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Proteins can aggregate into disordered aggregates or ordered assemblies such as amyloid fibrils. These two distinct phases serve differing roles in function and disease. How protein sequence determines the preferred phase is unknown. Here we establish a statistical mechanical disorder-to-order transition condition for compact polymer aggregates, including proteins. The theory produces a simple universal equation determining the favored phase as a function of temperature, polymer length, and interaction energy variance. We show that the sequence-dependent energy variance is efficiently calculated using atomistic simulations, so that the theory has no adjustable parameters. The equation accurately predicts experimental length-dependent crystallization temperatures of synthetic polymers. The equation also predicts that all protein sequences that aggregate will also favor ordering. Consequently, energy must be expended to maintain the steady-state disordered phase if it is not kinetically metastable on physiological timescales. More broadly, the theory suggests that aggregates of organic polymers will generally tend to order on habitable planets. This article is protected by copyright. All rights reserved.

The success of deep neural networks suggests that cognition may emerge from indecipherable patterns of distributed neural activity. Yet these networks are pattern-matching black boxes that cannot simulate higher cognitive functions and lack numerous neurobiological features. Accordingly, they are currently insufficient computational models for understanding neural information processing. Here, we show how neural circuits can directly encode cognitive processes via simple neurobiological principles. To illustrate, we implemented this model in a non-gradient-based machine learning algorithm to train deep neural networks called essence neural networks (ENNs). Neural information processing in ENNs is intrinsically explainable, even on benchmark computer vision tasks. ENNs can also simulate higher cognitive functions such as deliberation, symbolic reasoning and out-of-distribution generalization. ENNs display network properties associated with the brain, such as modularity, distributed and localist firing, and adversarial robustness. ENNs establish a broad computational framework to decipher the neural basis of cognition and pursue artificial general intelligence. The authors demonstrate how neural systems can encode cognitive functions, and use the proposed model to train robust, scalable deep neural networks that are explainable and capable of symbolic reasoning and domain generalization.

Synaptic vesicles are primed into a state that is ready for fast neurotransmitter release upon Ca2+-binding to Synaptotagmin-1. This state likely includes trans-SNARE complexes between the vesicle and plasma membranes that are bound to Synaptotagmin-1 and complexins. However, the nature of this state and the steps leading to membrane fusion are unclear, in part because of the difficulty of studying this dynamic process experimentally. To shed light into these questions, we performed all-atom molecular dynamics simulations of systems containing trans-SNARE complexes between two flat bilayers or a vesicle and a flat bilayer with or without fragments of Synaptotagmin-1 and/or complexin-1. Our results need to be interpreted with caution because of the limited simulation times and the absence of key components, but suggest mechanistic features that may control release and help visualize potential states of the primed Synaptotagmin-1-SNARE-complexin-1 complex. The simulations suggest that SNAREs alone induce formation of extended membrane-membrane contact interfaces that may fuse slowly, and that the primed state contains macromolecular assemblies of trans-SNARE complexes bound to the Synaptotagmin-1 C2B domain and complexin-1 in a spring-loaded configuration that prevents premature membrane merger and formation of extended interfaces, but keeps the system ready for fast fusion upon Ca2+ influx.

Two functional protein sequences can sometimes be separated by a fitness valley - a series of low or non-functional intermediate mutations that must be traversed to reach a more optimal or refined function. Time-varying selection pressure modulates evolutionary sampling of such valleys. Yet, how the amplitude and frequency of fluctuating selection influence the rate of protein evolution is poorly understood. Here, we derive a simple equation for the time-dependent probability of crossing a fitness valley as a function of evolutionary parameters: valley width, protein size, mutation rate, and selection pressure. The equation predicts that, under low selection pressure, the valley crossing rate is magnified by a factor that depends exponentially on valley width. However, after a characteristic time set by the evolutionary parameters, the rate rapidly decays. Thus, there is an optimal frequency of selection-pressure fluctuations that maximizes the rate of protein optimization. This result is reminiscent of the resonance frequency in mechanical systems. The equation unites empirical and theoretical results that were previously disconnected, and is consistent with time-dependent in vitro and clinical data. More generally, these results suggest that seasonal and climate oscillations could synchronously drive protein evolution at the resonant frequency across a range of organism hosts and timescales. This theory could also be applied to optimize de novo protein evolution in laboratory directed evolution using time-varying protocols.

