Miracle-dz/UniFAR-FTUs huggingface dataset:
Getting Users Smart Quick about Security: Results from 90 Minutes of Using a Persuasive Toolkit for Facilitating Information Security Problem Solving by Non-Professionals

—There is a conflict between the need for security compliance by users and the fact that commonly they cannot afford to dedicate much of their time and energy to that security. A balanced level of user engagement in security is difficult to achieve due to difference of priorities between the business perspective and the security perspective. We sought to find a way to engage users minimally, yet efficiently, so that they would both improve their security awareness and provide necessary feedback for improvement purposes to security designers. We have developed a persuasive software toolkit to engage users in structured discussions about security vulnerabilities in their company and potential interventions addressing these. In the toolkit we have adapted and integrated an established framework from conventional crime prevention. In the research reported here we examine how non-professionals perceived security problems through a short-term use of the toolkit. We present perceptions from a pilot lab study in which randomly recruited participants had to analyze a crafted insider threat problem using the toolkit. Results demonstrate that study participants were able to successfully identify causes, propose interventions and engage in providing feedback on proposed interventions. Subsequent interviews show that participants have developed greater awareness of information security issues and the framework to address these, which in a real setting would lead ultimately to significant benefits for the organization. These results indicate that when well-structured such short-term engagement is sufficient for users to meaningfully take part in complex security discussions and develop in-depth understanding of theoretical principles of security.
ProcessorFuzz: Guiding Processor Fuzzing using Control and Status Registers

As the complexity of modern processors has increased over the years, developing effective verification strategies to identify bugs prior to manufacturing has become critical. Undiscovered micro-architectural bugs in processors can manifest as severe security vulnerabilities in the form of side channels, functional bugs, etc. Inspired by software fuzzing, a technique commonly used for software testing, multiple recent works use hardware fuzzing for the verification of Register-Transfer Level (RTL) designs. However, these works suffer from several limitations such as lack of support for widely-used Hardware Description Languages (HDLs) and misleading coverage-signals that misidentify"interesting"inputs. Towards overcoming these shortcomings, we present ProcessorFuzz, a processor fuzzer that guides the fuzzer with a novel CSR-transition coverage metric. ProcessorFuzz monitors the transitions in Control and Status Registers (CSRs) as CSRs are in charge of controlling and holding the state of the processor. Therefore, transitions in CSRs indicate a new processor state, and guiding the fuzzer based on this feedback enables ProcessorFuzz to explore new processor states. ProcessorFuzz is agnostic to the HDL and does not require any instrumentation in the processor design. Thus, it supports a wide range of RTL designs written in different hardware languages. We evaluated ProcessorFuzz with three real-world open-source processors -- Rocket, BOOM, and BlackParrot. ProcessorFuzz triggered a set of ground-truth bugs 1.23$\times$ faster (on average) than DIFUZZRTL. Moreover, our experiments exposed 8 new bugs across the three RISC-V cores and one new bug in a reference model. All nine bugs were confirmed by the developers of the corresponding projects.
Symphony: Cosmological Zoom-in Simulation Suites over Four Decades of Host Halo Mass

We present Symphony, a compilation of 262 cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from 1011–1015 M ⊙. This compilation includes three existing simulation suites at the cluster and Milky Way–mass scales, and two new suites: 39 Large Magellanic Cloud-mass (1011 M ⊙) and 49 strong-lens-analog (1013 M ⊙) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of ≈3 × 10−7 times the host virial mass and a Plummer-equivalent gravitational softening length of ≈9 × 10−4 times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are ≈50% higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts’ underlying dark matter distributions. We compare our results to the semianalytic model Galacticus, which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We use UniverseMachine to model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available at http://web.stanford. edu/group/gfc/symphony.
MalNet: A Large-Scale Image Database of Malicious Software

Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation. These binary images are fast to generate, require no feature engineering, and are resilient to popular obfuscation methods. Significant research has been conducted in this area, however, it has been restricted to small-scale or private datasets that only a few industry labs and research teams have access to. This lack of availability hinders examination of existing work, development of new research, and dissemination of ideas. We release MalNet-Image, the largest public cybersecurity image database, offering 24x more images and 70x more classes than existing databases (available at https://mal-net.org). MalNet-Image contains over 1.2 million malware images-across 47 types and 696 families---democratizing image-based malware capabilities by enabling researchers and practitioners to evaluate techniques that were previously reported in propriety settings. We report the first million-scale malware detection results on binary images. MalNet-Image unlocks new and unique opportunities to advance the frontiers of machine learning, enabling new research directions into vision-based cyber defenses, multi-class imbalanced classification, and interpretable security.
Tailoring Structure‐Borne Sound through Bandgap Engineering in Phononic Crystals and Metamaterials: A Comprehensive Review

