Miracle-dz/UniFAR-FTUs huggingface dataset:
Certified grasping

This paper studies the robustness of grasping in the frictionless plane from a geometric perspective. By treating grasping as a process that shapes the free-space object over time, we define three types of certificates to guarantee success of a grasp: (a) invariance under an initial set, (b) convergence toward a goal grasp, and (c) observability over the final object pose. We develop convex-combinatorial models for each of these certificates, which can be expressed as simple semi-algebraic relations under mild-modeling assumptions, such as point-fingers and frictionless contact. By leveraging these models to synthesize certificates, we optimize certifiable grasps of planar objects composed as a union of convex polygons, using manipulators described as point-fingers. We validate this approach in simulations by grasping random polygons, and with real sensorless grasps of several objects.
A CO-multilayer outer atmosphere for eight evolved stars revealed with VLTI/AMBER

We determine the physical parameters of the outer atmosphere of a sample of eight evolved stars, including the red supergiant α Scorpii, the red giant branch stars α Bootis and γ Crucis, the K giant λ Velorum, the normal M giants BK Virginis and SW Virginis, and the Mira star W Hydrae (in two different luminosity phases) by spatially resolving the stars in the individual carbon monoxide (CO) first overtone lines. We used the Astronomical Multi-BEam combineR (AMBER) instrument at the Very Large Telescope Interferometer (VLTI), in high-resolution mode (λ/Δλ ≈ 12 000) between 2.28 and 2.31 $\, \mu {\rm m}$ in the K band. The maximal angular resolution is 10 mas, obtained using a triplet telescope configuration, with baselines from 7 to 48 m. By using a numerical model of a molecular atmosphere in a spherical shells (MOLsphere), called pampero (an acronym for the ‘physical approach of molecular photospheric ejection at high angular resolution for evolved stars’), we add multiple extended CO layers above the photospheric marcs model at an adequate spatial resolution. We use the differential visibilities and the spectrum to estimate the size (R) of the CO molsphere, its column density (NCO) and temperature (Tmol) distributions along the stellar radius. The combining of the χ2 minimization and a fine grid approach for uncertainty analysis leads to reasonable NCO and Tmol distributions along the stellar radius of the MOLsphere.
LOFAR early-time search for coherent radio emission from GRB 180706A

The nature of the central engines of gamma-ray bursts (GRBs) and the composition of their relativistic jets are still under debate. If the jets are Poynting flux dominated rather than baryon dominated, a coherent radio flare from magnetic reconnection events might be expected with the prompt gamma-ray emission. There are two competing models for the central engines of GRBs; a black hole or a newly formed millisecond magnetar. If the central engine is a magnetar it is predicted to produce coherent radio emission as persistent or flaring activity. In this paper, we present the deepest limits to date for this emission following LOFAR rapid response observations of GRB 180706A. No emission is detected to a 3σ limit of 1.7 mJy beam−1 at 144 MHz in a 2-h LOFAR observation starting 4.5 min after the gamma-ray trigger. A forced source extraction at the position of GRB 180706A provides a marginally positive (1σ) peak flux density of 1.1 ± 0.9 mJy. The data were time sliced into different sets of snapshot durations to search for FRB like emission. No short duration emission was detected at the location of the GRB. We compare these results to theoretical models and discuss the implications of a non-detection.
State sums for some super quantum link invariants

We present state sums for quantum link invariants arising from the representation theory of $U_q(\mathfrak{gl}_{N|M})$. We investigate the case of the $N$-th exterior power of the standard representation of $U_q(\mathfrak{gl}_{N|1})$ and explicit the relation with Kashaev invariants.
The Interplay of Magnetically Dominated Turbulence and Magnetic Reconnection in Producing Nonthermal Particles