In living systems, collective molecular behavior is driven by thermodynamic forces in the form of chemical gradients. Leveraging recent advances in the field of nonequilibrium physics, I show that increasing the thermodynamic force alone can induce qualitatively new behavior. To demonstrate this principle, general equations governing kinetic proofreading and microtubule assembly are derived. These equations show that new capabilities, including catalytic regulation of steady-state behavior and exponential enhancement of molecular discrimination, are only possible if the system is driven sufficiently far from equilibrium, and can emerge sharply at a threshold force. Regardless of design parameters, these results reveal that the thermodynamic force sets fundamental performance limits on tuning sensitivity, error, and waste. Experimental data show that these biomolecular processes operate at the limits allowed by theory.

Synaptic vesicles are primed into a state that is ready for fast neurotransmitter release upon Ca2+-binding to synaptotagmin-1. This state likely includes trans-SNARE complexes between the vesicle and plasma membranes that are bound to synaptotagmin-1 and complexins. However, the nature of this state and the steps leading to membrane fusion are unclear, in part because of the difficulty of studying this dynamic process experimentally. To shed light into these questions, we performed all-atom molecular dynamics simulations of systems containing trans-SNARE complexes between two flat bilayers or a vesicle and a flat bilayer with or without fragments of synaptotagmin-1 and/or complexin-1. Our results help visualize potential states of the release machinery en route to fusion, and suggest mechanistic features that may control the speed of release. In particular, the simulations suggest that the primed state contains almost fully assembled trans-SNARE complexes bound to the synaptotagmin-1 C2B domain and complexin-1 in a spring-loaded configuration where interactions of the C2B domain with the plasma membrane orient complexin-1 toward the vesicle, avoiding premature membrane merger but keeping the system ready for fast fusion upon Ca2+ influx.

Distributed in-memory caching is a key component of modern Internet services. Such caches are often accessed via remote procedure call (RPC), as RPC frameworks provide rich support for productionization, including protocol versioning, memory efficiency, auto-scaling, and hitless upgrades. However, full-featured RPC limits performance and scalability as it incurs high latencies and CPU overheads. Remote Memory Access (RMA) offers a promising alternative, but meeting productionization requirements can be a significant challenge with RMA-based systems due to limited programmability and narrow RMA primitives. This paper describes the design, implementation, and experience derived from CliqueMap, a hybrid RMA/RPC caching system. CliqueMap has been in production use in Google's datacenters for over three years, currently serves more than 1PB of DRAM, and underlies several end-user visible services. CliqueMap makes use of performant and efficient RMAs on the critical serving path and judiciously applies RPCs toward other functionality. The design embraces lightweight replication, client-based quoruming, self-validating server responses, per-operation client-side retries, and co-design with the network layers. These foci lead to a system resilient to the rigors of production and frequent post deployment evolution.

We propose a conceptual framework of multiversional narrative processing. Multiversional narrative processing is the consideration of multiple possible event sequences for an incomplete narrative during reception. It occurs naturally and is experienced in a wide range of cases, such as suspense, surprise, counterfactuals, and detective stories. Receiving a narrative, we propose, is characterized by the spontaneous creation of competing interpretive versions of the narrative that are then used to create predictions and projections for the narrative’s future. These predictions serve as a mechanism for integrating incoming information and updating the narrative model through prediction error, without completely eliminating past versions. We define this process as having three aspects: (1) constrained expectations, (2) preference projection, and (3) causal extrapolation. Constrained expectations and preference projections respectively create the bounds and subjective desires for a narrative’s progress, while causal extrapolation builds, reworks, and maintains the potential models for understanding the narrative.

Human reasoning can distill principles from observed patterns and generalize them to explain and solve novel problems. The most powerful artificial intelligence systems lack explainability and symbolic reasoning ability, and have therefore not achieved supremacy in domains requiring human understanding, such as science or common sense reasoning. Here we introduce deep distilling, a machine learning method that learns patterns from data using explainable deep learning and then condenses it into concise, executable computer code. The code, which can contain loops, nested logical statements, and useful intermediate variables, is equivalent to the neural network but is generally orders of magnitude more compact and human-comprehensible. On a diverse set of problems involving arithmetic, computer vision, and optimization, we show that deep distilling generates concise code that generalizes out-of-distribution to solve problems orders-of-magnitude larger and more complex than the training data. For problems with a known ground-truth rule set, deep distilling discovers the rule set exactly with scalable guarantees. For problems that are ambiguous or computationally intractable, the distilled rules are similar to existing human-derived algorithms and perform at par or better. Our approach demonstrates that unassisted machine intelligence can build generalizable and intuitive rules explaining patterns in large datasets that would otherwise overwhelm human reasoning.

