Safety-Critical Control with Bounded Inputs via Reduced Order Models

Guaranteeing safe behavior on complex autonomous systems -- from cars to walking robots -- is challenging due to the inherently high dimensional nature of these systems and the corresponding complex models that may be difficult to determine in practice. With this as motivation, this paper presents a safety-critical control framework that leverages reduced order models to ensure safety on the full order dynamics -- even when these models are subject to disturbances and bounded inputs (e.g., actuation limits). To handle input constraints, the backup set method is reformulated in the context of reduced order models, and conditions for the provably safe behavior of the full order system are derived. Then, the input-to-state safe backup set method is introduced to provide robustness against discrepancies between the reduced order model and the actual system. Finally, the proposed framework is demonstrated in high-fidelity simulation, where a quadrupedal robot is safely navigated around an obstacle with legged locomotion by the help of the unicycle model

Eventual Discounting Temporal Logic Counterfactual Experience Replay

Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard RL framework can be too myopic to find maximally LTL satisfying policies. This paper makes two contributions. First, we develop a new value-function based proxy, using a technique we call eventual discounting, under which one can find policies that satisfy the LTL specification with highest achievable probability. Second, we develop a new experience replay method for generating off-policy data from on-policy rollouts via counterfactual reasoning on different ways of satisfying the LTL specification. Our experiments, conducted in both discrete and continuous state-action spaces, confirm the effectiveness of our counterfactual experience replay approach

AutoBiasTest: Controllable Sentence Generation for Automated and Open-Ended Social Bias Testing in Language Models

Social bias in Pretrained Language Models (PLMs) affects text generation and other downstream NLP tasks. Existing bias testing methods rely predominantly on manual templates or on expensive crowd-sourced data. We propose a novel AutoBiasTest method that automatically generates sentences for testing bias in PLMs, hence providing a flexible and low-cost alternative. Our approach uses another PLM for generation and controls the generation of sentences by conditioning on social group and attribute terms. We show that generated sentences are natural and similar to human-produced content in terms of word length and diversity. We illustrate that larger models used for generation produce estimates of social bias with lower variance. We find that our bias scores are well correlated with manual templates, but AutoBiasTest highlights biases not captured by these templates due to more diverse and realistic test sentences. By automating large-scale test sentence generation, we enable better estimation of underlying bias distributions

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning

Augmenting pretrained language models (LMs) with a vision encoder (e.g., Flamingo) has obtained state-of-the-art results in image-to-text generation. However, these models store all the knowledge within their parameters, thus often requiring enormous model parameters to model the abundant visual concepts and very rich textual descriptions. Additionally, they are inefficient in incorporating new data, requiring a computational-expensive fine-tuning process. In this work, we introduce a Retrieval-augmented Visual Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the relevant knowledge from the external database for zero and in-context few-shot image-to-text generations. By storing certain knowledge explicitly in the external database, our approach reduces the number of model parameters and can easily accommodate new data during evaluation by simply updating the database. We also construct an interleaved image and text data that facilitates in-context few-shot learning capabilities. We demonstrate that Re-ViLM significantly boosts performance for image-to-text generation tasks, especially for zero-shot and few-shot generation in out-of-domain settings with 4 times less parameters compared with baseline methods

Weak upper-mantle base revealed by postseismic deformation of a deep earthquake

Mantle viscosity plays a key role in the Earth’s internal dynamics and thermal history. Geophysical inferences of the viscosity structure, however, have shown large variability depending on the types of observables used or the assumptions imposed. Here, we study the mantle viscosity structure by using the postseismic deformation following a deep (approximately 560 km) earthquake located near the bottom of the upper mantle. We apply independent component analysis to geodetic time series to successfully detect and extract the postseismic deformation induced by the moment magnitude 8.2, 2018 Fiji earthquake. To search for the viscosity structure that can explain the detected signal, we perform forward viscoelastic relaxation modelling with a range of viscosity structures. We find that our observation requires a relatively thin (approximately 100 km), low-viscosity (10¹⁷ to 10¹⁸ Pa s) layer at the bottom of the mantle transition zone. Such a weak zone could explain the slab flattening and orphaning observed in numerous subduction zones, which are otherwise challenging to explain in the whole mantle convection regime. The low-viscosity layer may result from superplasticity induced by the postspinel transition, weak CaSiO₃ perovskite, high water content or dehydration melting

Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans

During memory formation, the hippocampus is presumed to represent the “content” of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features that comprise each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world

