The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search

AISec’20, November 13, 2020, Virtual Event, USATraining classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge. There were two tracks in which teams could participate: the attack track asked for adversarially modified malware samples and the defend track asked for trained neural network classifiers that are robust to such modifications. The teams were unaware of the attacks/defenses they had to detect/evade. Although only 9 teams participated, this unique setting allowed us to make several interesting observations. We also present the challenge winner: GRAMS, a family of novel techniques to train adversarially robust networks that preserve the intended (malicious) functionality and yield high-quality adversarial samples. These samples are used to iteratively train a robust classifier. We show that our techniques, based on discrete optimization techniques, beat purely gradient-based methods. GRAMS obtained first place in both the attack and defend tracks of the competition

Remanufacturing and Energy Savings

Remanufactured products that can substitute for new products are generally claimed to save energy. These claims are made from studies that look mainly at the differences in materials production and manufacturing. However, when the use phase is included, the situation can change radically. In this Article, 25 case studies for eight different product categories were studied, including: (1) furniture, (2) clothing, (3) computers, (4) electric motors, (5) tires, (6) appliances, (7) engines, and (8) toner cartridges. For most of these products, the use phase energy dominates that for materials production and manufacturing combined. As a result, small changes in use phase efficiency can overwhelm the claimed savings from materials production and manufacturing. These use phase energy changes are primarily due to efficiency improvements in new products, and efficiency degradation in remanufactured products. For those products with no, or an unchanging, use phase energy requirement, remanufacturing can save energy. For the 25 cases, we found that 8 cases clearly saved energy, 6 did not, and 11 were too close to call. In some cases, we could examine how the energy savings potential of remanufacturing has changed over time. Specifically, during times of significant improvements in energy efficiency, remanufacturing would often not save energy. A general design trend seems to be to add power to a previously unpowered product, and then to improve on the energy efficiency of the product over time. These trends tend to undermine the energy savings potential of remanufacturing

Commodifying and Consuming Endocrine Drugs in Republican China (1920s–1940s)

Since the introduction of hormone pharmaceuticals into China during the early twentieth century, these substances became objects of fascination for a growing urban elite class. Drawing from newspapers, medical journals, and advertisements, this article examines the unique trajectories of hormone medicine in China. In conversation with previous scholarship on the dynamics of advertising and consuming hormones in China, this article examines specifically the discourses around the production and science of hormones. The circulation of hormones was informed by ideas of traditional Chinese medical cosmologies and enrolled in a nationalist movement encouraging the consumption of hormones produced by emerging Chinese medical entrepreneurs. This article provides a case study in a postcolonial context that problematizes historiographies depicting a linear transition of global hormone science from backwards to scientific, from traditional to modern.S.M

Burst Imaging with Learned Continuous Kernels

Burst imaging is a technique that consists of taking multiple images in quick succession and merging them into one output image. By aligning and combining data from multiple frames, we can increase resolution, attenuate noise, reduce motion blur and expand the dynamic range to obtain a higher quality image. In this thesis, we propose a method that learns continuous kernels to process and merge burst frames. We show that the learned kernels adapt to local image information and take advantage of sub-pixel sample location information to demosaic, denoise and merge the burst into a high quality output.S.M