To control viral infection, vertebrates rely on both inducible interferon responses and less well characterized cell intrinsic responses comprised of “at the ready” antiviral effector proteins. Here, we show that E3 ubiquitin ligase TRIM7 is a cell intrinsic antiviral effector that restricts multiple human enteroviruses by targeting viral 2BC, a membrane remodeling protein, for ubiquitination and proteasome-dependent degradation. Selective pressure exerted by TRIM7 results in emergence of a TRIM7-resistant coxsackievirus with a single point mutation in the viral 2C ATPase/helicase. In cultured cells, the mutation helps the virus evade TRIM7, but impairs optimal viral replication, and this correlates with a hyperactive and structurally plastic 2C ATPase. Unexpectedly, the TRIM7-resistant virus has a replication advantage in mice and causes lethal pancreatitis. These findings reveal a unique mechanism for targeting enterovirus replication and provide molecular insight into the benefits and trade-offs of viral evolution imposed by a host restriction factor.

Predicting the behavior of heterogeneous nonequilibrium systems is currently analytically intractable. Consequently, complex biological systems have resisted unifying principles. Here, I introduce a mapping from dynamical systems to battery-resistor circuits. I show that in these transformed variables (i) arbitrary numbers of heterogeneous dynamical transitions can be reduced to a Thevenin equivalent resistor which is invariant to driving from equilibrium, (ii) resistors (together with the external driving sources) are sufficient to describe system behavior, and (iii) the resistor's directional symmetry leads to universal theorems of nonequilibrium behavior. This mapping is used to derive two general steady-state relations. First, for any cyclic process, the maximum amplification of any state is tightly bounded by the total dissipation of all states; experimental data are used to show that the master signal protein Ras achieves this bound. Second, for any process, the response of any reaction due to driving any other reaction is identical to the reciprocal response rescaled by the ratio of the corresponding Thevenin resistors. This result generalizes Onsager's reciprocal relation to the strongly driven regime and makes a testable prediction about how systems should be designed or evolved to maximize response. These analytic results represent a new perspective applicable to biological complexity and suggest that this mapping provides the natural variables to study heterogeneous nonequilibrium systems.

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based optimization. Despite success on many tasks, such networks remain unexplainable black boxes incapable of symbolic reasoning and concept generalization. Here we show that a simple set of biologically consistent organizing principles confer these capabilities to neuronal networks. To demonstrate, we implement these principles in a novel machine learning algorithm, based on concept construction instead of optimization, to design deep neural networks that reason with explainable neuron activity. On a range of tasks including NP-hard problems, their reasoning capabilities grant additional cognitive functions, like deliberating through self-analysis, tolerating adversarial attacks, and learning transferable rules from simple examples to solve problems of unencountered complexity. The networks also naturally display properties of biological nervous systems inherently absent in current deep neural networks, including sparsity, modularity, and both distributed and localized firing patterns. Because they do not sacrifice performance, compactness, or training time on standard learning tasks, these networks provide a new black-box-free approach to artificial intelligence. They likewise serve as a quantitative framework to understand the emergence of cognition from neuronal networks.

Protein self-assembly is fundamental to biological function and disease. Experimentally, the atomic-level structure is difficult to obtain and the assembly mechanism is poorly understood. The large number of possible states accessible to such systems limits computational prediction. Here, I introduce a new computational approach that enforces conformational symmetry, whereby all chains in the system adopt the same conformation. Using this approach on a 2D lattice, a designed multi-chain conformation is found more than four orders of magnitude faster than existing approaches. Furthermore, the free energy landscape can be efficiently computed, showing potential for enabling atomistic prediction of protein self-assembly.

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within tau’s repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. We use cross-linking mass spectrometry, recombinant protein and synthetic peptide systems, in silico modeling, and cell models to conclude that the aggregation-prone 306VQIVYK311 motif forms metastable compact structures with its upstream sequence that modulates aggregation propensity. We report that disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis., The biophysical mechanisms of how disease-associated tau mutations drive amyloid formation are not well understood. Here the authors use biophysical approaches, cell models and MD simulations and find that the intrinsically disordered repeat domain of tau encodes a metastable local structure and perturbations through mutations and proline isomerization cause an aggregation phenotype in vitro and in cells.

