.13854759},
  year         = {2024},
}

@misc{19884,
  abstract     = {This is Marlin, a Mixed Auto-Regressive Linear kernel (and the name of one of the planet's fastest fish), an extremely optimized FP16xINT4 matmul kernel aimed at LLM inference that can deliver close to ideal (4x) speedups up to batchsizes of 16-32 tokens (in contrast to the 1-2 tokens of prior work with comparable speedup).

Additionally, it includes Sparse-Marlin, an extension of the MARLIN kernels adding support to 2:4 weight sparsity, achieving 5.3x speedups on NVIDIA GPUs (Ampere/Ada).},
  author       = {Frantar, Elias and Castro, Roberto and Chen, Jiale and Hoefler, Torsten and Alistarh, Dan-Adrian},
  publisher    = {Zenodo},
  title        = {{MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models}},
  doi          = {10.5281/ZENODO.14213091},
  year         = {2024},
}

@article{20039,
  abstract     = {This Comment presents a high-level protocol for data harmonization within large cohorts, in which it postulates four main steps including (1) expert review, (2) pre-statistical harmonization, (3) statistical harmonization, and (4) validation.},
  author       = {Neidhart, Maja and Kjelkenes, Rikka and Jansone, Karina and Rehák Bučková, Barbora and Holz, Nathalie and Nees, Frauke and Walter, Henrik and Schumann, Gunter and Rapp, Michael A. and Banaschewski, Tobias and Schwarz, Emanuel and Marquand, Andre and Ogoh, George and Stahl, Bernd and Young, Allan H. and Desrivières, Sylvane and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Heilmann-Heimbach, Stefanie and Mathey, Carina M. and Miller, Abigail and Claus, Isabelle and Nöthen, Markus M. and Hoffmann, Per and Forstner, Andreas J. and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Stringaris, Argyris and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas},
  issn         = {2731-6076},
  journal      = {Nature Mental Health},
  number       = {10},
  pages        = {1134--1137},
  publisher    = {Springer Nature},
  title        = {{A protocol for data harmonization in large cohorts}},
  doi          = {10.1038/s44220-024-00315-0},
  volume       = {2},
  year         = {2024},
}

@misc{20057,
  abstract     = {In article number 2305128, Qing Sun, Shang Wang, Yanhong Tian, Andreu Cabot, and co-workers report an investigation of the energy-storage mechanism of a layered Bi2Te3-based cathode for aqueous zinc-ion batteries (ZIBs). They demonstrate that the zinc ion is not inserted into the cathode as previously assumed; in contrast, proton charge-storage dominates the process. They also demonstrate the great application prospects of aqueous ZIBs in flexible electronics via jet printing technology.},
  author       = {Zeng, Guifang and Sun, Qing and Horta, Sharona and Wang, Shang and Lu, Xuan and Zhang, Chao Yue and Li, Jing and Li, Junshan and Ci, Lijie and Tian, Yanhong and Ibáñez, Maria and Cabot, Andreu},
  booktitle    = {Advanced Materials},
  issn         = {1521-4095},
  number       = {1},
  publisher    = {Wiley},
  title        = {{A layered Bi2Te3@PPy cathode for aqueous Zinc‐Ion batteries: Mechanism and application in printed flexible batteries}},
  doi          = {10.1002/adma.202470004},
  volume       = {36},
  year         = {2024},
}

@unpublished{20071,
  abstract     = {Farkas established that a system of linear inequalities has a solution if and only if we cannot obtain a contradiction by taking a linear combination of the inequalities. We state and formally prove several Farkas-like theorems over linearly ordered fields in Lean 4. Furthermore, we extend duality theory to the case when some coefficients are allowed to take "infinite values".},
  author       = {Dvorak, Martin and Kolmogorov, Vladimir},
  booktitle    = {arXiv},
  keywords     = {Farkas lemma, linear programming, extended reals, calculus of inductive constructions},
  title        = {{Duality theory in linear optimization and its extensions -- formally  verified}},
  doi          = {10.48550/arXiv.2409.08119},
  year         = {2024},
}

@misc{20121,
  abstract     = {PyDaddy is an open source package which is a key contribution of the manuscript Nabeel et al, arXiv:2205.02645. The basic scientific premise for this package is to discover the nature of stochasticity in ecological time series datasets. It is well known that the stochasticity can affect the dynamics of ecological systems in counter-intuitive ways. Without understanding the equations (typically, in the form of stochastic differential equations or SDEs, in short) that govern the dynamics of populations or ecosystems, it's challenging to determine the impact of randomness on real datasets. In this manuscript and accompanying package, we introduce a methodology for discovering equations (SDEs) that transforms time series data of state variables into stochastic differential equations. This approach merges traditional stochastic calculus with modern equation-discovery techniques. We showcase the generality of our method through various applications and discuss its limitations and potential pitfalls, offering diagnostic measures to address these challenges.},
  author       = {Nabeel, Arshed and Karichannavar, Ashwin and Palathingal, Shuaib and Jhawar, Jitesh and Brückner, David and Danny Raj, Masila and Guttal, Vishwesha},
  publisher    = {Zenodo},
  title        = {{PyDaddy: A Python Package for Discovering SDEs from Time Series Data}},
  doi          = {10.5281/ZENODO.7137151},
  year         = {2024},
}

