Miracle-dz/UniFAR-FTUs huggingface dataset:
SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel

Fuzzing has become one of the most effective bug finding approach for software. In recent years, 24*7 continuous fuzzing platforms have emerged to test critical pieces of software, e.g., Linux kernel. Though capable of discovering many bugs and providing reproducers (e.g., proof-of-concepts), a major problem is that they neglect a critical function that should have been built-in, i.e., evaluation of a bug's security impact. It is well-known that the lack of understanding of security impact can lead to delayed bug fixes as well as patch propagation. In this paper, we develop SyzScope, a system that can automatically uncover new"high-risk"impacts given a bug with seemingly"low-risk"impacts. From analyzing over a thousand low-risk bugs on syzbot, SyzScope successfully determined that 183 low-risk bugs (more than 15%) in fact contain high-risk impacts, e.g., control flow hijack and arbitrary memory write, some of which still do not have patches available yet.
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

The creation and destruction of agents in cooperative multi- agent reinforcement learning (MARL) is a critically under-explored area of research. Current MARL algorithms often assume that the number of agents within a group remains ﬁxed throughout an experiment. However, in many practi- cal problems, an agent may terminate before their teammates. This early termination issue presents a challenge: the termi- nated agent must learn from the group’s success or failure which occurs beyond its own existence. We refer to propa- gating value from rewards earned by remaining teammates to terminated agents as the Posthumous Credit Assignment problem. Current MARL methods handle this problem by plac- ing these agents in an absorbing state until the entire group of agents reaches a termination condition. Although absorb- ing states enable existing algorithms and APIs to handle terminated agents without modiﬁcation, practical training efﬁ- ciency and resource use problems exist. In this work, we ﬁrst demonstrate that sample complexity in- creases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while at- tention is more robust to variable size input. Then, we present a novel architecture for an existing state-of-the-art MARL algorithm which uses attention instead of a fully connected layer with absorbing states. Finally, we demonstrate that this novel architecture signiﬁcantly outperforms the standard architecture on tasks in which agents are created or destroyed within episodes as well as standard multi-agent coordination tasks.
Full-Body Visual Self-Modeling of Robot Morphologies

Internal computational models of physical bodies are fundamental to the ability of robots and animals alike to plan and control their actions. These"self-models"allow robots to consider outcomes of multiple possible future actions, without trying them out in physical reality. Recent progress in fully data-driven self-modeling has enabled machines to learn their own forward kinematics directly from task-agnostic interaction data. However, forward-kinema\-tics models can only predict limited aspects of the morphology, such as the position of end effectors or velocity of joints and masses. A key challenge is to model the entire morphology and kinematics, without prior knowledge of what aspects of the morphology will be relevant to future tasks. Here, we propose that instead of directly modeling forward-kinematics, a more useful form of self-modeling is one that could answer space occupancy queries, conditioned on the robot's state. Such query-driven self models are continuous in the spatial domain, memory efficient, fully differentiable and kinematic aware. In physical experiments, we demonstrate how a visual self-model is accurate to about one percent of the workspace, enabling the robot to perform various motion planning and control tasks. Visual self-modeling can also allow the robot to detect, localize and recover from real-world damage, leading to improved machine resiliency. Our project website is at: https://robot-morphology.cs. columbia.edu/
A Chinese Multi-type Complex Questions Answering Dataset over Wikidata

Complex Knowledge Base Question Answering is a popular area of research in the past decade. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in languages other than English. In addition, few state-of-the-art KBQA models are trained on Wikidata, one of the most popular real-world knowledge bases. We propose CLC-QuAD, the first large scale complex Chinese semantic parsing dataset over Wikidata to address these challenges. Together with the dataset, we present a text-to-SPARQL baseline model, which can effectively answer multi-type complex questions, such as factual questions, dual intent questions, boolean questions, and counting questions, with Wikidata as the background knowledge. We finally analyze the performance of SOTA KBQA models on this dataset and identify the challenges facing Chinese KBQA.
Harmony search algorithm for optimum design of steel frame structures: A comparative study with other optimization methods

In this article, a harmony search algorithm is presented for optimum design of steel frame structures. Harmony search is a meta-heuristic search method which has been developed recently. It is based on the analogy between the performance process of natural music and searching for solutions of optimization problems. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) and AISC Allowable Stress Design (ASD) specifications, maximum (lateral displacement) and interstorey drift constraints, and also size constraint for columns were imposed on frames. The results of harmony search algorithm were compared to those of the other optimization algorithms such as genetic algorithm, optimality criterion and simulated annealing for two planar and two space frame structures taken from the literature. The comparisons showed that the harmony search algorithm yielded lighter designs for the design examples presented.
Terahertz Radiation from Laser-Ionized Plasmas