Characterizing Language Representations in the Human Mind and Brain

Language allows for the mapping of speech signals or written characters to meaning every time we engage in conversation or read. How can biological tissue, our brains, support this mapping process? This thesis characterizes the neural representations that enable humans to infer the meaning of a sentence. My work builds on the foundation that regions in the left frontal and temporal parts of the brain causally and selectively support language processing (the ‘language network’). Chapter 2 asks how the language network develops. Through a case study of an individual born without their left temporal lobe (but with neurotypical language abilities), I demonstrate that the presence of temporal language regions appears to be necessary for the development of ipsilateral frontal regions, which echoes evidence from aphasia that the temporal areas are more important for language function. Chapters 3-5 aim to understand the representations and computations that mediate language comprehension. Traditionally, this line of inquiry has been challenging given the limited utility of probing animal models whose communication systems differ substantially from human language. However, the recent advent of artificial language models (LMs) has demonstrated that a system other than the human brain is capable of generating fluent and coherent text. Chapter 3 introduces the use of LMs as model systems for studying neural representations of language. I ask what aspects of an LM’s representation of the linguistic input matter the most for model-to-brain similarity. Across a series of systematic comparisons, I show that meanings of content words, such as nouns and verbs, matter more than syntactic structure (e.g., word order and function words). In Chapter 4, I leverage this model-to-brain similarity to ask what kinds of linguistic input the human language regions are most responsive to. I use an LM to identify sentences that maximally drive or suppress activity in language regions, and I demonstrate that these regions respond most strongly to sentences that are sufficiently linguistically well-formed but unpredictable in their structure or meaning, suggesting that this network is tuned to input predictability in the service of efficient meaning extraction. Finally, in Chapter 5, I use high-field (7T) fMRI to search for the organizing dimensions of the language network. By performing a data-driven decomposition of neural responses to linguistically diverse sentences, I show that only two components—shared across individuals—emerged robustly, accounting for about 34% of the explainable variance. In line with work in Chapter 4, the first component appears to correspond to processing difficulty. The second component appears to correspond to meaning abstractness. Both components are distributed across frontal and temporal brain areas but show systematic topographies across participants. Altogether, this thesis provides a detailed characterization—across thousands of sentences and through spatially-precise neural measurements—of how the fronto-temporal language network supports language comprehension. This work brings us closer to deciphering the circuits and mechanisms that underlie the astonishing human capacity to infer complex meanings through language.Ph.D

Interactive Spin Dynamics in Magnon and Quantum Spin Systems

Spintronics utilizes the intrinsic spin of electrons to design next-generation electronic devices, reducing power consumption and enabling innovative computing functions. Over the past decades, significant research interest has been directed toward two types of spin-based systems: collective excitations of spins, known as spin waves or magnons, in magnetic materials, and optically active spin defects as represented by nitrogen-vacancy (NV) centers in diamond, leading to the prosperity of magnonics, quantum sensing, and quantum information processing. As the understanding of dynamics in individual spin systems has deepened, recently there has been an increasing interest in the interactive dynamics within hybrid spin systems. This shift in focus reflects an increasing curiosity about how these complex interactions can be harnessed to further advance their microwave and quantum applications. However, several challenges persist, including the limited coherence length of magnons and the restricted frequency range of NV-based magnetometers, which will be tackled in this thesis. We first leverage the chirality of interlayer magnetic dipolar interactions to introduce an easily implementable system—antiparallel aligned magnetic multilayers—for realizing topological magnonic surface states and low-dissipation spin current transport in a tunable manner. We then expand the frequency window of NV-based magnetometers using nonlinear microwave-spin interactions, offering novel functionalities in quantum state control and sensing. We further exploit nonlinear spin dynamics by hybridizing NV centers with magnonic thin films, which not only amplifies the intensity of nonlinear resonance signals that are intrinsic to NV spins, but also enables novel frequency mixings through parametric pumping and nonlinear magnon scattering effects. We believe our study of interactive spin dynamics in hybrid systems involving magnons, quantum spin defects, and microwave photons help optimize these systems for a wide range of applications in both classical and quantum domains.Ph.D

Transformers as Empirical Bayes Estimators The Poisson Model

We study the ability of transformers to perform In Context Learning (ICL) in the setting of Empirical Bayes for the Poison Model. On the theoretical side, we demonstrate the expressibility of transformers by formulating a way to approximate the Robbins estimator, the first empirical Bayes estimator for the Poisson model. On the empirical side, we show that transformers pre-trained on synthetic data can generalize to unseen prior and sequence lengths, outperforming existing methods like Robbins, NPMLE, and ERM monotone in efficiency and accuracy. By studying the internal behavior of the representations of the intermediate layers of these transformers, we found that the representation converges quickly and smoothly over the layers. We also demonstrate that it’s unlikely transformers are implementing Robbin’s or NPMLE estimators in context.M.Eng

