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Christian Simon

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Artificial Intelligence Computer Vision cond-mat.stat-mech hep-ex Machine Learning physics.chem-ph physics.ins-det Sound

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preprint2026arXiv

Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis

Current methods for creating drum loop audio in digital music production, such as using one-shot samples or resampling, often demand non-trivial efforts of creators. While recent generative models achieve high fidelity and adhere to text, they lack the specific control needed for such a task. Existing symbolic-to-audio research often focuses on single, tonal instruments, leaving the challenge of polyphonic, percussive drum synthesis unaddressed. We address this gap by introducing ``Break-the-Beat!,'' a model capable of rendering a drum MIDI with the timbre of a reference audio. It is built by fine-tuning a pre-trained text-to-audio model with our proposed content encoder and a effective hybrid conditioning mechanism. To enable this, we construct a new dataset of paired target-reference drum audio from existing drum audio datasets. Experiments demonstrate that our model generates high-quality drum audio that follows high-resolution drum MIDI, achieving strong performance across metrics of audio quality, rhythmic alignment, and beat continuity. This offer producers a new, controllable tool for creative production. Demo page: https://ik4sumii.github.io/break-the-beat/

preprint2024arXiv

Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks

Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability. To overcome the limitations of existing approaches regarding generalization and consistency, we introduce a novel neural rendering technique. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Specifically, our method builds neural encoding volumes from generated multi-view inputs. We adjust the weights of the SDF network conditioned on an input image at test-time to allow model adaptation to novel scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts derived from the synthesized views, we propose the use of a volume transformer module to improve the aggregation of image features instead of processing each viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we avoid the bottleneck of scene-specific optimization and maintain consistency across the images generated from multiple viewpoints. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.

preprint2022arXiv

On Generalizing Beyond Domains in Cross-Domain Continual Learning

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Many recent methods focus on preventing catastrophic forgetting under the assumption of train and test data following similar distributions. In this work, we consider a more realistic scenario of continual learning under domain shifts where the model must generalize its inference to an unseen domain. To this end, we encourage learning semantically meaningful features by equipping the classifier with class similarity metrics as learning parameters which are obtained through Mahalanobis similarity computations. Learning of the backbone representation along with these extra parameters is done seamlessly in an end-to-end manner. In addition, we propose an approach based on the exponential moving average of the parameters for better knowledge distillation. We demonstrate that, to a great extent, existing continual learning algorithms fail to handle the forgetting issue under multiple distributions, while our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.

preprint2016arXiv

Development and test of a real-size MRPC for CBM-TOF

In the CBM (Compressed Baryonic Matter) experiment constructed at the Facility for Anti-proton and Ion Research (Fair) at GSI, Darmstadt, Germany, MRPC(Multi-gap Resistive Plate Chamber) is adopted to construct the large TOF (Time-of-Flight) system to achieve an unprecedented precision of hadron identification, benefiting from its good time resolution, relatively high efficiency and low building price. We have developed a kind of double-ended readout strip MRPC. It uses low resistive glass to keep good performance of time resolution under high-rate condition. The differential double stack structure of 2x4 gas gaps help to reduce the required high voltage to half. There are 24 strips on one counter, and each is 270mm long, 7mm wide and the interval is 3mm. Ground is placed onto the MRPC electrode and feed through is carefully designed to match the 100 Ohm impedance of PADI electronics. The prototype of this strip MRPC has been tested with cosmic ray, a 98% efficiency and 60ps time resolution is gotten. In order to further examine the performance of the detector working under higher particle flux rate, the prototype has been tested in the 2014 October GSI beam time and 2015 February CERN beam time. In both beam times a relatively high rate of 1 kHz/cm2 was obtained. The calibration is done with CBM ROOT. A couple of corrections has been considered in the calibration and analysis process (including time-walk correction, gain correction, strip alignment correction and velocity correction) to access actual counter performances such as efficiency and time resolution. An efficiency of 97% and time resolution of 48ps are obtained. All these results show that the real-size prototype is fully capable of the requirement of the CBM-TOF, and new designs such as self-sealing are modified into the strip counter prototype to obtain even better performance.

preprint2013arXiv

A generic system for critiquing physicians' prescriptions: usability, satisfaction and lessons learnt

Clinical decision support systems have been developed to help physicians to take clinical guidelines into account during consultations. The ASTI critiquing module is one such systems; it provides the physician with automatic criticisms when a drug prescription does not follow the guidelines. It was initially developed for hypertension and type 2 diabetes, but is designed to be generic enough for application to all chronic diseases. We present here the results of usability and satisfaction evaluations for the ASTI critiquing module, obtained with GPs for a newly implemented guideline concerning dyslipaemia, and we discuss the lessons learnt and the difficulties encountered when building a generic DSS for critiquing physicians' prescriptions.

preprint2012arXiv

Including many-body effects in models for ionic liquids

Realistic modeling of ionic systems necessitates taking explicitly account of many-body effects. In molecular dynamics simulations, it is possible to introduce explicitly these effects through the use of additional degrees of freedom. Here we present two models: The first one only includes dipole polarization effect, while the second also accounts for quadrupole polarization as well as the effects of compression and deformation of an ion by its immediate coordination environment. All the parameters involved in these models are extracted from first-principles density functional theory calculations. This step is routinely done through an extended force-matching procedure, which has proven to be very succesfull for molten oxides and molten fluorides. Recent developments based on the use of localized orbitals can be used to complement the force-matching procedure by allowing for the direct calculations of several parameters such as the individual polarizabilities.