Numerical simulations of laser-plasma interactions demonstrate the generation of axially polarized electromagnetic pulses (EMPs) that radiate away energy with a characteristic frequency determined by the plasma frequency, in the THz range for typical laser wakeﬁeld acceleration experiments. This is conﬁrmed by full 2D electromagnetic particle-in-cell simulations, as well as by a ponderomotively-driven reduced model that captures the EMP generation essentials. When the laser’s pulse length matches the plasma wavelength, that pulse’s fractional energy losses to the wakeﬁeld are independent of the pulse width, while the losses to the EMP are inversely dependent on the pulse width.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends

In this paper, we surveyed the existing literature studying different approaches and algorithms for the four critical components in the general branch and bound (B&B) algorithm, namely, branching variable selection, node selection, node pruning, and cutting-plane selection. However, the complexity of the B&B algorithm always grows exponentially with respect to the increase of the decision variable dimensions. In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently. We further surveyed how machine learning can be used to improve the four critical components in B&B algorithms. In general, a supervised learning method helps to generate a policy that mimics an expert but significantly improves the speed. An unsupervised learning method helps choose different methods based on the features. In addition, models trained with reinforcement learning can beat the expert policy, given enough training and a supervised initialization. Detailed comparisons between different algorithms have been summarized in our survey. Finally, we discussed some future research directions to accelerate and improve the algorithms further in the literature.
Agile Information System Development Organizations Transforming to Large-Scale Collaboration

We report findings from a case study of a large agile information systems development (ISD) organization`s sudden transformation to distributed, digital work in the context of the Covid-19 pandemic. It seeks to understand how knowledge creation and sharing changes. The findings show various forms of distance being introduced, digital tool usage, increased task orientation, and variations across teams. To analyze the findings, we use the concepts of large-scale collaborations and sociability. Large-scale collaboration offers a socio-technical perspective on tackling distributed knowledge sharing and creation in the presence of multiple, loosely coupled partners using digital tools for collaboration. We show what the digital tools afford using the concept of sociability. We discuss how distributed digital practices make teams more task-oriented and that creating and maintaining sociability, a key issue for knowledge sharing in agile ISD organizations, require relation oriented communication during practical problem solving using digital tools.
Persia: A Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters

Deep learning based models have dominated the current landscape of production recommender systems. Furthermore, recent years have witnessed an exponential growth of the model scale—from Google’s 2016 model with 1 billion parameters to the latest Face-book’s model with 12 trillion parameters. Significant quality boost has come with each jump of the model capacity, which makes us believe the era of 100 trillion parameters is around the corner. However, the training of such models is challenging even within industrial scale data centers. This difficulty is inherited from the staggering heterogeneity of the training computation—the model’s embedding layer could include more than 99 . 99% of the total model size, which is extremely memory-intensive; while the rest neural network is increasingly computation-intensive. To support the training of such huge models, an efficient distributed training sys-tem is in urgent need. In this paper, we resolve this challenge by careful co-design of both the optimization algorithm and the distributed system architecture. Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build
Driver-Specific Risk Recognition in Interactive Driving Scenarios Using Graph Representation

This paper presents a driver-specific risk recognition framework for autonomous vehicles that can extract inter-vehicle interactions. This extraction is carried out for urban driving scenarios in a driver-cognitive manner to improve the recognition accuracy of risky scenes. First, clustering analysis is applied to the operation data of drivers for learning the subjective assessment of risky scenes of different drivers and generating the corresponding risk label for each scene. Second, the graph representation model (GRM) is adopted to unify and construct the features of dynamic vehicles, inter-vehicle interactions, and static traffic markings in real driving scenes into graphs. The driver-specific risk label provides ground truth to capture the risk evaluation criteria of different drivers. In addition, the graph model represents multiple features of the driving scenes. Therefore, the proposed framework can learn the risk-evaluating pattern of driving scenes of different drivers and establish driver-specific risk identifiers. Last, the performance of the proposed framework is evaluated via experiments conducted using real-world urban driving datasets collected by multiple drivers. The results show that the risks and their levels in real driving environments can be accurately recognized by the proposed framework.
Full Characterization of Adaptively Strong Majority Voting in Crowdsourcing