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In this work, we utilize a figure-of-merit ( FOM ) to compare the performance of various phase-change materials (PCMs) in managing short bursts of high-power heat flux, particularly those associated with microprocessors undergoing bursty operation on a time scale of approximately one second. We numerically investigate the FOM for applications that have realistic boundary conditions and lack analytical solutions. We also fabricate microprocessor proxies with integrated PCM compartments mounted in a smartphone package, and experimentally demonstrate the use of a high- FOM PCM to buffer short heat spikes as large as 11 W/cm 2 .

The guest editors introduce the Top Picks and Honorable Mentions from the 2015 computer architecture conferences.

Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within tau’s repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. Using cross-linking mass spectrometry, intramolecular FRET, recombinant protein and synthetic peptide systems, in silico modeling, and cell models, we conclude that the aggregation prone 306VQIVYK311 motif forms metastable compact structures with the upstream sequence that modulates aggregation propensity. Disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.

Computational sprinting activates dark silicon to improve responsiveness by briefly but intensely exceeding a system's sustainable power limit. This article focuses on the energy implications of sprinting. The authors observe that sprinting can save energy even while improving responsiveness by enabling execution in chip configurations that, though thermally unsustainable, improve energy efficiency. Surprisingly, this energy savings can translate to throughput improvements even for long-running computations. Repeatedly alternating between sprint and idle modes while maintaining sustainable average power can outperform steady-state computation at the platform's thermal limit.

The lack of memory safety in languages such as C and C++ is a root source of exploitable security vulnerabilities. This article presents Watchdog, a hardware approach that eliminates such vulnerabilities by enforcing comprehensive memory safety. Inspired by prior software-only mechanisms, Watchdog maintains bounds and identifier metadata with pointers, propagates them on pointer operations, and checks them on pointer dereferences. Checking this bounds and identifier metadata provides both precise, byte-granularity buffer-overflow protection and protection from use-after-free errors, even in the presence of reallocations. Watchdog stores pointer metadata in a disjoint shadow space to provide comprehensive protection and ensure compatibility with existing code. To streamline implementation and reduce runtime overhead, Watchdog uses micro-operations to implement metadata access and checking, eliminates metadata copies via a register renaming scheme, and uses a dedicated identifier cache to reduce checking overhead.

The tight thermal constraints of mobile devices, which limit sustainable performance, and the bursty nature of interactive mobile applications call for a new design focus: enhancing user responsiveness rather than sustained throughput. To that end, this article explores computational sprinting, wherein a mobile device temporarily exceeds sustainable thermal limits to provide a brief, intense burst of computation in response to user input. By enabling tenfold more computation within the timescale of human patience, sprinting has the potential to fundamentally change the user experience that an interactive application can provide.

How protein structure encodes functionality is not fully understood. For example, long-range intraprotein communication can occur without measurable conformational change and is often not captured by existing structural correlation functions. It is shown here that important functional information is encoded in the timing of protein motions, rather than motion itself. I introduce the conditional activity function to quantify such timing correlations among the degrees of freedom within proteins. For three proteins, the conditional activities between side-chain dihedral angles were computed using the output of microseconds-long atomistic simulations. The new approach demonstrates that a sparse fraction of side-chain pairs are dynamically correlated over long distances (spanning protein lengths up to 7 nm), in sharp contrast to structural correlations, which are short-ranged (

The Boltzmann distribution predicts the collective behavior of systems at thermodynamic equilibrium as a function of their constituent parts. Yet most systems in nature are not at equilibrium, and a unified theory of their behavior does not currently exist. Here, I show that the Boltzmann distribution is a special case of a general distribution that governs all stochastic systems, even if far from equilibrium. The generalized Boltzmann distribution is explained as an analog of the voltage equation in electronics, where resistors, batteries, node voltages, and path currents correspond to equilibrium rate constants, driven rate constants, probabilities, and probability flows, respectively. The general distribution recapitulates known properties of weakly driven systems and enables new closed-form solutions for strongly-driven systems. These solutions provide insight into fundamental limits on system performance, and experimental data show that living systems can operate at those limits. The formal mapping between non-equilibrium systems and electronic circuits may provide a unified framework to simplify, understand, and ultimately control the behavior of complex non-equilibrium systems., Comment: Added additional clarifying examples, and more detailed derivations of the results as supplementary information