Magnetized turbulence and magnetic reconnection are often invoked to explain the nonthermal emission observed from a wide variety of astrophysical sources. By means of fully kinetic 2D and 3D particle-in-cell simulations, we investigate the interplay between turbulence and reconnection in generating nonthermal particles in magnetically dominated (or, equivalently, “relativistic”) pair plasmas. A generic by-product of the turbulence evolution is the generation of a nonthermal particle spectrum with a power-law energy range. The power-law slope p is harder for larger magnetizations and stronger turbulence fluctuations, and it can be as hard as p ≲ 2. The Larmor radius of particles at the high-energy cutoff is comparable to the size l of the largest turbulent eddies. Plasmoid-mediated reconnection, which self-consistently occurs in the turbulent plasma, controls the physics of particle injection. Then, particles are further accelerated by stochastic scattering off turbulent fluctuations. The work done by parallel electric fields—naturally expected in reconnection layers—is responsible for most of the initial energy increase and is proportional to the magnetization σ of the system, while the subsequent energy gain, which dominates the overall energization of high-energy particles, is powered by the perpendicular electric fields of turbulent fluctuations. The two-stage acceleration process leaves an imprint in the particle pitch-angle distribution: low-energy particles are aligned with the field, while the highest-energy particles move preferentially orthogonal to it. The energy diffusion coefficient of stochastic acceleration scales as Dγ ∼ 0. 1σ(c/l)γ2, where γ is the particle Lorentz factor. This results in fast acceleration timescales tacc ∼ (3/σ)l/c. Our findings have important implications for understanding the generation of nonthermal particles in high-energy astrophysical sources.
Privado: Practical and Secure DNN Inference with Enclaves

Cloud providers are extending support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation as well as an increase in adoption. In this paper, we ask a timely question: "Can third-party cloud services use Intel SGX enclaves to provide practical, yet secure DNN Inference-as-a-service? " We first demonstrate that DNN models executing inside enclaves are vulnerable to access pattern based attacks. We show that by simply observing access patterns, an attacker can classify encrypted inputs with 97% and 71% attack accuracy for MNIST and CIFAR10 datasets on models trained to achieve 99% and 79% original accuracy respectively. This motivates the need for PRIVADO, a system we have designed for secure, easy-to-use, and performance efficient inference-as-a-service. PRIVADO is input-oblivious: it transforms any deep learning framework that is written in C/C++ to be free of input-dependent access patterns thus eliminating the leakage. PRIVADO is fully-automated and has a low TCB: with zero developer effort, given an ONNX description of a model, it generates compact and enclave-compatible code which can be deployed on an SGX cloud platform. PRIVADO incurs low performance overhead: we use PRIVADO with Torch framework and show its overhead to be 17.18% on average on 11 different contemporary neural networks.
Topological transition on the conformal manifold

Despite great successes in the study of gapped phases, a comprehensive understanding of the gapless phases and their transitions is still under developments. In this paper, we study a general phenomenon in the space of (1+1)$d$ critical phases with fermionic degrees of freedom described by a continuous family of conformal field theories (CFT), a.k. a. the conformal manifold. Along a one-dimensional locus on the conformal manifold, there can be a transition point, across which the fermionic CFTs on the two sides differ by stacking an invertible fermionic topological order (IFTO), point-by-point along the locus. At every point on the conformal manifold, the order and disorder operators have power-law two-point functions, but their critical exponents cross over with each other at the transition point, where stacking the IFTO leaves the fermionic CFT unchanged. We call this continuous transition on the fermionic conformal manifold a topological transition. By gauging the fermion parity, the IFTO stacking becomes a Kramers-Wannier duality between the corresponding bosonic CFTs. Both the IFTO stacking and the Kramers-Wannier duality are induced by the electromagnetic duality of the (2+1)$d$ $\mathbb{Z}_2$ topological order. We provide several examples of topological transitions, including the familiar Luttinger model of spinless fermions (i.e. the $c=1$ massless Dirac fermion with the Thirring interaction), and a new class of $c=2$ examples describing $U(1)\times SU(2)$-protected gapless phases.
HinDom: A Robust Malicious Domain Detection System based on Heterogeneous Information Network with Transductive Classification