Engineered Antiviral Sensor Targets Infected Mosquitoes

Escalating vector disease burdens pose significant global health risks, so innovative tools for targeting mosquitoes are critical. We engineered an antiviral strategy termed REAPER (vRNA Expression Activates Poisonous Effector Ribonuclease) that leverages the programmable RNA-targeting capabilities of CRISPR Cas13 and its potent collateral activity. Akin to a stealthy Trojan Horse hiding in stealth awaiting the presence of its enemy, REAPER remains concealed within the mosquito until an infectious blood meal is up taken. Upon target viral RNA infection, REAPER activates, triggering programmed destruction of its target arbovirus such as chikungunya. Consequently, Cas13 mediated RNA targeting significantly reduces viral replication and its promiscuous collateral activity can even kill infected mosquitoes. This innovative REAPER technology adds to an arsenal of effective molecular genetic tools to combat mosquito virus transmission

Tuning nonequilibrium phase transitions with inertia

In striking contrast to equilibrium systems, inertia can profoundly alter the structure of active systems. Here, we demonstrate that driven systems can exhibit effective equilibrium-like states with increasing particle inertia, despite rigorously violating the fluctuation–dissipation theorem. Increasing inertia progressively eliminates motility-induced phase separation and restores equilibrium crystallization for active Brownian spheres. This effect appears to be general for a wide class of active systems, including those driven by deterministic time-dependent external fields, whose nonequilibrium patterns ultimately disappear with increasing inertia. The path to this effective equilibrium limit can be complex, with finite inertia sometimes acting to accentuate nonequilibrium transitions. The restoration of near equilibrium statistics can be understood through the conversion of active momentum sources to passive-like stresses. Unlike truly equilibrium systems, the effective temperature is now density dependent, the only remnant of the nonequilibrium dynamics. This density-dependent temperature can in principle introduce departures from equilibrium expectations, particularly in response to strong gradients. Our results provide additional insight into the effective temperature ansatz while revealing a mechanism to tune nonequilibrium phase transitions

Age-related matrix stiffening epigenetically regulates α-Klotho expression and compromises chondrocyte integrity

AbstractExtracellular matrix stiffening is a quintessential feature of cartilage aging, a leading cause of knee osteoarthritis. Yet, the downstream molecular and cellular consequences of age-related biophysical alterations are poorly understood. Here, we show that epigenetic regulation of α-Klotho represents a novel mechanosensitive mechanism by which the aged extracellular matrix influences chondrocyte physiology. Using mass spectrometry proteomics followed by a series of genetic and pharmacological manipulations, we discovered that increased matrix stiffness drove Klotho promoter methylation, downregulated Klotho gene expression, and accelerated chondrocyte senescence in vitro. In contrast, exposing aged chondrocytes to a soft matrix restored a more youthful phenotype in vitro and enhanced cartilage integrity in vivo. Our findings demonstrate that age-related alterations in extracellular matrix biophysical properties initiate pathogenic mechanotransductive signaling that promotes Klotho promoter methylation and compromises cellular health. These findings are likely to have broad implications even beyond cartilage for the field of aging research

The Evolution and Mass Dependence of Galaxy Cluster Pressure Profiles at 0.05 ≤ z ≤ 0.60 and 4 × 10¹⁴ M_⊙ ≤ M₅₀₀ ≤ 30 × 10¹⁴ M_⊙

We have combined X-ray observations from Chandra with Sunyaev–Zel’dovich effect data from Planck and Bolocam to measure intracluster medium pressure profiles from 0.03 R₅₀₀ ≤ R ≤ 5 R₅₀₀ for a sample of 21 low-z galaxy clusters with a median redshift of〈z〉= 0.08 and a median mass of =〈M₅₀₀〉= 6.1 × 10¹⁴ M_⊙ and a sample of 19 mid-z galaxy clusters with〈z〉= 0.50 and〈M₅₀₀〉= 10.6 × 10¹⁴ M_⊙. The mean scaled pressure in the low-z sample is lower at small radii and higher at large radii, a trend that is accurately reproduced in similarly selected samples from The Three Hundred simulations. This difference appears to be primarily due to dynamical state at small radii, evolution at intermediate radii, and a combination of evolution and mass dependence at large radii. Furthermore, the overall flattening of the mean scaled pressure profile in the low-z sample compared to the mid-z sample is consistent with expectations due to differences in the mass accretion rate and the fractional impact of feedback mechanisms. In agreement with previous studies, the fractional scatter about the mean scaled pressure profile reaches a minimum of ≃20% near 0.5 R₅₀₀. This scatter is consistent between the low-z and mid-z samples at all radii, suggesting it is not strongly impacted by sample selection, and this general behavior is reproduced in The Three Hundred simulations. Finally, analytic functions that approximately describe the mass and redshift trends in mean pressure profile shape are provided