In crowdsourcing, quality control is commonly achieved by having workers examine items and vote on their correctness. To minimize the impact of unreliable worker responses, a $\delta$-margin voting process is utilized, where additional votes are solicited until a predetermined threshold $\delta$ for agreement between workers is exceeded. The process is widely adopted but only as a heuristic. Our research presents a modeling approach using absorbing Markov chains to analyze the characteristics of this voting process that matter in crowdsourced processes. We provide closed-form equations for the quality of resulting consensus vote, the expected number of votes required for consensus, the variance of vote requirements, and other distribution moments. Our findings demonstrate how the threshold $\delta$ can be adjusted to achieve quality equivalence across voting processes that employ workers with varying accuracy levels. We also provide efficiency-equalizing payment rates for voting processes with different expected response accuracy levels. Additionally, our model considers items with varying degrees of difficulty and uncertainty about the difficulty of each example. Our simulations, using real-world crowdsourced vote data, validate the effectiveness of our theoretical model in characterizing the consensus aggregation process. The results of our study can be effectively employed in practical crowdsourcing applications.
Design and testing of a microbial fuel cell for the conversion of lignocellulosic biomass into electricity

Title of Document: DESIGN AND TESTING OF A MICROBIAL FUEL CELL FOR THE GENERATION OF ENERGY FROM LIGNOCELLULOSIC BIOMASS Kyla Gregoire, Master of Science, 2010 Civil & Environmental Engineering Directed By: Associate Professor, Jennifer Becker Environmental Science & Technology Previous research has demonstrated that microbial fuel cells (MFCs) have the ability to degrade soluble substrates such as wastewater; however, very few studies have attempted the conversion particulate biomass to electricity in an MFC. A single-chamber, air cathode MFC was developed using a solid, lignocellulosic substrate (corncob pellets) as the electron donor. The first trial, using a prototype reactor with a graphite rod anode, ran for 415 hours, and generated a maximum open circuit voltage and current of 0.67 V and 0.25 mA, respectively. The second trial employed graphite brush anodes and multiple microbial inocula. A pasteurized soil inoculum resulted in negligible power (Pmax = 0.144 mW/m 3 ). The addition of rumen fluid, which naturally contains cellulosedegrading microorganisms, and Geobacter metallireducens, resulted in Pmax values of 77 mW/m 3 and 159 mW/m 3 , respectively. Analysis of hydrogen, methane, organic acids, and the mass of substrate consumed provided insight into the relationship between cellulose oxidation, methanogenesis, and power production. DESIGN AND TESTING OF A MICROBIAL FUEL CELL FOR THE CONVERSION OF LIGNOCELLULOSIC BIOMASS INTO ELECTRICITY
FINO: Flow-based Joint Image and Noise Model

One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements. To tackle such an ill-posed inverse problem, the existing denoising approaches generally focus on exploiting effective natural image priors. The utilization and analysis of the noise model are often ignored, although the noise model can provide complementary information to the denoising algorithms. In this paper, we propose a novel Flow-based joint Image and NOise model (FINO) that distinctly decouples the image and noise in the latent space and losslessly reconstructs them via a series of invertible transformations. We further present a variable swapping strategy to align structural information in images and a noise correlation matrix to constrain the noise based on spatially minimized correlation information. Experimental results demonstrate FINO's capacity to remove both synthetic additive white Gaussian noise (AWGN) and real noise. Furthermore, the generalization of FINO to the removal of spatially variant noise and noise with inaccurate estimation surpasses that of the popular and state-of-the-art methods by large margins.
The fundamentals of Lyman-alpha exoplanet transits

Lyman-$\alpha$ transits have been detected from several nearby exoplanets and are one of our best insights into the atmospheric escape process. However, due to ISM absorption, we typically only observe the transit signature in the blue-wing, making them challenging to interpret. This challenge has been recently highlighted by non-detections from planets thought to be undergoing vigorous escape. Pioneering 3D simulations have shown that escaping hydrogen is shaped into a cometary tail receding from the planet. Motivated by this work, we develop a simple model to interpret Lyman-$\alpha$ transits. Using this framework, we show that the Lyman-$\alpha$ transit depth is primarily controlled by the properties of the stellar tidal field rather than details of the escape process. Instead, the transit duration provides a direct measurement of the velocity of the planetary outflow. This result arises because the underlying physics is the distance a neutral hydrogen atom can travel before it is photoionized in the outflow. Thus, higher irradiation levels, expected to drive more powerful outflows, produce weaker, shorter Lyman-$\alpha$ transits because the outflowing gas is ionized more quickly. Our framework suggests that the generation of energetic neutral atoms may dominate the transit signature early, but the acceleration of planetary material produces long tails. Thus, Lyman-$\alpha$ transits do not primarily probe the mass-loss rates. Instead, they inform us about the velocity at which the escape mechanism is ejecting material from the planet, providing a clean test of predictions from atmospheric escape models.
Proper Scoring Rules, Gradients, Divergences, and Entropies for Paths and Time Series

