Miracle-dz/UniFAR-FTUs huggingface dataset:
A silicon-carbide micro-capillary pumped loop for cooling high power devices

As more high-power wide-bandgap devices are being utilized, the thermal management issues associated with these devices need to be resolved. High power devices dissipate excessive heat that must be cooled, but traditional cooling methods are insufficient to provide such a cooling means. Although using SiC allows higher operating temperatures, an upper temperature threshold still exists for high frequency devices. The package under study consists of a SiC high frequency power device whose junction temperature must not exceed a specified temperature, such as 150/spl deg/C, 175/spl deg/C, or 200/spl deg/C, set by the particular device technology. The technical goal is to develop an innovative cooling concept providing less than 100/spl deg/C temperature rise relative to the base plate. In the baseline configuration, a high power SiC device is mounted onto a shim or thermal spacer that is then mounted onto a cold plate. This paper evaluates a SiC microcapillary pumped loop thermal management system that is incorporated into the shim of the device, taking advantage of phase-change to increase the thermal conductivity of the system. The results of the modeling, fabrication, and testing of the thermal management system are discussed.
Implementing Rowhammer Memory Corruption in the gem5 Simulator

Modern computer memories have shown to have reliability issues. The main memory is the target of a security threat called Rowhammer, which causes bit flips in adjacent victim cells of aggressor rows. Numerous countermeasures have been proposed, some of the most efficient ones relying on memory controller modifications, which make them non-integrable in existing systems. These solutions have to be effective against attacks on current and future architectures and technology nodes. In order to prove the efficiency of such mitigation techniques, we have to use simulation platforms. Unfortunately, existing architecture simulators do not provide any implementation of unintended memory modifications like bit-flips. Integrating memory corruption into architecture simulators would allow the construction of attacks and mitigations for current and future computers, using feedback from the simulator. In this paper, we propose an implementation of the Rowhammer effect in the gem5 architecture simulator, demonstrate its capabilities and state its limitations.
Reducing Tactile Sim2Real Domain Gaps via Deep Texture Generation Networks

Recently simulation methods have been developed for optical tactile sensors to enable the Sim2Real learning, i.e., first training models in simulation before deploying them on a real robot. However, some artefacts in real objects are unpredictable, such as imperfections caused by fabrication processes, or scratches by natural wear and tear, and thus cannot be represented in the simulation, resulting in a significant gap between the simulated and real tactile images. To address this Sim2Real gap, we propose a novel texture generation network to map the simulated images into photorealistic tactile images that resemble a real sensor contacting a real imperfect object. Each simulated tactile image is first divided into two types of regions: areas that are in contact with the object and areas that are not. The former is applied with generated textures learned from real textures in the real tactile images, whereas the latter maintains its appearance as when the sensor is not in contact with any object. This makes sure that the artefacts are only applied to deformed regions of the sensor. Our extensive experiments show that the proposed texture generation network can generate realistic artefacts on the deformed regions of the sensor, while avoiding leaking the textures into areas of no contact. Quantitative experiments further reveal that when using the adapted images generated by our proposed network for a Sim2Real classification task, the drop in accuracy caused by the Sim2Real gap is reduced from 38.43% to merely 0.81%. As such, this work has potential to accelerate the Sim2Real learning for robotic tasks requiring tactile sensing.
Explicit Abelian Lifts and Quantum LDPC Codes

For an abelian group $H$ acting on the set $[\ell]$, an $(H,\ell)$-lift of a graph $G_0$ is a graph obtained by replacing each vertex by $\ell$ copies, and each edge by a matching corresponding to the action of an element of $H$. In this work, we show the following explicit constructions of expanders obtained via abelian lifts. For every (transitive) abelian group $H \leqslant \text{Sym}(\ell)$, constant degree $d \ge 3$ and $\epsilon>0$, we construct explicit $d$-regular expander graphs $G$ obtained from an $(H,\ell)$-lift of a (suitable) base $n$-vertex expander $G_0$ with the following parameters: (i) $\lambda(G) \le 2\sqrt{d-1} + \epsilon$, for any lift size $\ell \le 2^{n^{\delta}}$ where $\delta=\delta(d,\epsilon)$, (ii) $\lambda(G) \le \epsilon \cdot d$, for any lift size $\ell \le 2^{n^{\delta_0}}$ for a fixed $\delta_0>0$, when $d \ge d_0(\epsilon)$, or (iii) $\lambda(G) \le \widetilde{O}(\sqrt{d})$, for lift size ``exactly'' $\ell = 2^{\Theta(n)}$. As corollaries, we obtain explicit quantum lifted product codes of Panteleev and Kalachev of almost linear distance (and also in a wide range of parameters) and explicit classical quasi-cyclic LDPC codes with wide range of circulant sizes. Items $(i)$ and $(ii)$ above are obtained by extending the techniques of Mohanty, O'Donnell and Paredes [STOC 2020] for $2$-lifts to much larger abelian lift sizes (as a byproduct simplifying their construction). This is done by providing a new encoding of special walks arising in the trace power method, carefully"compressing'"depth-first search traversals. Result $(iii)$ is via a simpler proof of Agarwal et al. [SIAM J. Discrete Math 2019] at the expense of polylog factors in the expansion.
Images of fractional Brownian motion with deterministic drift: Positive Lebesgue measure and non-empty interior

