Recent Submissions 

  1. Televised Debates and Emotional Appeals in Politics: Evidence from C-SPAN 

    Gennaro, Gloria; Ash, Elliott (2023)
    Center for Law & Economics Working Paper Series
    We study the effect of televised broadcasts of floor debates on the rhetoric and behav ior of U.S. Congress Members. First, we show in a differences-in-differences analysis that the introduction of C-SPAN broadcasts in 1979 increased the use of emotional appeals in the House relative to the Senate, where televised floor debates were not introduced until later. Second, we use exogenous variation in C-SPAN channel posi tioning as an instrument ...
    Working Paper
  2. Von austauschbaren Physikern, Zukunftsmaschinen und flexiblen Computern 

    Schmid, Julius (2023)
    Preprints zur Kulturgeschichte der Technik
    Working Paper
  3. Primate pre-arcuate cortex actively maintains persistent representations of saccades from plans to outcomes 

    Calangiu, Ioana; Kollmorgen, Sepp; Reppas, John; et al. (2022)
    bioRxiv
    Dorso-lateral prefrontal cortex is thought to contribute to adaptive behavior by integrating temporally dispersed, behaviorally-relevant factors. Past work has revealed a variety of neural representations preceding actions, which are involved in internal processes like planning, working memory and covert attention. Task-related activity following actions has often been reported, but so far lacks a clear interpretation. We leveraged modified ...
    Working Paper
  4. A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making 

    Pagan, Marino; Tang, Vincent D.; Aoi, Mikio C.; et al. (2022)
    bioRxiv
    The ability to flexibly select and accumulate relevant information to form decisions, while ignoring irrelevant information, is a fundamental component of higher cognition. Yet its neural mechanisms remain unclear. Here we demonstrate that, under assumptions supported by both monkey and rat data, the space of possible network mechanisms to implement this ability is spanned by the combination of three different components, each with specific ...
    Working Paper
  5. Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex 

    Ehret, Benjamin; Boehringer, Roman; Amadei, Elizabeth A.; et al. (2023)
    bioRxiv
    The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based ...
    Working Paper
  6. Stochastic chain termination in bacterial pilus assembly 

    Giese, Christoph; Puorger, Chasper; Ignatov, Oleksandr; et al. (2022)
    bioRxiv
    Adhesive type 1 pili from uropathogenic Escherichia coli strains are filamentous, supramolecular protein complexes consisting of a short tip fibrillum and a long, helical rod formed by up to several thousand copies of the major pilus subunit FimA. Here, we reconstituted the entire type 1 pilus rod assembly reaction in vitro, using all constituent protein subunits in the presence of the assembly platform FimD, and identified the so-far ...
    Working Paper
  7. The Labor Market Effects of Restricting Refugees’ Employment Opportunities 

    Ahrens, Achim; Beerli, Andreas; Hangartner, Dominik; et al. (2023)
    KOF Working Papers
    Refugees, and immigrants more generally, often do not have access to all jobs in the labor market. We argue that restrictions on employment opportunities help explain why immigrants have lower employment and wages than native citizens. To test this hypothesis, we leverage refugees’ exogenous geographic assignment in Switzerland, within-canton variation in labor market restrictions, and linked register data 1999–2016. We document large ...
    Working Paper
  8. Empowering Data Centers for Next Generation Trusted Computing 

    Dhar, Aritra; Sridhara, Supraja; Shinde, Shweta; et al. (2022)
    arXiv
    Modern data centers have grown beyond CPU nodes to provide domain-specific accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud customers want to protect their data. They are willing to pay additional costs for trusted execution environments such as enclaves provided by Intel SGX and AMD SEV. Unfortunately, the customers have to make a critical choice -- either use domain-specific accelerators for speed ...
    Working Paper
  9. First results on QCD+QED with C* boundary conditions 

    Bushnaq, Lucius; Campos, Isabel; Catillo, Marco; et al. (2022)
    arXiv
    Accounting for isospin-breaking corrections is critical for achieving subpercent precision in lattice computations of hadronic observables. A way to include QED and strong-isospin-breaking corrections in lattice QCD calculations is to impose C⋆ boundary conditions in space. Here, we demonstrate the computation of a selection of meson and baryon masses on two QCD and five QCD+QED gauge ensembles in this setup, which preserves locality, ...
    Working Paper
  10. On the Identifiability and Estimation of Causal Location-Scale Noise Models 

