Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal adaptive control with separable drift uncertainty

We consider a problem of stochastic optimal control with separable drift uncertainty in strong formulation on a finite horizon. The drift coefficient of the state $Y^{u}$ is multiplicatively influenced by an unknown random variable $\lambda$, while admissible controls $u$ are required to be adapted to the observation filtration. Choosing a control actively influences the state and information acquisition simultaneously and comes with a learning effect. The problem, initially non-Markovian, is embedded into a higher-dimensional Markovian, full information control problem with control-dependent filtration and noise. To that problem, we apply the stochastic Perron method to characterize the value function as the unique viscosity solution to the HJB equation, explicitly construct $\varepsilon$-optimal controls and show that the values of strong and weak formulations agree. Numerical illustrations show a significant difference between the adaptive control and the certainty equivalence control.
Tree-Structured Shading Decomposition

We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees.
Outer rise seismicity of the subducting Nazca Plate: Plate stress distribution, fault orientation and plate hydration

Subduction of the Nazca plate beneath the South American plate drives frequent and sometimes large magnitude earthquakes. During the past 40 years, significant numbers of outer rise earthquakes have occurred in the offshore regions of Colombia and Chile. In this study, we investigate the distribution of stress due to lithospheric bending and the extent of faults within the subducting plate. To calculate more accurate epicenters and to constrain which earthquakes occurred within the outer rise, we use hypocentroidal decomposition to relocate earthquakes with Global Centroid Moment Tensor (GCMT) solutions occurring after 1976 offshore Colombia and Chile. We determine centroid depths of outer rise earthquakes by inverting teleseismic P-, SH-, and SVwaveforms for earthquakes occurring from 1993 to 2014 with 5.5. In order to further constrain the results of the waveform inversion, we estimate depths by comparing earthquake duration, amplitude, and arrival times for select stations with waveforms with good signal to noise ratios. Our results indicate that tensional earthquakes occur at depths down to 13 km and 24 km depth beneath the surface in the Colombia and Chile regions, respectively. Since faulting within the outer rise can make the plate susceptible to hydration and mantle serpentinization, we therefore infer the extent of possible hydration of the Nazca plate to extend no deeper than the extent of tensional outer rise earthquakes. OUTER RISE SEISMICITY OF THE SUBDUCTING NAZCA PLATE: PLATE STRESS DISTRIBUTION, FAULT ORIENTATION
Undetectable Selfish Mining

Seminal work of Eyal and Sirer (2014) establishes that a strategic Bitcoin miner may strictly profit by deviating from the intended Bitcoin protocol, using a strategy now termed *selfish mining*. More specifically, any miner with $>1/3$ of the total hashrate can earn bitcoin at a faster rate by selfish mining than by following the intended protocol (depending on network conditions, a lower fraction of hashrate may also suffice). One convincing critique of selfish mining in practice is that the presence of a selfish miner is *statistically detectable*: the pattern of orphaned blocks created by the presence of a selfish miner cannot be explained by natural network delays. Therefore, if an attacker chooses to selfish mine, users can detect this, and this may (significantly) negatively impact the value of BTC. So while the attacker may get slightly more bitcoin by selfish mining, these bitcoin may be worth significantly less USD. We develop a selfish mining variant that is provably *statistically undetectable*: the pattern of orphaned blocks is statistically identical to a world with only honest miners but higher network delay. Specifically, we consider a stylized model where honest miners with network delay produce orphaned blocks at each height independently with probability $\beta'$. We propose a selfish mining strategy that instead produces orphaned blocks at each height independently with probability $\beta>\beta'$. We further show that our strategy is strictly profitable for attackers with $38.2\% \ll 50\%$ of the total hashrate (and this holds for all natural orphan rates $\beta'$).
Breaking the k/log k Barrier in Collective Tree Exploration via Tree-Mining