Abstract Let $B^{H}$ be a fractional Brownian motion in $\mathbb{R}^{d}$ of Hurst index $H\in\left(0,1\right)$ , $f\;:\;\left[0,1\right]\longrightarrow\mathbb{R}^{d}$ a Borel function and $A\subset\left[0,1\right]$ a Borel set. We provide sufficient conditions for the image $(B^{H}+f)(A)$ to have a positive Lebesgue measure or to have a non-empty interior. This is done through the study of the properties of the density of the occupation measure of $(B^{H}+f)$ . Precisely, we prove that if the parabolic Hausdorff dimension of the graph of f is greater than Hd, then the density is a square integrable function. If, on the other hand, the Hausdorff dimension of A is greater than Hd, then it even admits a continuous version. This allows us to establish the result already cited.
Tensor-network simulation of the strong-coupling $U(N)$ model

We apply tensor network methods to study the strong-coupling U (N) model in its dimer formulation. In three and four dimensions, we investigate the chiral condensate as a function of the quark mass and the degree of the symmetry group, and find good agreement with Monte Carlo simulations. Particularly interesting is the study of chiral symmetry breaking as a function of the mass and the volume, which clearly shows that this symmetry is spontaneously broken in the limit of infinite volume and zero mass.
A low power X‐band CMOS differential VCO

This article proposes a 12 GHz low power differential voltage‐controlled oscillator (VCO) based on single‐ended Colpitts oscillators. The differential VCO uses two single‐ended Colpitts VCOs coupled by a pair of pMOS cross‐coupled transistors and a transformer inductor. The differential VCO was fabricated in the TSMC 0.18‐nm CMOS process, it oscillates from 11.46 GHz to 12.45 GHz, and the power consumption is 2.76 mW at the supply voltage of 1.2 V. The measured phase noise at 1‐MHz offset is g110.95 dBc/Hz at the oscillation frequency of 12.42 GHz. The power consumption of the VCO is 2.3 mA when the supplied voltage is 1.2 V. The figure of merit of the VCO is −188.43 dBc/Hz. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1389–1392, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23388
Dynamics around Non-Spherical Symmetric Bodies: I. The case of a spherical body with mass anomaly

The space missions designed to visit small bodies of the Solar System boosted the study of the dynamics around non-spherical bodies. In this vein, we study the dynamics around a class of objects classified by us as Non-Spherical Symmetric Bodies, including contact binaries, triaxial ellipsoids, spherical bodies with a mass anomaly, among others. In the current work, we address the results for a body with a mass anomaly. We apply the pendulum model to obtain the width of the spin-orbit resonances raised by non-asymmetric gravitational terms of the central object. The Poincaré surface of section technique is adopted to confront our analytical results and to study the system’s dynamics by varying the parameters of the central object. We verify the existence of two distinct regions around an object with a mass anomaly: a chaotic inner region that extends beyond the corotation radius and a stable outer region. In the latter, we identify structures remarkably similar to those of the classical restrict and planar 3-body problem in the Poincaré surface of sections, including asymmetric periodic orbits associated with 1:1+p resonances. We apply our results to a Chariklo with a mass anomaly, obtaining that Chariklo rings are probably related to first kind periodic orbits and not with 1:3 spin-orbit resonance, as proposed in the literature. We believe that our work presents the first tools for studying mass anomaly systems.
Scheduling to Learn In An Unsupervised Online Streaming Model