    Immer, Alexander; Schultheiss, Christoph; Vogt, Julia E.; et al. (2022)
    arXiv
    We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i.e., $Y = f(X) + g(X)N$. Despite the generality of the model class, we show the causal direction is identifiable up to some pathological cases. To empirically validate these theoretical ...
    Working Paper
  11. Complexity reduction in resonant open quantum system Tavis-Cummings model with quantum circuit mapping 

    Marinkovic, Marina; Radulaski, Marina (2022)
    arXiv
    vis-Cummings (TC) cavity quantum electrodynamical effects, describing the interaction of N atoms with an optical resonator, are at the core of atomic, optical and solid state physics. The full numerical simulation of TC dynamics scales exponentially with the number of atoms. By restricting the open quantum system to a single excitation, typical of experimental realizations in quantum optics, we analytically solve the TC model with an ...
    Working Paper
  12. Entropy Maximization with Depth: A Variational Principle for Random Neural Networks 

    Joudaki, Amir; Daneshmand, Hadi; Bach, Francis (2022)
    arXiv
    To understand the essential role of depth in neural networks, we investigate a variational principle for depth: Does increasing depth perform an implicit optimization for the representations in neural networks? We prove that random neural networks equipped with batch normalization maximize the differential entropy of representations with depth up to constant factors, assuming that the representations are contractive. Thus, representations ...
    Working Paper
  13. PAC-Bayesian Meta-Learning: From Theory to Practice 

    Rothfuss, Jonas; Josifoski, Martin; Fortuin, Vincent; et al. (2022)
    arXiv
    Meta-Learning aims to accelerate the learning on new tasks by acquiring useful inductive biases from related data sources. In practice, the number of tasks available for meta-learning is often small. Yet, most of the existing approaches rely on an abundance of meta-training tasks, making them prone to overfitting. How to regularize the meta-learner to ensure generalization to unseen tasks, is a central question in the literature. We provide ...
    Working Paper
  14. Neuromorphic Visual Odometry with Resonator Networks 

    Renner, Alpha; Supic, Lazar; Danielescu, Andreea; et al. (2022)
    Autonomous agents require self-localization to navigate in unknown environments. They can use Visual Odometry (VO) to estimate self-motion and localize themselves using visual sensors. This motion-estimation strategy is not compromised by drift as inertial sensors or slippage as wheel encoders. However, VO with conventional cameras is computationally demanding, limiting its application in systems with strict low-latency, -memory, and ...
    Working Paper
  15. Scrooge: A Fast and Memory-Frugal Genomic Sequence Aligner for CPUs, GPUs, and ASICs 

    Lindegger, Joël; Cali, Damla Senol; Alser, Mohammed; et al. (2022)
    Motivation: Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations and/or hardware acceleration. A promising candidate for all of the above is the recently proposed GenASM algorithm. We identify and address three inefficiencies in the GenASM algorithm: it has a high amount of data movement, a large memory footprint, and does some ...
    Working Paper
  16. ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis 

    Firtina, Can; Pillai, Kamlesh; Kalsi, Gurpreet S.; et al. (2022)
    Profile hidden Markov models (pHMMs) are widely used in many bioinformatics applications to accurately identify similarities between biological sequences (e.g., DNA or protein sequences). PHMMs use a commonly-adopted and highly-accurate method, called the Baum-Welch algorithm, to calculate these similarities. However, the Baum-Welch algorithm is computationally expensive, and existing works provide either software- or hardware-only solutions ...
    Working Paper
  17. Sectored DRAM: An Energy-Efficient High-Throughput and Practical Fine-Grained DRAM Architecture 

    Olgun, Ataberk; Bostanci, F. Nisa; Oliveira, Geraldo F.; et al. (2022)
    There are two major sources of inefficiency in computing systems that use modern DRAM devices as main memory. First, due to coarse-grained data transfers (size of a cache block, usually 64B between the DRAM and the memory controller, systems waste energy on transferring data that is not used. Second, due to coarse-grained DRAM row activation, systems waste energy by activating DRAM cells that are unused in many workloads where spatial ...
    Working Paper
  18. An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System 

    Gómez-Luna, Juan; Guo, Yuxin; Brocard, Sylvan; et al. (2022)
    Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly data movement between memory units and processing units, which consumes large amounts of energy and execution cycles. Memory-centric computing systems, i.e., with processing-in-memory (PIM) capabilities, ...
    Working Paper
  19. EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators 

    Orosa, Lois; Koppula, Skanda; Umuroglu, Yaman; et al. (2022)
    Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and high-resolution image generation. Although these kernels have grown in popularity, they stress current compute systems due to their high memory intensity, exascale compute demands, and large energy consumption. We find that ...
    Working Paper

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