Domain name system (DNS) is a crucial part of the Internet, yet has been widely exploited by cyber attackers. Apart from making static methods like blacklists or sinkholes infeasible, some weasel attackers can even bypass detection systems with machine learning based classifiers. As a solution to this problem, we propose a robust domain detection system named HinDom. Instead of relying on manually selected features, HinDom models the DNS scene as a Heterogeneous Information Network (HIN) consist of clients, domains, IP addresses and their diverse relationships. Besides, the metapath-based transductive classification method enables HinDom to detect malicious domains with only a small fraction of labeled samples. So far as we know, this is the first work to apply HIN in DNS analysis. We build a prototype of HinDom and evaluate it in CERNET2 and TUNET. The results reveal that HinDom is accurate, robust and can identify previously unknown malicious domains.
1.2 – Green Tea as a Cancer Preventive

1 Abstract Green tea is now an acknowledged cancer preventive in Japan and will possibly soon be recognized as such in other countries. Initially, we found that (—)- epigallocatechin gallate (EGCG), the main constituent of green tea inhibited tumor promotion on mouse skin in a two-stage carcinogenesis experiment. Numerous additional studies revealed the anticarcinogenic effects of EGCG and green tea on various organs in rodent experiments. This paper reviews the unique role of green tea in cancer chemoprevention, its anticarcinogenic effects and other preventive activities, bioavailability of tea polyphenols and epidemiological studies with green tea. Of particular interest are studies which showed that daily consumption of green tea delayed clinical onset of various cancers and led to more hopeful prognoses for breast cancer patients in Stage I and II following treatment. Based on these results, I propose two stages of cancer prevention with green tea: prevention before cancer onset, and following cancer treatment. Since green tea is a common beverage, the knowledge that it inhibits cancer will be a great comfort to, especially, aging folk concerned with cancer prevention and any high risk population.
Maximum amplification of enstrophy in three-dimensional Navier–Stokes flows

This investigation concerns a systematic search for potentially singular behaviour in three-dimensional (3-D) Navier–Stokes flows. Enstrophy serves as a convenient indicator of the regularity of solutions to the Navier Stokes system – as long as this quantity remains finite, the solutions are guaranteed to be smooth and satisfy the equations in the classical (pointwise) sense. However, there are no estimates available with finite a priori bounds on the growth of enstrophy and, hence, the regularity problem for the 3-D Navier–Stokes system remains open. In order to quantify the maximum possible growth of enstrophy, we consider a family of optimization problems in which initial conditions with prescribed enstrophy ${\mathcal{E}}_{0}$ are sought such that the enstrophy in the resulting Navier–Stokes flow is maximized at some time $T$. Such problems are solved computationally using a large-scale adjoint-based gradient approach derived in the continuous setting. By solving these problems for a broad range of values of ${\mathcal{E}}_{0}$ and $T$, we demonstrate that the maximum growth of enstrophy is in fact finite and scales in proportion to ${\mathcal{E}}_{0}^{3/2}$ as ${\mathcal{E}}_{0}$ becomes large. Thus, in such a worst-case scenario the enstrophy still remains bounded for all times and there is no evidence for formation of singularity in finite time. We also analyse properties of the Navier–Stokes flows leading to the extreme enstrophy values and show that this behaviour is realized by a series of vortex reconnection events.
On the road to per cent accuracy – III. Non-linear reaction of the matter power spectrum to massive neutrinos

We analytically model the non-linear effects induced by massive neutrinos on the total matter power spectrum using the halo model reaction framework of Cataneo et al. In this approach, the halo model is used to determine the relative change to the matter power spectrum caused by new physics beyond the concordance cosmology. Using standard fitting functions for the halo abundance and the halo mass–concentration relation, the total matter power spectrum in the presence of massive neutrinos is predicted to per cent-level accuracy, out to $k=10 \,{ h}\,{\rm Mpc}^{-1}$. We find that refining the prescriptions for the halo properties using N-body simulations improves the recovered accuracy to better than 1 per cent. This paper serves as another demonstration for how the halo model reaction framework, in combination with a single suite of standard Λ cold dark matter (ΛCDM) simulations, can recover per cent-level accurate predictions for beyond ΛCDM matter power spectra, well into the non-linear regime.
Analysis of the disc components of our galaxy via kinematic and spectroscopic procedures