Languages such as C and C++ use unsafe manual memory management, allowing simple bugs (i.e., accesses to an object after deallocation) to become the root cause of exploitable security vulnerabilities. This paper proposes Watchdog, a hardware-based approach for ensuring safe and secure manual memory management. Inspired by prior software-only proposals, Watchdog generates a unique identifier for each memory allocation, associates these identifiers with pointers, and checks to ensure that the identifier is still valid on every memory access. This use of identifiers and checks enables Watchdog to detect errors even in the presence of reallocations. Watchdog stores these pointer identifiers in a disjoint shadow space to provide comprehensive protection and ensure compatibility with existing code. To streamline the implementation and reduce runtime overhead: Watchdog (1) uses micro-ops to access metadata and perform checks, (2) eliminates metadata copies among registers via modified register renaming, and (3) uses a dedicated metadata cache to reduce checking overhead. Furthermore, this paper extends Watchdog's mechanisms to detect bounds errors, thereby providing full hardware-enforced memory safety at low overheads.

Understanding and predicting protein folding would elucidate how misfolded proteins cause aggregation and amyloid formation, for example in Alzheimer's disease. Despite the seemingly bewildering complexity of protein biology, simple analytic models can still capture the basic physics and predict the fundamental limits for protein domain size and folding speed.

Today’s multicore chips commonly implement shared memory with cache coherence as low-level support for operating systems and application software. Technology trends continue to enable the scaling of the number of (processor) cores per chip. Because conventional wisdom says that the coherence does not scale well to many cores, some prognosticators predict the end of coherence. This paper seeks to refute this conventional wisdom by showing one way to scale on-chip cache coherence with bounded, modest costs by combining known techniques such as: shared caches augmented to track cached copies, explicit cache eviction notifications, and hierarchical design. Based on this scalable proof-of-concept design, we predict that on-chip coherence and the programming convenience and compatibility it provides are here to stay.

This paper presents Vellvm (verified LLVM), a framework for reasoning about programs expressed in LLVM's intermediate representation and transformations that operate on it. Vellvm provides a mechanized formal semantics of LLVM's intermediate representation, its type system, and properties of its SSA form. The framework is built using the Coq interactive theorem prover. It includes multiple operational semantics and proves relations among them to facilitate different reasoning styles and proof techniques. To validate Vellvm's design, we extract an interpreter from the Coq formal semantics that can execute programs from LLVM test suite and thus be compared against LLVM reference implementations. To demonstrate Vellvm's practicality, we formalize and verify a previously proposed transformation that hardens C programs against spatial memory safety violations. Vellvm's tools allow us to extract a new, verified implementation of the transformation pass that plugs into the real LLVM infrastructure; its performance is competitive with the non-verified, ad-hoc original.

Among the macromolecular patterns of biological significance, right-handed α-helices are perhaps the most abundant structural motifs. Here, guided by experimental findings, we discuss both ultrafast initial steps and longer-time-scale structural dynamics of helix-coil transitions induced by a range of temperature jumps in large, isolated macromolecular ensembles of an α-helical protein segment thymosin β_9 (Tβ_9), and elucidate the comprehensive picture of (un)folding. In continuation of an earlier theoretical work from this laboratory that utilized a simplistic structure-scrambling algorithm combined with a variety of self-avoidance thresholds to approximately model helix-coil transitions in Tβ_9, in the present contribution we focus on the actual dynamics of unfolding as obtained from massively distributed ensemble-convergent MD simulations which provide an unprecedented scope of information on the nature of transient macromolecular structures, and with atomic-scale spatiotemporal resolution. In addition to the use of radial distribution functions of ultrafast electron diffraction (UED) simulations in gaining an insight into the elementary steps of conformational interconversions, we also investigate the structural dynamics of the protein via the native (α-helical) hydrogen bonding contact metric which is an intuitive coarse graining approach. Importantly, the decay of α-helical motifs and the (globular) conformational annealing in Tβ_9 occur consecutively or competitively, depending on the magnitude of temperature jump.

We have studied the structural, energetic, and electronic properties of crystalline cellulose I using first-principles density functional theory (DFT) with semiempirical dispersion corrections. The predicted crystal structures of both Iα and Iβ phases agree well with experiments and are greatly improved over those predicted by DFT within the local and semilocal density approximations. The cohesive energy is analyzed in terms of interchain and intersheet interactions, which are calculated to be of similar magnitude. Both hydrogen bonding and van der Waals (vdW) dispersion forces are found to be responsible for binding cellulose chains together. In particular, dispersion corrections prove to be indispensable in reproducing the equilibrium intersheet distance and binding strength; however, they do not improve the underestimated hydrogen bond length from DFT. The computed energy gaps of crystalline cellulose are 5.7 eV (Iα) and 5.4 eV (Iβ), whereas localized surface states appear within the gap for surfaces. The interaction of cellulose with water is studied by investigating the adsorption of a single water molecule on the hydrophobic Iβ(100) surface. The formation of hydrogen bond at the water/cellulose interface is shown to depend sensitively on the adsorption site for example above the equatorial hydroxyls or the CH moieties pointing out of the cellulose sheets. VdW dispersion interactions also contribute significantly to the adsorption energy.