In solid state physics, a bandgap (BG) refers to a range of energies where no electronic states can exist. This concept was extended to classical waves, spawning the entire fields of photonic and phononic crystals where BGs are frequency (or wavelength) intervals where wave propagation is prohibited. For elastic waves, BGs are found in periodically alternating mechanical properties (i.e., stiffness and density). This gives birth to phononic crystals and later elastic metamaterials that have enabled unprecedented functionalities for a wide range of applications. Planar metamaterials are built for vibration shielding, while a myriad of works focus on integrating phononic crystals in microsystems for filtering, waveguiding, and dynamical strain energy confinement in optomechanical systems. Furthermore, the past decade has witnessed the rise of topological insulators, which leads to the creation of elastodynamic analogs of topological insulators for robust manipulation of mechanical waves. Meanwhile, additive manufacturing has enabled the realization of 3D architected elastic metamaterials, which extends their functionalities. This review aims to comprehensively delineate the rich physical background and the state‐of‐the art in elastic metamaterials and phononic crystals that possess engineered BGs for different functionalities and applications, and to provide a roadmap for future directions of these manmade materials.
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 Bose-Einstein condensate phases and dynamics with dipolar anisotropy

We investigate the quench dynamics of quasi-one and two dimensional dipolar Bose-Einstein condensates (dBEC) of $^{164}$Dy atoms under the influence of a fast rotating magnetic field. The magnetic field thus controls both the magnitude and sign of the dipolar potential. We account for quantum fluctuations, critical to formation of exotic quantum droplet and supersolid phases in the extended Gross-Pitaevskii formalism, which includes the so-called Lee-Huang-Yang (LHY) correction. An analytical variational ansatz allows us to obtain the phase diagrams of the superfluid and droplet phases. The crossover from the superfluid to the supersolid phase and to single and droplet arrays is probed with particle number and dipolar interaction. The dipolar strength is tuned by rotating the magnetic field with subsequent effects on phase boundaries. Following interaction quenches across the aforementioned phases, we monitor the dynamical formation of supersolid clusters or droplet lattices. We include losses due to three-body recombination over the crossover regime, where the three-body recombination rate coefficient scales with the fourth power of the scattering length ($a_s$) or the dipole length ($a_{dd}$). For fixed values of the dimensionless parameter, $\epsilon_{dd} = a_{dd}/a_s$, tuning the dipolar anisotropy leads to an enhancement of the droplet lifetimes.
Decoding Measurement-Prepared Quantum Phases and Transitions: from Ising model to gauge theory, and beyond

Measurements allow eﬃcient preparation of interesting quantum many-body states with long-range entanglement, conditioned on additional transformations based on measurement outcomes. Here, we demonstrate that the so-called conformal quantum critical points (CQCP) can be obtained by performing general single-site measurements in an appropriate basis on the cluster states in d ≥ 2. The equal-time correlators of the said states are described by correlation functions of certain d -dimensional classical model at ﬁnite temperatures, and feature spatial conformal invariance. This establishes an exact correspondence between the measurement-prepared critical states and conformal ﬁeld theories of a range of critical spin models, including familiar Ising models and gauge theories. Furthermore, by mapping the long-range entanglement structure of measured quantum states into the correlations of the corresponding thermal spin model, we rigorously establish the stability condition of the long-range entanglement in the measurement-prepared quantum states deviating from the ideal setting. Most importantly, we describe protocols to decode the resulting quantum phases and transitions without post-selection, thus transferring the exponential measurement complexity to a polynomial classical computation. Therefore, our ﬁndings suggest a novel mechanism in which a quantum critical wavefunction emerges, providing new practical ways to study quantum phases and conformal quantum critical points.
Automatic Infectious Disease Classification Analysis with Concept Discovery

Automatic infectious disease classification from images can facilitate needed medical diagnoses. Such an approach can identify diseases, like tuberculosis, which remain under-diagnosed due to resource constraints and also novel and emerging diseases, like monkeypox, which clinicians have little experience or acumen in diagnosing. Avoiding missed or delayed diagnoses would prevent further transmission and improve clinical outcomes. In order to understand and trust neural network predictions, analysis of learned representations is necessary. In this work, we argue that automatic discovery of concepts, i.e., human interpretable attributes, allows for a deep understanding of learned information in medical image analysis tasks, generalizing beyond the training labels or protocols. We provide an overview of existing concept discovery approaches in medical image and computer vision communities, and evaluate representative methods on tuberculosis (TB) prediction and monkeypox prediction tasks. Finally, we propose NMFx, a general NMF formulation of interpretability by concept discovery that works in a unified way in unsupervised, weakly supervised, and supervised scenarios.
On the Origins of Self-Modeling