Abstract We used the spectroscopic and astrometric data provided from the GALactic Archaeology with HERMES (GALAH) Data Release (DR2) and Gaia DR2, respectively, for a large sample of stars to investigate the behaviour of the [ $\alpha$ /Fe] abundances via two procedures, that is, kinematically and spectroscopically. With the kinematical procedure, we investigated the distribution of the [ $\alpha$ /Fe] abundances into the high-/low-probability thin disc, and high-/low-probability thick-disc populations in terms of total space velocity, [Fe/H] abundance, and age. The high-probability thin-disc stars dominate in all sub-intervals of [ $\alpha$ /Fe], including the rich ones: [ $\alpha$ /Fe] $\,>\,0.3$ dex, where the high-probability thick-disc stars are expected to dominate. This result can be explained by the limiting apparent magnitude of the GALAH DR2 ( $V \lt 14$ mag) and intermediate galactic latitude of the star sample. Stars in the four populations share equivalent [ $\alpha$ /Fe] and [Fe/H] abundances, total space velocities, and ages. Hence, none of these parameters can be used alone for separation of a sample of stars into different populations. High-probability thin-disc stars with abundance $-1.3 \lt {\rm[Fe/H]}\leq -0.5$ dex and age $9 \lt \tau\leq13$ Gyr are assumed to have different birth places relative to the metal-rich and younger ones. With the spectroscopic procedure, we separated the sample stars into $\alpha$ -rich and $\alpha$ -poor categories by means of their ages as well as their [ $\alpha$ /Fe] and [Fe/H] abundances. Stars older than 8 Gyr are richer in [ $\alpha$ /Fe] than the younger ones. We could estimate the abundance [ $\alpha$ /Fe] = 0.14 dex as the boundary separating the $\alpha$ -rich and $\alpha$ -poor sub-samples in the [ $\alpha$ /Fe] $\,\times\,$ [Fe/H] plane.
Statistical learnability of nuclear masses

After more than 80 years from the seminal work of Weizsacker and the liquid drop model of the atomic nucleus, deviations from experiments of mass models ($\sim$ MeV) are orders of magnitude larger than experimental errors ($\lesssim$ keV). Predicting the mass of atomic nuclei with precision is extremely challenging. This is due to the non--trivial many--body interplay of protons and neutrons in nuclei, and the complex nature of the nuclear strong force. Statistical theory of learning will be used to provide bounds to the prediction errors of model trained with a finite data set. These bounds are validated with neural network calculations, and compared with state of the art mass models. Therefore, it will be argued that the nuclear structure models investigating ground state properties explore a system on the limit of the knowledgeable, as defined by the statistical theory of learning.
Februus: Input Purification Defence Against Trojan Attacks on Deep Neural Network Systems

We propose Februus; a novel idea to neutralize insidous and highly potent Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model's decision to a target prediction---a target determined by and only known to the attacker. Februus sanitizes the incoming input by devising an extraction method to surgically remove the potential trigger artifacts and use an inpainting method we propose for restoring the input for the classification task. Through extensive experiments, we demonstrate the efficacy of Februus against backdoor attacks, including advance variants and adaptive attacks, across vision applications. Notably, in contrast to existing approaches, our approach removes the need for ground-truth labelled data or anomaly detection methods for Trojan detection or retraining a model or prior knowledge of an attack. We achieve dramatic reductions in the attack success rates; from 100\% to 0.25\% (in the worst case) with no loss of performance for benign or trojaned inputs sanitized by Februus. To the best of our knowledge, this is the first backdoor defense method for operation in black-box setting capable of sanitizing trojaned inputs without requiring costly labelled data.
Development of 13-μm cutoff HgCdTe detector arrays for astronomy