Many forecasts consist not of point predictions but concern the evolution of quantities. For example, a central bank might predict the interest rates during the next quarter, an epidemiologist might predict trajectories of infection rates, a clinician might predict the behaviour of medical markers over the next day, etc. The situation is further complicated since these forecasts sometimes only concern the approximate"shape of the future evolution"or"order of events". Formally, such forecasts can be seen as probability measures on spaces of equivalence classes of paths modulo time-parametrization. We leverage the statistical framework of proper scoring rules with classical mathematical results to derive a principled approach to decision making with such forecasts. In particular, we introduce notions of gradients, entropy, and divergence that are tailor-made to respect the underlying non-Euclidean structure.
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series

The Levenberg-Marquardt (LM) optimization algorithm has been widely used for solving machine learning problems. Literature reviews have shown that the LM can be very powerful and effective on moderate function approximation problems when the number of weights in the network is not more than a couple of hundred. In contrast, the LM does not seem to perform as well when dealing with pattern recognition or classification problems, and inefficient when networks become large (e.g. with more than 500 weights). In this paper, we exploit the true power of LM algorithm using some real world aircraft datasets. On these datasets most other commonly used optimizers are unable to detect the anomalies caused by the changing conditions of the aircraft engine. The challenging nature of the datasets are the abrupt changes in the time series data. We find that the LM optimizer has a much better ability to approximate abrupt changes and detect anomalies than other optimizers. We compare the performance, in addressing this anomaly/change detection problem, of the LM and several other optimizers. We assess the relative performance based on a range of measures including network complexity (i.e. number of weights), fitting accuracy, over fitting, training time, use of GPUs and memory requirement etc. We also discuss the issue of robust LM implementation in MATLAB and Tensorflow for promoting more popular usage of the LM algorithm and potential use of LM optimizer for large-scale problems.
COMAP Early Science. VII. Prospects for CO Intensity Mapping at Reionization

We introduce COMAP-EoR, the next generation of the Carbon Monoxide Mapping Array Project aimed at extending CO intensity mapping to the Epoch of Reionization. COMAP-EoR supplements the existing 30 GHz COMAP Pathfinder with two additional 30 GHz instruments and a new 16 GHz receiver. This combination of frequencies will be able to simultaneously map CO(1–0) and CO(2–1) at reionization redshifts (z ∼ 5–8) in addition to providing a significant boost to the z ∼ 3 sensitivity of the Pathfinder. We examine a set of existing models of the EoR CO signal, and find power spectra spanning several orders of magnitude, highlighting our extreme ignorance about this period of cosmic history and the value of the COMAP-EoR measurement. We carry out the most detailed forecast to date of an intensity mapping cross correlation, and find that five out of the six models we consider yield signal to noise ratios (S/Ns) ≳ 20 for COMAP-EoR, with the brightest reaching a S/N above 400. We show that, for these models, COMAP-EoR can make a detailed measurement of the cosmic molecular gas history from z ∼ 2–8, as well as probe the population of faint, star-forming galaxies predicted by these models to be undetectable by traditional surveys. We show that, for the single model that does not predict numerous faint emitters, a COMAP-EoR-type measurement is required to rule out their existence. We briefly explore prospects for a third-generation Expanded Reionization Array (COMAP-ERA) capable of detecting the faintest models and characterizing the brightest signals in extreme detail.
Comparison of the semiclassical and quantum optical field dynamics in a pulse-excited optical cavity with a finite number of quantum emitters

The spectral and temporal response of a set of N quantum emitters embedded in a photonic cavity is studied. Quantum mechanically, such systems can be described by the Tavis-Cummings (TC) model of N two-level systems coupled to a single light mode. Here we compare the full quantum solution of the TC model for different numbers of quantum emitters with its semiclassical limit after a pulsed excitation of the cavity mode. Considering different pulse amplitudes, we find that the spectra obtained from the TC model approach the semiclassical one for an increasing number of emitters N . Furthermore they match very well for small pulse amplitudes. While we observe a very good agreement in the temporal dynamics for photon numbers much smaller than N , considerable deviations occur in the regime of photon numbers similar to or larger than N , which are linked to collapse and revival phenomena. Wigner functions of the light mode are calculated for different scenarios to analyze the quantum state of the light field. We find strong deviations from a coherent state even if the dynamics of the expectation values are still well described by the semiclassical limit. For higher pulse amplitudes Wigner functions similar to those of Schrödinger cat states between two or more quasi-coherent contributions build up.