Self-Modeling is the process by which an agent, such as an animal or machine, learns to create a predictive model of its own dynamics. Once captured, this self-model can then allow the agent to plan and evaluate various potential behaviors internally using the self-model, rather than using costly physical experimentation. Here, we quantify the benefits of such self-modeling against the complexity of the robot. We find a R2 =0.90 correlation between the number of degrees of freedom a robot has, and the added value of self-modeling as compared to a direct learning baseline. This result may help motivate self modeling in increasingly complex robotic systems, as well as shed light on the origins of self-modeling, and ultimately self-awareness, in animals and humans.
Rupture dynamics of flat colloidal films

Here, we report experimental results on the rupture of flat colloidal films over a large range of volume fractions, 0.00 $\le \phi\le$ 0.47. The films are formed using a constant fluid volume, ruptured with a needle, and recorded using a high-speed camera. We show that colloidal films rupture in a manner quantitatively similar to Newtonian fluids, even well into the shear thinning regime. These results are consistent with the well-known mechanism of the rupture of Newtonian films, where the rupture rim rolls outward collecting more fluid and thus film rupture is a shear-free process. However, in the case of spontaneous rupture under controlled humidity conditions, the same dense colloidal films show exotic instabilities reminiscent of a wrinkling fabric on the film surface. These instabilities were absent in manually ruptured films. We hypothesize that these instabilities occur when the film thickness becomes thin enough to compete with the colloidal particle size, due to film drainage before spontaneous rupture. Thus, although non-Newtonian flow properties do not influence film rupture dynamics for thick enough films, the effect of microstructure has dramatic consequences in thinner films.
Every picture tells a story: Image-grounded controllable stylistic story generation

Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriately assessing the quality of the story, steering the generated story into a certain style, and addressing the scarcity of image-story pair reference datasets limiting supervision during training. In this work, we introduce Plug-and-Play Story Teller (PPST) and improve image-to-story generation by: 1) alleviating the data scarcity problem by incorporating large pre-trained models, namely CLIP and GPT-2, to facilitate a fluent image-to-text generation with minimal supervision, and 2) enabling a more style-relevant generation by incorporating stylistic adapters to control the story generation. We conduct image-to-story generation experiments with non-styled, romance-styled, and action-styled PPST approaches and compare our generated stories with those of previous work over three aspects, i.e., story coherence, image-story relevance, and style fitness, using both automatic and human evaluation. The results show that PPST improves story coherence and has better image-story relevance, but has yet to be adequately stylistic.
Learning by Consuming: Optimal Pricing with Endogenous Information Provision

We study the revenue-maximizing mechanism when a buyer's value evolves endogenously because of learning-by-consuming. A seller sells one unit of a divisible good, while the buyer relies on his private, rough valuation to choose his first-stage consumption level. Consuming more leads to a more precise valuation estimate, after which the buyer determines the second-stage consumption level. The optimum is a menu of try-and-decide contracts, consisting of a first-stage price-quantity pair and a second-stage per-unit price for the remaining quantity. In equilibrium, a higher first-stage valuation buyer pays more for higher first-stage consumption and enjoys a lower second-stage per-unit price. Methodologically, we deal with the difficulty that due to the failure of single-crossing condition, monotonicity in allocation plus the envelope condition is insufficient for incentive compatibility. Our results help to understand contracts about sequential consumption with the learning feature; e.g., leasing contracts for experience goods and trial sessions for certain courses.
Understanding the Quality Factor of Mass-Loaded Tensioned Resonators

Mechanical resonators featuring large tensile stress have enabled a range of experiments in quantum optomechanics and novel sensing. Many sensing applications require functionalizing tensioned resonators by appending additional mass to them. However, this may dramatically change the resonator mode quality factor, and hence its sensitivity. In this work, we investigate the effect of the crossover from no mass load to a large mass load on the mode shape and quality factor of a tensioned resonator. We show through an analytical model and finite element analysis that as the load mass increases, surprisingly, the resonator mode shape becomes independent of the exact load mass, and therefore, its quality factor saturates. We validate this saturation effect experimentally by measuring the quality factor of a silicon nitride trampoline resonator while varying the load mass in a controlled manner.
Learning to Predict Requires Integrated Information

