Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy and Performance Evaluation of Low Density Internal and External Flows using CFD and DSMC

The Direct Simulation Monte Carlo (DSMC) method was widely used, among others, to simulate low density gas flows with large Knudsen numbers. However, DSMC has its drawbacks when the lower Knudsen number regime (Kn<0.1) is considered. In this case, approaches from classical computational fluid dynamics (CFD) relying on the continuum assumption are the methods of choice, providing accurate solutions at acceptable computational costs. Here, we analyzed the advantages and drawbacks of DSMC/CFD simulations of internal and external flow fields in terms of accuracy and computational effort: We used DSMC and CFD simulations to calculate the flow inside a convergent-divergent nozzle (internal flow) and the flow around a conical body (external flow). CFD simulations utilized the software OpenFOAM and the DSMC solution was obtained using the software SPARTA. The results of these simulation techniques were evaluated in terms of (1) comparison with experimental results, (2) time-to-solution, (3) energy consumption, and (4) feasibility of hybrid CFD-DSMC approaches. Keywords: DSMC; SPARTA; Continuum assumption; Transition regime; Rarefied flow; high-performance computing
Expressive Acoustic Guitar Sound Synthesis with an Instrument-Specific Input Representation and Diffusion Outpainting

Synthesizing performing guitar sound is a highly challenging task due to the polyphony and high variability in expression. Recently, deep generative models have shown promising results in synthesizing expressive polyphonic instrument sounds from music scores, often using a generic MIDI input. In this work, we propose an expressive acoustic guitar sound synthesis model with a customized input representation to the instrument, which we call guitarroll. We implement the proposed approach using diffusion-based outpainting which can generate audio with long-term consistency. To overcome the lack of MIDI/audio-paired datasets, we used not only an existing guitar dataset but also collected data from a high quality sample-based guitar synthesizer. Through quantitative and qualitative evaluations, we show that our proposed model has higher audio quality than the baseline model and generates more realistic timbre sounds than the previous leading work.
MATRYOSHKA: Non-Exclusive Memory Tiering via Transactional Page Migration

With the advent of byte-addressable memory devices, such as CXL memory, persistent memory, and storage-class memory, tiered memory systems have become a reality. Page migration is the de facto method within operating systems for managing tiered memory. It aims to bring hot data whenever possible into fast memory to optimize the performance of data accesses while using slow memory to accommodate data spilled from fast memory. While the existing research has demonstrated the effectiveness of various optimizations on page migration, it falls short of addressing a fundamental question: Is exclusive memory tiering, in which a page is either present in fast memory or slow memory, but not both simultaneously, the optimal strategy for tiered memory management? We demonstrate that page migration-based exclusive memory tiering suffers significant performance degradation when fast memory is under pressure. In this paper, we propose non-exclusive memory tiering, a page management strategy that retains a copy of pages recently promoted from slow memory to fast memory to mitigate memory thrashing. To enable non-exclusive memory tiering, we develop MATRYOSHKA, a new mechanism that features transactional page migration and page shadowing. MATRYOSHKA removes page migration off the program's critical path and makes migration asynchronous. Evaluations with microbenchmarks and realworld applications show that MATRYOSHKA achieves 6x performance improvement over the state-of-the-art transparent page placement (TPP) approach under memory pressure. We also compare MATRYOSHKA with a recently proposed sampling-based migration approach and demonstrate MATRYOSHKA's strengths and potential weaknesses in various scenarios. Through the evaluations, we discover a serious issue facing all tested approaches, unfortunately including MATRYOSHKA, and call for further research on tiered memory-aware memory allocation.
Assessing Influential Observations in Pain Prediction using fMRI Data