Traditional coherence protocols present a set of difficult trade-offs: the reliance of snoopy protocols on broadcast and ordered interconnects limits their scalability, while directory protocols incur a performance penalty on sharing misses due to indirection. This work introduces Patch (Predictive/Adaptive Token-Counting Hybrid), a coherence protocol that provides the scalability of directory protocols while opportunistically sending direct requests to reduce sharing latency. Patch extends a standard directory protocol to track tokens and use token-counting rules for enforcing coherence permissions. Token counting allows Patch to support direct requests on an unordered interconnect, while a mechanism called token tenure provides broadcast-free forward progress using the directory protocol's per-block point of ordering at the home along with either timeouts at requesters or explicit race notification messages. Patch makes three main contributions. First, Patch introduces token tenure, which provides broadcast-free forward progress for token-counting protocols. Second, Patch deprioritizes best-effort direct requests to match or exceed the performance of directory protocols without restricting scalability. Finally, Patch provides greater scalability than directory protocols when using inexact encodings of sharers because only processors holding tokens need to acknowledge requests. Overall, Patch is a “one-size-fits-all” coherence protocol that dynamically adapts to work well for small systems, large systems, and anywhere in between.

Temporal memory safety errors, such as dangling pointer dereferences and double frees, are a prevalent source of software bugs in unmanaged languages such as C. Existing schemes that attempt to retrofit temporal safety for such languages have high runtime overheads and/or are incomplete, thereby limiting their effectiveness as debugging aids. This paper presents CETS, a compile-time transformation for detecting all violations of temporal safety in C programs. Inspired by existing approaches, CETS maintains a unique identifier with each object, associates this metadata with the pointers in a disjoint metadata space to retain memory layout compatibility, and checks that the object is still allocated on pointer dereferences. A formal proof shows that this is sufficient to provide temporal safety even in the presence of arbitrary casts if the program contains no spatial safety violations. Our CETS prototype employs both temporal check removal optimizations and traditional compiler optimizations to achieve a runtime overhead of just 48% on average. When combined with a spatial-checking system, the average overall overhead is 116% for complete memory safety

A multiprocessor's memory consistency model imposes ordering constraints among loads, stores, atomic operations, and memory fences. Even for consistency models that relax ordering among loads and stores, ordering constraints still induce significant performance penalties due to atomic operations and memory ordering fences. Several prior proposals reduce the performance penalty of strongly ordered models using post-retirement speculation, but these designs either (1) maintain speculative state at a per-store granularity, causing storage requirements to grow proportionally to speculation depth, or (2) employ distributed global commit arbitration using unconventional chunk-based invalidation mechanisms. In this paper we propose InvisiFence, an approach for implementing memory ordering based on post-retirement speculation that avoids these concerns. InvisiFence leverages minimalistic mechanisms for post-retirement speculation proposed in other contexts to (1) track speculative state efficiently at block-granularity with dedicated storage requirements independent of speculation depth, (2) provide fast commit by avoiding explicit commit arbitration, and (3) operate under a conventional invalidation-based cache coherence protocol. InvisiFence supports both modes of operation found in prior work: speculating only when necessary to minimize the risk of rollback-inducing violations or speculating continuously to decouple consistency enforcement from the processor core. Overall, InvisiFence requires approximately one kilobyte of additional state to transform a conventional multiprocessor into one that provides performance-transparent memory ordering, fences, and atomic operations.