An unsupervised online streaming model is considered where samples arrive in an online fashion over $T$ slots. There are $M$ classifiers, whose confusion matrices are unknown a priori. In each slot, at most one sample can be labeled by any classifier. The accuracy of a sample is a function of the set of labels obtained for it from various classifiers. The utility of a sample is a scalar multiple of its accuracy minus the response time (difference of the departure slot and the arrival slot), where the departure slot is also decided by the algorithm. Since each classifier can label at most one sample per slot, there is a tradeoff between obtaining a larger set of labels for a particular sample to improve its accuracy, and its response time. The problem of maximizing the sum of the utilities of all samples is considered, where learning the confusion matrices, sample-classifier matching assignment, and sample departure slot decisions depend on each other. The proposed algorithm first learns the confusion matrices, and then uses a greedy algorithm for sample-classifier matching. A sample departs once its incremental utility turns non-positive. We show that the competitive ratio of the proposed algorithm is $\frac{1}{2}-{\mathcal O}\left(\frac{\log T}{T}\right)$.
Improving the Reliability of Network Intrusion Detection Systems Through Dataset Integration

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. We also propose a new dataset, called UNK22. It is built from three of the most well-known network datasets (UGR’16, USNW-NB15 and NLS-KDD), each one gathered from its own network environment, with different features and classes, by using a data aggregation approach present in R-NIDS . Therefore, R-NIDS targets the design of more robust models that generalize better than traditional approaches. Following R-NIDS, in this work we propose to build two well-known ML models for reliable predictions thanks to the meaningful information integrated in UNK22. The results show how these models benefit from the proposed approach, being able to generalize better when using UNK22 in the training process, in comparison to individually using the datasets composing it. Furthermore, these results are carefully analyzed with statistical tools that provide high confidence on our conclusions. Finally, the proposed solution is feasible to be deployed in network production environments, not usually taken into account in the literature.
An integrated identification and control design methodology for multivariable process system applications

We present a way to take advantage of the favorable asymptotic properties of ARX estimators to develop an integrated methodology for identification and controller design for multivariable process plants. This method relies on well-established numerical tools and builds on an engineer's existing process and statistical intuition. Specifically, the ARX estimate serves as a suitable intermediate model for the design and analysis of MIMO process control systems. Guidelines for the design of pseudo-random binary sequence signals that take advantage of the engineer's prior knowledge of the process time constants are presented. Control-relevant model reduction is performed on elements of the ARX model to obtain low-order models conforming to the IMC-PID tuning rules. A simple analysis technique is used to assess stability of the decentralized and decoupled strategies. These techniques and full multivariable control are applied to the Shell heavy oil fractionator problem and the Weischedel-McAvoy distillation column model, respectively.
Variability of Stride Characteristics and Joint Coordination among Individuals with Unilateral Patellofemoral Pain

The purpose of this investigation was to determine whether individuals with patellofemoral pain (PFP) display a reduction in intralimb joint coordination variability compared to nonimpaired persons. In addition, it was hypothesized that the variability of the stride characteristics would be similar between groups. Eight individuals with unilateral PFP and 8 nonimpaired participants ran on a treadmill at a fixed (2.68 m·s–1) and preferred speed while stride characteristics and 3-D kinematics of the bilateral lower extremities were recorded. Intralimb coordination variability was measured using a vector coding technique applied to relative motion plots of various joint couplings. The PFP group displayed greater stride length variability during running at the preferred speed. However, this was not the case during running at the fixed speed. When averaging across the entire stride cycle, coordination variability for all joint couplings was consistent between the two groups. However, further analysis about hee.
Differential attenuation in star-forming galaxies at 0.3 ≲ z ≲ 1.5 in the SHARDS/CANDELS field

We use a sample of 706 galaxies, selected as [OII]λ3727 ([OII]) emitters in the Survey for High-z Absorption Red and Dead Sources (SHARDS) on the CANDELS/GOODS-N field, to study the differential attenuation of the nebular emission with respect to the stellar continuum. The sample includes only galaxies with a counterpart in the infrared and log10(M*/M⊙) > 9, over the redshift interval 0.3 ≲ z ≲ 1.5. Our methodology consists in the comparison of the star formation rates inferred from [OII] and Hα emission lines with a robust quantification of the total star-forming activity (SFRTOT) that is independently estimated based on both infrared and ultraviolet (UV) luminosities. We obtain f = E(B − V)stellar/E(B − V)nebular = 0.69$^{0.71}_{0.69}$ and 0.55$^{0.56}_{0.53}$ for [OII] and Hα, respectively. Our resulting f-factors display a significant positive correlation with the UV attenuation and shallower or not-significant trends with the stellar mass, the SFRTOT, the distance to the main sequence, and the redshift. Finally, our results favour an average nebular attenuation curve similar in shape to the typical dust curve of local starbursts.
ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