Influential diagnosis is an integral part of data analysis, of which most existing methodological frameworks presume a deterministic submodel and are designed for low-dimensional data (i.e., the number of predictors p smaller than the sample size n). However, the stochastic selection of a submodel from high-dimensional data where p exceeds n has become ubiquitous. Thus, methods for identifying observations that could exert undue influence on the choice of a submodel can play an important role in this setting. To date, discussion of this topic has been limited, falling short in two domains: (i) constrained ability to detect multiple influential points, and (ii) applicability only in restrictive settings. After describing the problem, we characterize and formalize the concept of influential observations on variable selection. Then, we propose a generalized diagnostic measure, extended from an available metric accommodating different model selectors and multiple influential observations, the asymptotic distribution of which is subsequently establish large p, thus providing guidelines to ascertain influential observations. A high-dimensional clustering procedure is further incorporated into our proposed scheme to detect multiple influential points. Simulation is conducted to assess the performances of various diagnostic approaches. The proposed procedure further demonstrates its value in improving predictive power when analyzing thermal-stimulated pain based on fMRI data.
Task structure and nonlinearity jointly determine learned representational geometry

The utility of a learned neural representation depends on how well its geometry supports performance in downstream tasks. This geometry depends on the structure of the inputs, the structure of the target outputs, and the architecture of the network. By studying the learning dynamics of networks with one hidden layer, we discovered that the network's activation function has an unexpectedly strong impact on the representational geometry: Tanh networks tend to learn representations that reflect the structure of the target outputs, while ReLU networks retain more information about the structure of the raw inputs. This difference is consistently observed across a broad class of parameterized tasks in which we modulated the degree of alignment between the geometry of the task inputs and that of the task labels. We analyzed the learning dynamics in weight space and show how the differences between the networks with Tanh and ReLU nonlinearities arise from the asymmetric asymptotic behavior of ReLU, which leads feature neurons to specialize for different regions of input space. By contrast, feature neurons in Tanh networks tend to inherit the task label structure. Consequently, when the target outputs are low dimensional, Tanh networks generate neural representations that are more disentangled than those obtained with a ReLU nonlinearity. Our findings shed light on the interplay between input-output geometry, nonlinearity, and learned representations in neural networks.
Discretization-Dependent Dissolution of (Dis)Continuous Gliders: Non-Platonic Self-Organization in Complex Systems

Many simulated complex systems that support persistent self-organizing patterns, i.e. gliders, have a 'state-plus-update' paradigm. This approach can be found in computational models of physics, continuous and neural cellular automata, residual connections in neural networks, and optimization methods like stochastic gradient descent. If the update is the output of a differential equation and modulated by a step size parameter, we have the familiar and general Euler method. Generally, a smaller step size is expected to yield more accurate results, at the expense of more computations to arrive at a desired end point. I examine multiple systems supporting gliders that fit into the Euler method framework, including multiple approaches to continuous cellular automata and the Gray-Scott artificial chemistry system. Each of these systems yield one or more glider pattern-rule pairs that persist under specific, and sometimes quite coarse, discretization conditions, but become unstable at nominally more accurate, finer simulation conditions. These patterns (in combination with the systems they persist in) are clearly not approximations approaching an abstract ideal as discretization tends to zero, but exist on their own, somewhat baffling, terms that include the systematic errors of particular discretization regimes. I refer to these gliders as 'non-Platonic'. Code for replicating or expanding on this work has been made available at https://github.com/RiveSunder/DiscoGliders.
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation

In the rapidly evolving field of AI research, foundational models like BERT and GPT have significantly advanced language and vision tasks. The advent of pretrain-prompting models such as ChatGPT and Segmentation Anything Model (SAM) has further revolutionized image segmentation. However, their applications in specialized areas, particularly in nuclei segmentation within medical imaging, reveal a key challenge: the generation of high-quality, informative prompts is as crucial as applying state-of-the-art (SOTA) fine-tuning techniques on foundation models. To address this, we introduce Segment Any Cell (SAC), an innovative framework that enhances SAM specifically for nuclei segmentation. SAC integrates a Low-Rank Adaptation (LoRA) within the attention layer of the Transformer to improve the fine-tuning process, outperforming existing SOTA methods. It also introduces an innovative auto-prompt generator that produces effective prompts to guide segmentation, a critical factor in handling the complexities of nuclei segmentation in biomedical imaging. Our extensive experiments demonstrate the superiority of SAC in nuclei segmentation tasks, proving its effectiveness as a tool for pathologists and researchers. Our contributions include a novel prompt generation strategy, automated adaptability for diverse segmentation tasks, the innovative application of Low-Rank Attention Adaptation in SAM, and a versatile framework for semantic segmentation challenges.
Reversible out-of-plane to in-plane magnetic transition by electrical and thermal cycling in Ni$_{90}$Fe$_{10}$/BaTiO$_3$(001)