The C programming language is at least as well known for its absence of spatial memory safety guarantees (i.e., lack of bounds checking) as it is for its high performance. C's unchecked pointer arithmetic and array indexing allow simple programming mistakes to lead to erroneous executions, silent data corruption, and security vulnerabilities. Many prior proposals have tackled enforcing spatial safety in C programs by checking pointer and array accesses. However, existing software-only proposals have significant drawbacks that may prevent wide adoption, including: unacceptably high run-time overheads, lack of completeness, incompatible pointer representations, or need for non-trivial changes to existing C source code and compiler infrastructure. Inspired by the promise of these software-only approaches, this paper proposes a hardware bounded pointer architectural primitive that supports cooperative hardware/software enforcement of spatial memory safety for C programs. This bounded pointer is a new hardware primitive datatype for pointers that leaves the standard C pointer representation intact, but augments it with bounds information maintained separately and invisibly by the hardware. The bounds are initialized by the software, and they are then propagated and enforced transparently by the hardware, which automatically checks a pointer's bounds before it is dereferenced. One mode of use requires instrumenting only malloc, which enables enforcement of perallocation spatial safety for heap-allocated objects for existing binaries. When combined with simple intraprocedural compiler instrumentation, hardware bounded pointers enable a low-overhead approach for enforcing complete spatial memory safety in unmodified C programs.

This paper presents NoSQ (short for no store queue), a microarchitecture that performs store-load communication without a store queue and without executing stores in the out-of-order engine. NoSQ implements store-load communication using speculative memory bypassing (SMB), the dynamic short-circuiting of DEF-store-load-USE chains to DEF-USE chains. Whereas previous proposals used SMB as an opportunistic complement to conventional store queue-based forwarding, NoSQ uses SMB as a store queue replacement. NoSQ relies on two supporting mechanisms. The first is an advanced store-load bypassing predictor that for a given dynamic load can predict whether that load will bypass and the identity of the communicating store. The second is an efficient verification mechanism for both bypassed and non-bpyassed loads using in-order load re-execution with an SMB-aware store vulnerability window (SVW) filter. The primary benefit of NoSQ is a simple, fast datapath that does not contain store-load forwarding hardware; all loads get their values either from the data cache or from the register file. Experiments show that this simpler design - despite being more speculative - slightly outperforms a conventional store-queue based design on most benchmarks (by 2% on average)

Aims\ud Amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS) are two syndromic variants within the motor neurone disease spectrum. As PLS and most ALS cases are sporadic (SALS), this limits the availability of cellular models for investigating pathogenic mechanisms and therapeutic targets. The aim of this study was to use gene expression profiling to evaluate fibroblasts as cellular models for SALS and PLS, to establish whether dysregulated biological processes recapitulate those seen in the central nervous system and to elucidate pathways that distinguish the clinically defined variants of SALS and PLS.\ud \ud Methods\ud Microarray analysis was performed on fibroblast RNA and differentially expressed genes identified. Genes in enriched biological pathways were validated by quantitative PCR and functional assays performed to establish the effect of altered RNA levels on the cellular processes.\ud \ud Results\ud Gene expression profiling demonstrated that whilst there were many differentially expressed genes in common between SALS and PLS fibroblasts, there were many more expressed specifically in the SALS fibroblasts, including those involved in RNA processing and the stress response. Functional analysis of the fibroblasts confirmed a significant decrease in miRNA production and a reduced response to hypoxia in SALS fibroblasts. Furthermore, metabolic gene changes seen in SALS, many of which were also evident in PLS fibroblasts, resulted in dysfunctional cellular respiration.\ud \ud Conclusions\ud The data demonstrate that fibroblasts can act as cellular models for ALS and PLS, by establishing the transcriptional changes in known pathogenic pathways that confer subsequent functional effects and potentially highlight targets for therapeutic intervention.

Multicomponent seismic data have unique value for studying near-seafloor geology in deepwater environments. When properly processed, PP (compressional) and PS (converted-shear) images made from multicomponent seismic data acquired in deepwater with seafloor sensors show near-seafloor geology with impressive detail. These high-resolution images are invaluable for studying deepwater gas-hydrate systems.

The world's offshore continental margins contain vast reserves of gas hydrate, a frozen form of natural gas that is embedded in cold, near-seafloor strata. Published estimates suggest that the energy represented by gas hydrate may exceed the energy available from conventional fossil fuel by a factor of 2 or more. Understanding marine hydrate systems has become critical for long-term worldwide energy planning. Groups in several nations are attempting to evaluate the resource and to define seafloor stability problems across hydrate accumulations.

In this Letter, we report on the direct mapping of helicity in the helix-to-coil structural transitions by theoretically simulating the ultrafast electron diffraction (UED) technique for isolated proteins with thousands of possible conformations. A novel approach for producing random coil structures of a given helical protein is presented with proper account of the steric avoidance threshold. A ‘resonance’ is manifested in the UED patterns of the helical structures. This resonance erodes as the transition to random coils becomes dominant, indicating that the residual helicity in the sample and the ensemble-averaged radius of gyration can be obtained and related to structural dynamics of isolated proteins.