Abstract. Building on the successful development of the 10-μm mercury cadmium telluride (HgCdTe) detector arrays for the proposed NEOCam mission, the University of Rochester Infrared Detector team and Teledyne Imaging Systems are working together to extend the cutoff wavelength of HgCdTe detector arrays initially to 13 μm, with the ultimate goal of developing 15-μm HgCdTe detector arrays for space and ground-based astronomy. The advantage of HgCdTe detector arrays is that they can operate at higher temperatures than the currently used arsenic doped silicon detector arrays at the longer wavelengths. Our infrared detector team at the University of Rochester has received and tested four 13-μm detector arrays from Teledyne Imaging Systems with three different pixel designs, two of which are meant to reduce quantum tunneling dark current. The pixel design of one of these arrays has mitigated the effects of quantum tunneling dark currents for which we have been able to achieve, at a temperature of 28 K and applied bias of 350 mV, a well depth of at least 75ke − for 90% of the pixels with a median dark current of 1.8e − / s. These arrays have demonstrated encouraging results as we move forward to extending the cutoff wavelength to 15 μm.
On the Shafarevich group of restricted ramification extensions of number fields in the tame case

Let $K$ be a number field and $S$ a finite set of places of $K$. We study the kernels $\Sha_S$ of maps $H^2(G_S,\fq_p) \rightarrow \oplus_{v\in S} H^2(\G_v,\fq_p)$. There is a natural injection $\Sha_S \hookrightarrow \CyB_S$, into the dual $\CyB_S$ of a certain readily computable Kummer group $V_S$, which is always an isomorphism in the wild case. The tame case is much more mysterious. Our main result is that given a finite $X$ coprime to $p$, there exists a finite set of places $S$ coprime to $p$ such that $\Sha_{S\cup X} \stackrel{\simeq}{\hookrightarrow} \CyB_{S\cup X} \stackrel{\simeq}{\twoheadleftarrow} \CyB_X \hookleftarrow \Sha_X$. In particular, we show that in the tame case $\Sha_Y$ can {\it increase} with increasing $Y$. This is in contrast with the wild case where $\Sha_Y$ is nonincreasing in size with increasing $Y$.
Sampling Conditionally on a Rare Event via Generalized Splitting

We propose and analyze a generalized splitting method to sample approximately from a distribution conditional on the occurrence of a rare event. This has important applications in a variety of contexts in operations research, engineering, and computational statistics. The method uses independent trials starting from a single particle. We exploit this independence to obtain asymptotic and non-asymptotic bounds on the total variation error of the sampler. Our main finding is that the approximation error depends crucially on the relative variability of the number of points produced by the splitting algorithm in one run, and that this relative variability can be readily estimated via simulation. We illustrate the relevance of the proposed method on an application in which one needs to sample (approximately) from an intractable posterior density in Bayesian inference.
SRIM and SCRIM Factors of xn+1 over Finite Fields and Their Applications

Self-Reciprocal Irreducible Monic (SRIM) and Self-Conjugate-Reciprocal Irreducible Monic (SCRIM) factors of [Formula: see text] over finite fields have become of interest due to their rich algebraic structures and wide applications. In this paper, these notions are extended to factors of [Formula: see text] over finite fields. Characterization and enumeration of SRIM and SCRIM factors of [Formula: see text] over finite fields are established. Simplification and recessive formulas for the number of such factors are given. Finally, applications in the study of complementary negacyclic codes are discussed.
Permutation Recovery from Multiple Measurement Vectors in Unlabeled Sensing

In Unlabeled Sensing, one observes a set of linear measurements of an underlying signal with incomplete or missing information about their ordering, which can be modeled in terms of an unknown permutation. In this paper, we study the case of multiple noisy measurement vectors (MMVs) resulting from a common permutation and investigate to what extent the number of MMVs m facilitates permutation recovery by "borrowing strength". We provide an affirmative answer for an oracle setting in which the matrix of signals is known by establishing matching upper and lower bounds on the required Signal-to-Noise Ratio (SNR), which – as distinguished from the case of a single measurement vector – involves a dependence on the stable rank of the matrix of signals. Specifically, a larger stable rank significantly reduces the required average SNR which can drop from nΩ(1) for m = 1 to Ω(log n) for m = Ω(log n), where n denotes the number of measurements per MMV. Numerical results are well-aligned with our theoretical findings.