@article{20156,
  abstract     = {Integrative analyses that incorporate different levels of ‘-omics’ data represent a powerful tool for deciphering the biological mechanisms that underlie environmental influences on mental health and disease. This Comment highlights various aspects of such multi-omics approaches, using the example of the EU-funded environMENTAL project.},
  author       = {Desrivières, Sylvane and Miller, Abigail and Mathey, Carina M. and Yu, Xinyang and Chen, Di and Agunbiade, Kofoworola and Heilmann-Heimbach, Stefanie and Forstner, Andreas J. and Schumann, Gunter and Hoffmann, Per and Nöthen, Markus M. and Ogoh, George and Stahl, Bernd and Young, Allan H. and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Claus, Isabelle and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Marquand, Andre and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Kjelkenes, Rikka and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Neidhart, Maja and Stringaris, Argyris and Schwarz, Emanuel and Holz, Nathalie and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Jansone, Karina and Banaschewski, Tobias and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas and Walter, Henrik},
  issn         = {2731-6076},
  journal      = {Nature Mental Health},
  number       = {10},
  pages        = {1131--1133},
  publisher    = {Springer Nature},
  title        = {{Multi-omics analyses of the environMENTAL project provide insights into mental health and disease}},
  doi          = {10.1038/s44220-024-00317-y},
  volume       = {2},
  year         = {2024},
}

@article{20157,
  abstract     = {The focus of much of contemporary research ethics is on compliance with established protocols. However, large data-driven neuroscience research raises new ethical concerns that have no agreed-upon solution. Here we reflect on these challenges and propose better integration of public and patient involvement in this evolving landscape.},
  author       = {Stahl, Bernd and Ogoh, George and Schumann, Gunter and Walter, Henrik and Stahl, Bernd and Young, Allan H. and Desrivières, Sylvane and Clinton, Nicholas and Thompson, Paul and Schwalber, Ameli and Liu, Jingyu and Calhoun, Vince and Chang, Xiao and Xia, Yunman and Gong, Yanting and Jia, Tianye and Renner, Paul and Hese, Sören and Giner, Arantxa and Sanchez, Mavi and Alvarez, Elena and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Otten, Lisa and Pitel, Séverine and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes and Patraskaki, Myrto and Sommer, Peter and Heilmann-Heimbach, Stefanie and Mathey, Carina M. and Miller, Abigail and Claus, Isabelle and Nöthen, Markus M. and Hoffmann, Per and Forstner, Andreas J. and Pastor, Alvaro and Gallego, Jaime and Orosa, Francisco Eiroa and Viapiana, Guillem Feixas and Slater, Mel and Marr, Lena and Novarino, Gaia and Marquand, Andre and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Kjelkenes, Rikka and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Siehl, Sebastian and Nees, Frauke and Neidhart, Maja and Stringaris, Argyris and Schwarz, Emanuel and Holz, Nathalie and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Jansone, Karina and Banaschewski, Tobias and Banks, Jamie and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas and Walter, Henrik},
  issn         = {2731-6076},
  journal      = {Nature Mental Health},
  number       = {10},
  publisher    = {Springer Nature},
  title        = {{Rethinking ethics in interdisciplinary and big data-driven neuroscience projects}},
  doi          = {10.1038/s44220-024-00320-3},
  volume       = {2},
  year         = {2024},
}

@article{10018,
  abstract     = {In order to study integral points of bounded log-anticanonical height on weak del Pezzo surfaces, we classify weak del Pezzo pairs. As a representative example, we consider a quartic del Pezzo surface of singularity type A1 + A3 and prove an analogue of Manin's conjecture for integral points with respect to its singularities and its lines.},
  author       = {Derenthal, Ulrich and Wilsch, Florian Alexander},
  issn         = {1475-3030 },
  journal      = {Journal of the Institute of Mathematics of Jussieu},
  keywords     = {Integral points, del Pezzo surface, universal torsor, Manin’s conjecture},
  number       = {3},
  pages        = {1259--1294},
  publisher    = {Cambridge University Press},
  title        = {{Integral points on singular del Pezzo surfaces}},
  doi          = {10.1017/S1474748022000482},
  volume       = {23},
  year         = {2024},
}

@article{17202,
  abstract     = {Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions have recently emerged as building blocks for hybrid quantum circuits. In this study, we present a gatemon fabricated in planar Germanium. We induce superconductivity in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which separates the superconductor from the Ge quantum well. The Josephson junction is then integrated into an Xmon circuit and capacitively coupled to a transmission line resonator. We showcase the qubit tunability in a broad frequency range with resonator and two-tone spectroscopy. Time-domain characterizations reveal energy relaxation and coherence times up to 75 ns. Our results, combined with the recent advances in the spin qubit field, pave the way towards novel hybrid and protected qubits in a group IV, CMOS-compatible material.},
  author       = {Sagi, Oliver and Crippa, Alessandro and Valentini, Marco and Janik, Marian and Baghumyan, Levon and Fabris, Giorgio and Kapoor, Lucky and Hassani, Farid and Fink, Johannes M and Calcaterra, Stefano and Chrastina, Daniel and Isella, Giovanni and Katsaros, Georgios},
  issn         = {2041-1723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{A gate tunable transmon qubit in planar Ge}},
  doi          = {10.1038/s41467-024-50763-6},
  volume       = {15},
  year         = {2024},
}