In collective tree exploration, a team of $k$ mobile agents is tasked to go through all edges of an unknown tree as fast as possible. An edge of the tree is revealed to the team when one agent becomes adjacent to that edge. The agents start from the root and all move synchronously along one adjacent edge in each round. Communication between the agents is unrestricted, and they are, therefore, centrally controlled by a single exploration algorithm. The algorithm's guarantee is typically compared to the number of rounds required by the agents to go through all edges if they had known the tree in advance. This quantity is at least $\max\{2n/k,2D\}$ where $n$ is the number of nodes and $D$ is the tree depth. Since the introduction of the problem by [FGKP04], two types of guarantees have emerged: the first takes the form $r(k)(n/k+D)$, where $r(k)$ is called the competitive ratio, and the other takes the form $2n/k+f(k,D)$, where $f(k,D)$ is called the competitive overhead. In this paper, we present the first algorithm with linear-in-$D$ competitive overhead, thereby reconciling both approaches. Specifically, our bound is in $2n/k + O(k^{\log_2(k)-1} D)$ and leads to a competitive ratio in $O(k/\exp(\sqrt{\ln 2\ln k}))$. This is the first improvement over $O(k/\ln k)$ since the introduction of the problem, twenty years ago. Our algorithm is developed for an asynchronous generalization of collective tree exploration (ACTE). It belongs to a broad class of locally-greedy exploration algorithms that we define. We show that the analysis of locally-greedy algorithms can be seen through the lens of a 2-player game that we call the tree-mining game and which could be of independent interest.
Understanding Molecular Abundances in Star-Forming Regions Using Interpretable Machine Learning

Astrochemical modelling of the interstellar medium typically makes use of complex computational codes with parameters whose values can be varied. It is not always clear what the exact nature of the relationship is between these input parameters and the output molecular abundances. In this work, a feature importance analysis is conducted using SHapley Additive exPlanations (SHAP), an interpretable machine learning technique, to identify the most important physical parameters as well as their relationship with each output. The outputs are the abundances of species and ratios of abundances. In order to reduce the time taken for this process, a neural network emulator is trained to model each species' output abundance and this emulator is used to perform the interpretable machine learning. SHAP is then used to further explore the relationship between the physical features and the abundances for the various species and ratios we considered. \ce{H2O} and CO's gas phase abundances are found to strongly depend on the metallicity. \ce{NH3} has a strong temperature dependence, with there being two temperature regimes (<100 K and>100K). By analysing the chemical network, we relate this to the chemical reactions in our network and find the increased temperature results in increased efficiency of destruction pathways. We investigate the HCN/HNC ratio and show that it can be used as a cosmic thermometer, agreeing with the literature. This ratio is also found to be correlated with the metallicity. The HCN/CS ratio serves as a density tracer, but also has three separate temperature-dependence regimes, which are linked to the chemistry of the two molecules.
Investigating boson sector in an extended standard model with U(1) D symmetry

We have investigated the boson sector in an extended standard model (SM) with additional U(1) D symmetry. In the proposed model, the singlet scalar and doublet scalar Higgs are added in addition to the SM-like scalar Higgs. These scalars are also coupled to the gauge boson fields. In this work, we calculate the masses of both gauge and scalar Higgs bosons. Their masses are obtained through spontaneous symmetry breaking using the Higgs fields with non-zero vacuum expectation values. We also study numerically the positivity conditions of the vacuum expectation value of the scalars. In particular, we perform scanning of the parameter space of the potential and study the obtained scalar mass dependence on the parameter of the model.
DNNShifter: An Efficient DNN Pruning System for Edge Computing

Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which has a significant resource footprint. This presents a challenge for resources operating at the extreme edge of the network, such as mobile and embedded devices that have limited computational and memory resources. To address this, models are pruned to create lightweight, more suitable variants for these devices. Existing pruning methods are unable to provide similar quality models compared to their unpruned counterparts without significant time costs and overheads or are limited to offline use cases. Our work rapidly derives suitable model variants while maintaining the accuracy of the original model. The model variants can be swapped quickly when system and network conditions change to match workload demand. This paper presents DNNShifter, an end-to-end DNN training, spatial pruning, and model switching system that addresses the challenges mentioned above. At the heart of DNNShifter is a novel methodology that prunes sparse models using structured pruning. The pruned model variants generated by DNNShifter are smaller in size and thus faster than dense and sparse model predecessors, making them suitable for inference at the edge while retaining near similar accuracy as of the original dense model. DNNShifter generates a portfolio of model variants that can be swiftly interchanged depending on operational conditions. DNNShifter produces pruned model variants up to 93x faster than conventional training methods. Compared to sparse models, the pruned model variants are up to 5.14x smaller and have a 1.67x inference latency speedup, with no compromise to sparse model accuracy. In addition, DNNShifter has up to 11.9x lower overhead for switching models and up to 3.8x lower memory utilisation than existing approaches.
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning

The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial. In general, this task is challenging because modern autonomous systems software relies heavily on black-box artificial intelligence models. Towards this goal, this paper introduces a novel dataset, Rank2Tell, a multi-modal ego-centric dataset for Ranking the importance level and Telling the reason for the importance. Using various close and open-ended visual question answering, the dataset provides dense annotations of various semantic, spatial, temporal, and relational attributes of various important objects in complex traffic scenarios. The dense annotations and unique attributes of the dataset make it a valuable resource for researchers working on visual scene understanding and related fields. Further, we introduce a joint model for joint importance level ranking and natural language captions generation to benchmark our dataset and demonstrate performance with quantitative evaluations.
A Wearable Ultra-Low-Power sEMG-triggered Ultrasound System for Long-term Muscle Activity Monitoring

Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal attenuation, and crosstalk degrade sensing performance. Ultrasound (US) complements sEMG effectively with its higher SNR at high penetration depths. In fact, combining US and sEMG improves the accuracy of muscle dynamic assessment, compared to using only one modality. However, the power envelope of US hardware is considerably higher than that of sEMG, thus inflating energy consumption and reducing the battery life. This work proposes a wearable solution that integrates both modalities and utilizes an EMG-driven wake-up approach to achieve ultra-low power consumption as needed for wearable long-term monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG biosignal acquisition devices to acquire time-synchronized measurements of the short head of the biceps. To minimize power consumption, the US probe is kept in a sleep state when there is no muscle activity. sEMG data are processed on the probe (filtering, envelope extraction and thresholding) to identify muscle activity and generate a trigger to wake-up the US counterpart. The US acquisition starts before muscle fascicles displacement thanks to a triggering time faster than the electromechanical delay (30-100 ms) between the neuromuscular junction stimulation and the muscle contraction. Assuming a muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed approach enables more than 59% energy saving (with a full-system average power consumption of 12.2 mW) as compared to operating both sEMG and US continuously.
Diagnostik und Therapie benigner Knochentumoren der Hand

Gutartige Knochentumoren der Hand sind ein seltenes klinisches Bild. Sie sind häufig asymptomatisch und werden als Zufallsbefund entdeckt. Die korrekte präoperative Diagnose stellt daher den Schlüsselpunkt im therapeutischen Pfad dar.
ADARNet: Deep Learning Predicts Adaptive Mesh Refinement

Deep Learning (DL) algorithms have gained popularity for super-resolution tasks - reconstructing a high-resolution (HR) output from its low-resolution (LR) counterpart. However, current DL approaches, both in computer vision and computational fluid dynamics (CFD), perform spatially uniform super-resolution. Therefore, DL for CFD approaches often over-resolve regions of the LR input that are already accurate at low numerical precision. This hardware over-utilization limits their scalability. To address this limitation, we propose ADARNet, a DL-based adaptive mesh refinement (AMR) framework. ADARNet takes a LR image as input and outputs its non-uniform HR counterpart, predicting HR only in areas that require higher numerical accuracy. As a result, ADARNet predicts the target 1024 × 1024 solution 7 − 28.5 × faster than state-of-the-art DL methods and reduces the memory usage by 4.4 − 7.65 × while maintaining the same level of accuracy. Moreover, unlike traditional AMR solvers that refine the mesh iteratively, ADARNet is a one-shot method that accelerates it by 2.6 − 4.5 ×.
Investigating Dependency Graph Discovery Impact on Task-based MPI+OpenMP Applications Performances

The architecture of supercomputers is evolving to expose massive parallelism. MPI and OpenMP are widely used in application codes on the largest supercomputers in the world. The community primarily focused on composing MPI with OpenMP before its version 3.0 introduced task-based programming. Recent advances in OpenMP task model and its interoperability with MPI enabled fine model composition and seamless support for asynchrony. Yet, OpenMP tasking overheads limit the gain of task-based applications over their historical loop parallelization (parallel for construct). This paper identifies the OpenMP task dependency graph discovery speed as a limiting factor in the performance of task-based applications. We study its impact on intra and inter-node performances over two benchmarks (Cholesky, HPCG) and a proxy-application (LULESH). We evaluate the performance impacts of several discovery optimizations, and introduce a persistent task dependency graph reducing overheads by a factor up to 15 at run-time. We measure 2x speedup over parallel for versions weak scaled to 16K cores, due to improved cache memory use and communication overlap, enabled by task refinement and depth-first scheduling.
To Smooth or not to Smooth: Enhancing Specificity While Maintaining Sensitivity