We present ROCA 11The code is made available at https://github.com/cangurneli/ROCA. , a novel end-to-end approach that re-trieves and aligns 3D CAD models from a shape database to a single input image. This enables 3D perception of an ob-served scene from a 2D RGB observation, characterized as a lightweight, compact, clean CAD representation. Core to our approach is our differentiable alignment optimization based on dense 2D-3D object correspondences and Pro-crustes alignment. ROCA can thus provide a robust CAD alignment while simultaneously informing CAD retrieval by leveraging the 2D-3D correspondences to learn geometri-cally similar CAD models. Experiments on challenging, real-world imagery from ScanNet show that ROCA signif-icantly improves on state of the art, from 9.5% to 17.6% in retrieval-aware CAD alignment accuracy.
Multitask Finetuning for Improving Neural Machine Translation in Indian Languages

Transformer based language models have led to impressive results across all domains in Natural Language Processing. Pretraining these models on language modeling tasks and finetuning them on downstream tasks such as Text Classification, Question Answering and Neural Machine Translation has consistently shown exemplary results. In this work, we propose a Multitask Finetuning methodology which combines the Bilingual Machine Translation task with an auxiliary Causal Language Modeling task to improve performance on the former task on Indian Languages. We conduct an empirical study on three language pairs, Marathi-Hindi, Marathi-English and Hindi-English, where we compare the multitask finetuning approach to the standard finetuning approach, for which we use the mBART50 model. Our study indicates that the multitask finetuning method could be a better technique than standard finetuning, and could improve Bilingual Machine Translation across language pairs.
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing

Deep neural networks (DNNs) often accept high-dimensional media data (e.g., photos, text, and audio) and understand their perceptual content (e.g., a cat). To test DNNs, diverse inputs are needed to trigger mis-predictions. Some preliminary works use byte-level mutations or domain-specific filters (e.g., foggy), whose enabled mutations may be limited and likely error-prone. SOTA works employ deep generative models to generate (infinite) inputs. Also, to keep the mutated inputs perceptually valid (e.g., a cat remains a"cat"after mutation), existing efforts rely on imprecise and less generalizable heuristics. This study revisits two key objectives in media input mutation - perception diversity (DIV) and validity (VAL) - in a rigorous manner based on manifold, a well-developed theory capturing perceptions of high-dimensional media data in a low-dimensional space. We show important results that DIV and VAL inextricably bound each other, and prove that SOTA generative model-based methods fundamentally fail to mutate real-world media data (either sacrificing DIV or VAL). In contrast, we discuss the feasibility of mutating real-world media data with provably high DIV and VAL based on manifold. We concretize the technical solution of mutating media data of various formats (images, audios, text) via a unified manner based on manifold. Specifically, when media data are projected into a low-dimensional manifold, the data can be mutated by walking on the manifold with certain directions and step sizes. When contrasted with the input data, the mutated data exhibit encouraging DIV in the perceptual traits (e.g., lying vs. standing dog) while retaining reasonably high VAL (i.e., a dog remains a dog). We implement our techniques in DEEPWALK for testing DNNs. DEEPWALK outperforms prior methods in testing comprehensiveness and can find more error-triggering inputs with higher quality.
D3Net: A Speaker-Listener Architecture for Semi-supervised Dense Captioning and Visual Grounding in RGB-D Scans

Recent studies on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-language data causes overﬁtting issues for 3D visual grounding and 3D dense captioning methods. Also, how to discriminatively describe objects in complex 3D environments is not fully studied yet. To address these challenges, we present D 3 Net, an end-to-end neural speaker-listener architecture that can d etect, d escribe and d iscriminate. Our D 3 Net uniﬁes dense captioning and visual grounding in 3D in a self-critical manner. This self-critical property of D 3 Net also introduces discriminability during object caption generation and enables semi-supervised training on ScanNet data with partially annotated descriptions. Our method outperforms SOTA meth-ods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense captioning method by a signiﬁcant margin (
Semantic Map Injected GAN Training for Image-to-Image Translation

Image-to-image translation is the recent trend to transform images from one domain to another domain using generative adversarial network (GAN). The existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Lets refer the original training as the training for the translation of input image into target domain. The injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. The semantic map is only utilized at the training time and is not required at the test time. The experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID and KID scores after injecting semantic training as compared to original training.
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.