Investigating the Atmospheric and Oceanic Drivers of Atlantic Multidecadal Variability and Predictability

Despite its numerous impacts across the Earth system, the relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. This thesis presents three pathways to understanding how oceanic and atmospheric processes generate key spatio-temporal signatures of AMV through a combination of processed-based and data-driven approaches. Part 1 (Chapter 2) takes a "bottom up" approach, building a hierarchy of stochastic models to identify the contributions of vertical entrainment and seasonality in local upper-ocean processes to sea surface temperature (SST) variability. Through this hierarchy, I highlight unrealistic features present in slab ocean models widely used to isolate atmospheric contributions to AMV. On the opposite end of the spectrum, Part 2 (Chapter 3) utilizes a "top-down" data-driven approach where deep neural networks are trained to predict the North Atlantic SST Index in both the Community Earth System Model 1 Large Ensemble (CESM1) and observation-based datasets using atmospheric and oceanic predictors. I apply explainable artificial intelligence techniques to highlight a significant source of multidecadal predictability over the Transition Zone in oceanic predictors such as sea surface salinity (SSS) and sea surface height in the presence of external forcings. Part 3 (Chapter 4) returns to the process-based hierarchy, but applies this to understanding SSS variability. The stochastic salinity model is used to investigate the role of mixed-layer re-emergence, subsurface ocean damping and SST-evaporation feedback in shaping the pattern and amplitude of AMV.Ph.D

Digital Thread Maturity in Manufacturing: A Cross-Industry Study Using the Model-Based Enterprise Capability Assessment Framework

Modern-day manufacturing organizations find themselves in volatile and competitive markets with increasing pressure to deliver products faster, at lower cost, and with increased quality. In response to this pressure, many organizations are considering how technological advancements may improve the efficiency of their product development operations. Leading organizations have digitally transformed their businesses by shifting away from manual processes, static documents, and siloed operations toward automation, model-based data, and interconnectivity enabled by a digital thread. Accordingly, organizations pursuing the competitive edge offered through the digitalization of their business operations have often used different assessment tools to benchmark their current capabilities and define their vision for the future of their organizational operations. This thesis proposes a set of model-based and digital thread capabilities that are central to the long-term success of product development operations, along with a corresponding maturity model that may be used to identify gaps between current- and future-state capability implementation. Using the proposed capability maturity model, known as the Model-based Enterprise Capability Assessment Framework (MECAF), this study evaluated and compared capability maturity across various organizations in the Aerospace and Defense, Automotive, and Heavy Machinery industries. Through interviews with each participating organization, this thesis also explores the expected benefits, common challenges, and anticipated value of implementing model-based capabilities. Additionally, this thesis proposes an approach to bridging the gap from strategy to implementation based on the lessons learned and best practices of the organizations studied.S.M

Search for long-lived heavy neutral leptons in proton-proton collision events with a lepton-jet pair associated with a secondary vertex at √s = 13 TeV

A search for long-lived heavy neutral leptons (HNLs) using proton-proton collision data corresponding to an integrated luminosity of 138 fb−1 collected at s = 13 TeV with the CMS detector at the CERN LHC is presented. Events are selected with a charged lepton originating from the primary vertex associated with the proton-proton interaction, as well as a second charged lepton and a hadronic jet associated with a secondary vertex that corresponds to the semileptonic decay of a long-lived HNL. No excess of events above the standard model expectation is observed. Exclusion limits at 95% confidence level are evaluated for HNLs that mix with electron and/or muon neutrinos. Limits are presented in the mass range of 1–16.5 GeV, with excluded square mixing parameter values reaching as low as 2 × 10−7. For masses above 11 GeV, the presented limits exceed all previous results in the semileptonic decay channel, and for some of the considered scenarios are the strongest to date