Traditionally, when conducting voxel- or vertex-wise analyses in neuroimaging studies, it seemed imperative that brain data are convoluted with a Gaussian kernel, a procedure known as “spatial smoothing”. However, we suggest that – under certain conditions – smoothing may be omitted for the benefit of an improved regional specificity. We demonstrate the suitability of this omission by combining high-dimensional spatial registration and threshold-free cluster enhancement (TFCE) in a sample of 754 brains. Our findings revealed that, without smoothing, it is possible to capture brain atrophy within the hippocampal complex while dissociating neighboring areas (cornu ammonis, dentate gyrys, subiculum, and amygdala). In contrast, the traditional smoothing step would result in a single hippocampal cluster (the larger the smoothing kernel, the lower the specificity). Supplemental analyses not only varying the size of the smoothing kernel, but also the size of the sample, the signal-to-noise ratio, as well as the accuracy of the spatial registration confirm that no smoothing (or less smoothing) leads to increased specificity while maintaining sensitivity, at least for small-scale structures (e.g., hippocampus and amygdala). Nevertheless, classic analyses based on smoothed data will continue to provide important insights, especially for large-scale structures (e.g., cortical regions).
Preparing Computer Science Education PhD Students: Our Process

Training the growing number of Computer Science Education (CSEd) PhD students is a pressing concern for our community. To meet the needs of our CSEd PhD students at University of Illinois Urbana-Champaign, we have developed a new course designed to strengthen students’ foundation in relevant fields. Through a collaborative process, we developed a reading list that covers the educational theory and perspectives that most inform our own work, as well as concepts that prepare our graduates to engage with the broader CSEd community.
Composable Workflow for Accelerating Neural Architecture Search Using In Situ Analytics for Protein Classification

Neural architecture search (NAS), which automates the design of neural network (NN) architectures for scientific datasets, requires significant computational resources and time — often on the order of days or weeks of GPU hours and training time. We design the Analytics for Neural Network (A4NN) workflow, a composable workflow that significantly reduces the time and resources required to design accurate and efficient NN architectures. We introduce a parametric fitness prediction strategy and distribute training across multiple accelerators to decrease the aggregated NN training time. A4NN rigorously record neural architecture histories, model states, and metadata to reproduce the search for near-optimal NNs. We demonstrate A4NN’s ability to reduce training time and resource consumption on a dataset generated by an X-ray Free Electron Laser (XFEL) experiment simulation. When deploying A4NN, we decrease training time by up to 37% and epochs required by up to 38%.
Impact of Cache Coherence on the Performance of Shared-Memory based MPI Primitives: A Case Study for Broadcast on Intel Xeon Scalable Processors

Recent processor advances have made feasible HPC nodes with high core counts, capable of hosting tens or even, hundreds of processes. Therefore, designing MPI collective operations at the intra-node level has received significant attention over the past years. Deriving efficient algorithms for modern HPC nodes, with complex internal topologies and memory hierarchies, is challenging. Moreover, the cache coherency protocol, and its impact on performance, further complicate algorithm design for MPI collectives. This latter concern is often only partially addressed. In this work, we demonstrate a particularly challenging performance degradation scenario in the case of shared-memory–based MPI broadcast, on three generations of the Intel Xeon Scalable processor architecture. Based on analysis of hardware-based performance counters, we conclude that the performance degradation observed is attributed to the cache coherency protocol and the multi-socket configuration of the execution platforms examined. We present a number of novel approaches designed to amend this effect, and apply them in a cache coherency aware version of the MPI broadcast implementation. We reduce the overall latency of the broadcast operation by up to 1.5 × and 1.25 × for small and large messages, respectively.
RLB: Reordering-Robust Load Balancing in Lossless Datacenter Networks

Many existing load balancing mechanisms work effectively in lossy datacenter networks (DCNs), but they suffer from serious packet reordering in lossless Ethernet DCNs deployed with the hop-by-hop Priority-based Flow Control (PFC). The key reason is that the prior solutions are not able to correctly and timely perceive PFC triggering when making load balancing decisions. Once the forwarding path pauses transmission due to PFC triggering, the packets allocated on it are blocked, inevitably leading to out-of-order packets and retransmission. In this paper, we present a Reordering-robust Load Balancing (RLB) scheme with PFC prediction in lossless DCNs. At its heart, RLB leverages the derivative of ingress queue length to predict PFC triggering and proactively notifies the upstream switches to choose an appropriate rerouting path or perform packet recirculation to avoid reordering. As a building block for existing load balancing mechanisms, we have integrated RLB into Presto, LetFlow, Hermes and DRILL. The test results show that the RLB-enhanced solutions deliver significant performance by avoiding packet reordering. For example, it reduces the 99th percentile flow completion time (FCT) by up to 58%, 67%, 72% and 54% over Presto, LetFlow, Hermes and DRILL, respectively.