The study investigates the manipulation of the magnetic anisotropy in a thick (1 $\mu$m) Ni$_{90}$Fe$_{10}$ layer electrodeposited on a ferroelectric BaTiO$_3$(001) substrate, using a combination of Magneto-optical Kerr Effect, Photoemission Electron Microscopy with X-ray circular magnetic dichroism and X-ray diffraction. In the as-grown state, the system shows weak perpendicular magnetic anisotropy and characteristic stripe domains. Upon out-of-plane electrical poling of the BaTiO$_3$ substrate, the magnetic anisotropy switches to in-plane with a strong uniaxial behavior. This change is ascribed to the magnetoelastic effect due to the switching of the BaTiO$_3$ ferroelectric [001] axis into the sample plane, as evidenced by XRD. The strong mechanical interaction with the thick Ni$_{90}$Fe$_{10}$ overlayer prevents the full inversion of the substrate. The perpendicular magnetic anisotropy can be recovered by a mild thermal annealing above the BaTiO$_3$ tetrahedral to cubic phase transition and can be cycled by repeated electrical poling/thermal annealing. This method opens the path to a reversible control of the magnetic anisotropy in Ni$_{90}$Fe$_{10}$/BaTiO$_3$ heterostructures from perpendicular to in-plane.
Considering Capacitive Effects in Foil Winding Homogenization

In conventional finite element simulations, foil windings with a thin foil and many turns require many mesh elements. This renders models quickly computationally infeasible. With the use of homogenization approaches, the finite element mesh does not need to resolve the small-scale structure of the foil winding domain. Present homogenization approaches take resistive and inductive effects into account. With an increase of the operation frequency of foil windings, however, capacitive effects between adjacent turns in the foil winding become relevant. This paper presents an extension to the standard foil winding model that covers the capacitive behavior of foil windings.
A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms

In the intricate acoustic landscapes where speech intelligibility is challenged by noise and reverberation, multichannel speech enhancement emerges as a promising solution for individuals with hearing loss. Such algorithms are commonly evaluated at the utterance level. However, this approach overlooks the granular acoustic nuances revealed by phoneme-specific analysis, potentially obscuring key insights into their performance. This paper presents an in-depth phoneme-scale evaluation of 3 state-of-the-art multichannel speech enhancement algorithms. These algorithms -- FasNet, MVDR, and Tango -- are extensively evaluated across different noise conditions and spatial setups, employing realistic acoustic simulations with measured room impulse responses, and leveraging diversity offered by multiple microphones in a binaural hearing setup. The study emphasizes the fine-grained phoneme-level analysis, revealing that while some phonemes like plosives are heavily impacted by environmental acoustics and challenging to deal with by the algorithms, others like nasals and sibilants see substantial improvements after enhancement. These investigations demonstrate important improvements in phoneme clarity in noisy conditions, with insights that could drive the development of more personalized and phoneme-aware hearing aid technologies.
Magnetic Penrose process in the magnetized Kerr spacetime

Magnetic Penrose process (MPP) could be highly efficient (efficiency can even exceed $100\%$) for extracting the energy from a Kerr black hole, if it is immersed in a mG order magnetic field. Considering the exact solution of the magnetized Kerr spacetime, here we derive the exact expression of efficiency ($\eta_{\rm MPP}$) for MPP, which is valid for both the Kerr black hole (BH) as well as Kerr superspinar (SS), and also from the weak magnetic field to an ultra-strong magnetic field $(B)$ which can even distort the original Kerr geometry. We show that although the value of $\eta_{\rm MPP}$ increases upto a certain value of ultra-strong magnetic field ($B_p$), it decreases to zero for $B>B_p$, in case of the Kerr BHs. On the other hand, $\eta_{\rm MPP}$ shows the opposite behavior in case of the Kerr SSs. One intriguing feature that emerges is, $\eta_{\rm MPP}$ acquires the maximum value for the Kerr parameter $a_* \approx 0.786$ (unlike $a_*=1$ for the ordinary PP), decreases for $0.7860.786$, and is fully expelled from the extremal Kerr BH due to the gravitational Meissner effect. As a special case of MPP, we also study the ordinary Penrose process (PP) for magnetized Kerr spacetime. We show that MPP for Kerr BHs, Kerr SSs and ordinary PP for Kerr SSs can be superefficient for the astrophysical applications to powering engines in the high-energy sources like active galactic nuclei and quasars, in the weak magnetic fields. Our strong magnetic field result of MPP could be important to the primordial BHs in the early Universe immersed in the primordial magnetic fields, and to the transmuted BHs which are formed by collapsing and/or by merging of the magnetized progenitors. It is almost impossible to extract the energy from a BH (SS) through MPP (PP) in the ultra-strong magnetic fields.
Addressing Data Quality Challenges in Observational Ambulatory Studies: Analysis, Methodologies and Practical Solutions for Wrist-worn Wearable Monitoring