The Wisconsin Multifacet Project has created a simulation toolset to characterize and evaluate the performance of multiprocessor hardware systems commonly used as database and web servers. We leverage an existing full-system functional simulation infrastructure (Simics [14]) as the basis around which to build a set of timing simulator modules for modeling the timing of the memory system and microprocessors. This simulator infrastructure enables us to run architectural experiments using a suite of scaled-down commercial workloads [3]. To enable other researchers to more easily perform such research, we have released these timing simulator modules as the Multifacet General Execution-driven Multiprocessor Simulator (GEMS) Toolset, release 1.0, under GNU GPL [9].

We discuss, in a two-part article, the benefits of 90°-phase wavelets in stratigraphic and lithologic interpretation of seismically thin beds. In Part 1, seismic models of Ricker wavelets with selected phases are constructed to assess interpretability of composite waveforms in increasingly complex geologic settings. Although superior for single surface and thick-layer interpretation, zero-phase seismic data are not optimal for interpreting beds thinner than a wavelength because their antisymmetric thin-bed responses tie to the reflectivity series rather than to impedance logs. Nonsymmetrical wavelets (e.g., minimum-phase wavelets) are generally not recommended for interpretation because their asymmetric composite waveforms have large side lobes. Integrated zero-phase traces are also less desirable because they lose high-frequency components in the integration process. However, the application of 90°-phase data consistently improves seismic interpretability. The unique symmetry of 90°-phase thin-bed response eliminates the dual polarity of thin-bed responses, resulting in better imagery of thin-bed geometry, impedance profiles, lithology, and stratigraphy. Less amplitude distortion and less stratigraphy-independent, thin-bed interference lead to more accurate acoustic impedance estimation from amplitude data and a better tie of seismic traces to lithology-indicative wireline logs. Field data applications are presented in part 2 of this article.

We examine field seismic data to test the benefits of 90°-phase wavelets in thin-bed interpretation that are predicted by seismic modeling in part 1 of this paper. In an interbedded sandstone-shale Miocene succession in the Gulf of Mexico basin, a 90°-phase shift of nearly zero-phase seismic data significantly improves lithologic and stratigraphic interpretation. A match between seismic and acoustic impedance (AI) profiles results in a better tie between seismic amplitude traces and lithology-indicative logs. Better geometric imaging of AI units that does not use dual-polarity seismic events results in easier and more accurate reservoir delineation. Less amplitude distortion and the stratigraphy-independent nature of thin-bed interference significantly improves stratigraphic resolution and seismic stratigraphic profiling. For a Ricker-like wavelet having small side lobes, stratigraphic resolution of 90°-phase data is considerably higher than that of zero-phase data. In this specific case, stratigraphic resolution of 90°-phase data is λ/4 (λ = wavelength), compared with λ/2 for its zero-phase counterpart. Stratal slices made from 90°-phase data show geomorphologic patterns of depositional systems with less noise and fewer interference fingerprints. A Permian Basin field provides a real-world example of porous zones in thin, high-frequency carbonate sequences that are better visualized with 90°-phase seismic data than with zero-phase data.

Physiological adaptation to pregnancy exposes mother's cardiovascular system to relevant hemodynamic overload. These changes and other specific conditions of pregnancy, such as amniotic embolism, can point out unrecognized preexisting heart disease or, in the presence of some cofactors, be burdensome even for healthy hearts. Thus, tragic cases of heart failure or cardiac arrest may occur, whose management requires several considerations with respect of trying to save two lives at the same time, the need for drugs potentially harmful to the fetus, and assessment of emergent cesarean section.

Multicomponent seismic data provide the potential to better characterize the subsurface than do P-wave data alone. Converted waves (C-waves) and pure shear waves can be used to validate bright spot reflections, provide Poisson's ratio estimates, provide reflection images where the P-wave reflectivity contrast is small, image through gas clouds where the P-wave signal is attenuated/scattered, and detect/characterize fractures through shear-wave splitting.

Commercial workload and technology trends are pushing existing shared-memory multiprocessor coherence protocols in divergent directions. Token coherence provides a framework for new coherence protocols that can reconcile these opposing trends. The token coherence framework directly enforces the coherence invariant by counting tokens (requiring all of a block's tokens to write and at least one token to read). This token-counting approach enables more obviously correct protocols that do not rely on request ordering and can operate with alternative policies that seek to improve the performance of future multiprocessors.