Embodied agents are regularly faced with the challenge to learn new tasks. In order to do that they need to be able to predict their next sensory state by forming an internal world model. We theorize that agents require a high value of information integration to update that world model in light of new information. This can be seen in the context of Integrated Information Theory, which provides a quantitative approach to consciousness and can be applied to neural networks. We use the sensorimotor loop to model the interactions among the agent’s brain, body and environment. Thereby we can calculate various information theoretic measures that quantify diﬀerent information ﬂows in the system, one of which corresponds to Integrated Information. Additionally we are able to measure the interaction among the body and the environment, which leads to the concept of Morphological Computation. Previous research reveals an antagonistic relationship between Integrated Information and Morphological Computation. A morphology adapted well to a task can reduce the necessity for Integrated Information signiﬁcantly. This creates the problem that embodied intelligence is correlated with reduced conscious experience. Here we propose a solution to this problem, namely that the agents need Integrated Information to learn. We support our hypothesis with results from a simple experimental setup in which the agents learn by using the em-algorithm.
Facial Expression Translation Using Landmark Guided GANs

We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in computer vision since the expression-to-expression translation is a non-linear and non-aligned problem. Moreover, it requires a high-level semantic understanding between the input and output images since the objects in images can have arbitrary poses, sizes, locations, backgrounds, and self-occlusions. To tackle this problem, we propose utilizing facial landmark information explicitly. Since it is a challenging problem, we split it into two sub-tasks, (i) category-guided landmark generation, and (ii) landmark-guided expression-to-expression translation. Two sub-tasks are trained in an end-to-end fashion that aims to enjoy the mutually improved benefits from the generated landmarks and expressions. Compared with current keypoint-guided approaches, the proposed LandmarkGAN only needs a single facial image to generate various expressions. Extensive experimental results on four public datasets demonstrate that the proposed LandmarkGAN achieves better results compared with state-of-the-art approaches only using a single image.
Neural network approximation of coarse-scale surrogates in numerical homogenization

Coarse-scale surrogate models in the context of numerical homogenization of linear elliptic problems with arbitrary rough diffusion coefficients rely on the efficient solution of fine-scale sub-problems on local subdomains whose solutions are then employed to deduce appropriate coarse contributions to the surrogate model. However, in the absence of periodicity and scale separation, the reliability of such models requires the local subdomains to cover the whole domain which may result in high offline costs, in particular for parameter-dependent and stochastic problems. This paper justifies the use of neural networks for the approximation of coarse-scale surrogate models by analyzing their approximation properties. For a prototypical and representative numerical homogenization technique, the Localized Orthogonal Decomposition method, we show that one single neural network is sufficient to approximate the coarse contributions of all occurring coefficient-dependent local sub-problems for a non-trivial class of diffusion coefficients up to arbitrary accuracy. We present rigorous upper bounds on the depth and number of non-zero parameters for such a network to achieve a given accuracy. Further, we analyze the overall error of the resulting neural network enhanced numerical homogenization surrogate model.
Assessment of coupled bilayer–cytoskeleton modelling strategy for red blood cell dynamics in flow

The red blood cell (RBC) membrane is composed of a lipid bilayer and a cytoskeleton interconnected by protein junction complexes, allowing for potential sliding between the lipid bilayer and the cytoskeleton. Despite this biological reality, it is most often modelled as a single-layer model, a hyperelastic capsule or a fluid vesicle. Another approach involves incorporating the membrane's composite structure using double layers, where one layer represents the lipid bilayer and the other represents the cytoskeleton. In this paper, we computationally assess the various modelling strategies by analysing RBC behaviour in extensional flow and four distinct regimes that simulate RBC dynamics in shear flow. The proposed double-layer strategies, such as the vesicle–capsule and capsule–capsule models, account for the fluidity and surface incompressibility of the lipid bilayer in different ways. Our findings demonstrate that introducing sliding between the layers offers the cytoskeleton a considerable degree of freedom to alleviate its elastic stresses, resulting in a significant increase in RBC elongation. Surprisingly, our study reveals that the membrane modelling strategy for RBCs holds greater importance than the choice of the cytoskeleton's reference shape. These results highlight the inadequacy of considering mechanical properties alone and emphasise the need for careful integration of these properties. Furthermore, our findings fortuitously uncover a novel indicator for determining the appropriate stress-free shape of the cytoskeleton.