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable sensing technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient follow-up by shifting from subjective, intermittent self-reporting to objective, continuous monitoring. However, collecting and analyzing wearable data presents unique challenges, such as data entry errors, non-wear periods, missing wearable data, and wearable artifacts. We therefore present an in-depth exploration of data analysis challenges tied to wrist-worn wearables and ambulatory label acquisition, using two real-world datasets (i.e., mBrain21 and ETRI lifelog2020). We introduce novel practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized wearable non-wear detection pipeline. Further, we propose a visual analytics approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we investigate the impact of missing wearable data on"window-of-interest"analysis methodologies. Prioritizing transparency and reproducibility, we offer open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for wearable data collection and analysis in chronic disease management.
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues

Code-mixing, the blending of multiple languages within a single conversation, introduces a distinctive challenge, particularly in the context of response generation. Capturing the intricacies of code-mixing proves to be a formidable task, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. In this study, we explore response generation within code-mixed conversations. We introduce a novel approach centered on harnessing the Big Five personality traits acquired in an unsupervised manner from the conversations to bolster the performance of response generation. These inferred personality attributes are seamlessly woven into the fabric of the dialogue context, using a novel fusion mechanism, . It uses an effective two-step attention formulation to fuse the dialogue and personality information. This fusion not only enhances the contextual relevance of generated responses but also elevates the overall performance of the model. Our experimental results, grounded in a dataset comprising of multi-party Hindi-English code-mix conversations, highlight the substantial advantages offered by personality-infused models over their conventional counterparts. This is evident in the increase observed in ROUGE and BLUE scores for the response generation task when the identified personality is seamlessly integrated into the dialogue context. Qualitative assessment for personality identification and response generation aligns well with our quantitative results.
PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across $$\sim $$ ∼ 20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20K
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations

In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their practical implementations. In this overview, we include a detailed proof of the continuous version of the Policy Gradient Theorem, convergence results and a comprehensive discussion of practical algorithms. We compare the most prominent algorithms on continuous control environments and provide insights on the benefits of regularization. All code is available at https://github.com/Matt00n/PolicyGradientsJax.
Constructing a fully homomorphic encryption scheme with the Yoneda Lemma

This paper redefines the foundations of asymmetric cryptography's homomorphic cryptosystems through the application of the Yoneda Lemma. It explicitly illustrates that widely adopted systems, including ElGamal, RSA, Benaloh, Regev's LWE, and NTRUEncrypt, directly derive from the principles of the Yoneda Lemma. This synthesis gives rise to a holistic homomorphic encryption framework named the Yoneda Encryption Scheme. Within this scheme, encryption is elucidated through the bijective maps of the Yoneda Lemma Isomorphism, and decryption seamlessly follows from the naturality of these maps. This unification suggests a conjecture for a unified model theory framework, providing a basis for reasoning about both homomorphic and fully homomorphic encryption (FHE) schemes. As a practical demonstration, the paper introduces an FHE scheme capable of processing arbitrary finite sequences of encrypted multiplications and additions without the need for additional tweaking techniques, such as squashing or bootstrapping. This not only underscores the practical implications of the proposed theoretical advancements but also introduces new possibilities for leveraging model theory and forcing techniques in cryptography to facilitate the design of FHE schemes.