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Abstract Optical phonon engineering through nonlinear effects has been utilized in ultrafast control of material properties. However, nonlinear optical phonons typically exhibit rapid decay due to strong mode-mode couplings, limiting their effectiveness in temperature or frequency sensitive applications. Here we report the observation of long-lived nonlinear optical phonons through the spontaneous formation of phonon frequency combs in the van der Waals material CrXTe₃(X=Ge, Si) using high-resolution Raman scattering. Unlike conventional optical phonons, the highestA_gmode in CrGeTe₃splits into equidistant, sharp peaks forming a frequency comb that persists for hundreds of oscillations and survives up to 200K. These modes correspond to localized oscillations of Ge₂Te₆clusters, isolated from Cr hexagons, behaving as independent quantum oscillators. Introducing a cubic nonlinear term to the harmonic oscillator model, we simulate the phonon time evolution and successfully replicate the observed comb structure. Similar frequency comb behavior is observed in CrSiTe₃, demonstrating the generalizability of this phenomenon. Our findings demonstrate that Raman scattering effectively probes high-frequency nonlinear phonon modes, offering insight into the generation of long-lived, tunable phonon frequency combs with potential applications in ultrafast material control and phonon-based technologies. 

Dynamic treatment regimes (DTRs) are critical to precision medicine, optimizing long-term outcomes through personalized, real-time decision making in evolving clinical contexts, but require careful supervision for unsafe treatment risks. Existing efforts rely primarily on clinician prescribed gold standards despite the absence of a known optimal strategy, and predominantly using structured EHR data without extracting valuable insights from clinical notes, limiting their reliability for treatment recommendations. In this work, we introduce SAFER, a calibrated risk-aware tabular-language recommendation framework for DTR that integrates both structured EHR and clinical notes, enabling them to learn from each other, and addresses inherent label uncertainty by assuming ambiguous optimal treatment solution for deceased patients. Moreover, SAFER employs conformal prediction to provide statistical guarantees, ensuring safe treatment recommendations while filtering out uncertain predictions. Experiments on two publicly available sepsis datasets demonstrate that SAFER outperforms state-of-the-art baselines across multiple recommendation metrics and counterfactual mortality rate, while offering robust formal assurances. These findings underscore SAFER’s potential as a trustworthy and theoretically grounded solution for high-stakes DTR applications. 

ABSTRACT ObjectiveEstuarine fishes experience significant diel and seasonal variations in their environments, with climate change introducing additional stressors, including altered salinity, temperatures, and water levels. American Eels Anguilla rostrata are present in Atlantic estuaries from Venezuela to Greenland. Despite their wide distribution and shrinking population, American Eels are understudied, in part because of the research challenges posed by their unusual catadromous life history. This study examines the spatial effects of changing estuarine water quality variables (water temperature, dissolved oxygen, and salinity) on the American Eel population in the Hudson River estuary (HRE). MethodsThe Hudson River Biological Monitoring Program, conducted from 1974 to 2017, consists of a suite of surveys recording fish abundance data and water quality variables. As the largest component of the Hudson River Biological Monitoring Program, the Long River Ichthyoplankton Survey contains 44 years of data on American Eels in the HRE. Using LRS catch data and Hudson River Biological Monitoring Program water quality measurements, we developed statistical models of American Eel population centers in the HRE. ResultsThe young-of-year and yearling-or-older population centers shifted downstream over the course of the Long River Ichthyoplankton Survey at average rates of approximately 1.1 and 0.41 km per year, respectively, despite higher temperatures and lower dissolved oxygen conditions closer to the estuary’s mouth. Mean water temperature and dissolved oxygen for the entire estuary have significant relationships with the population centers of both age-classes, although the eels were not apparently tracking stable conductivity or water temperature conditions; nor were the young of year tracking stable dissolved oxygen levels. ConclusionsThe downstream shift in HRE American Eel population centers over several decades and the relationship between this shift and changing environmental conditions indicate the need for improved understanding of the population dynamics of the globally distributed and declining species of the genus Anguilla. This knowledge is critical in the face of rapidly changing ecosystems. 

Retinal image plays a crucial role in diagnosing various diseases, as retinal structures provide essential diagnostic information. However, effectively capturing structural features while integrating them with contextual information from retinal images remains a challenge. In this work, we propose segmentation-guided dual-branch network for retinal disease diagnosis using retinal images and their segmentation maps, named SegImgNet. SegImgNet incorporates a segmentation module to generate multi-scale retinal structural feature maps from retinal images. The classification module employs two encoders to independently extract features from segmented images and retinal images for disease classification. To further enhance feature extraction, we introduce the Segmentation-Guided Attention (SGA) block, which leverages feature maps from the segmentation module to refine the classification process. We evaluate SegImgNet on the public AIROGS dataset and the private e-ROP dataset. Experimental results demonstrate that SegImgNet consistently outperforms existing methods, underscoring its effectiveness in retinal disease diagnosis. 

With advancements in sensor technology, real-time monitoring of machine health conditions allows us to perform condition-based maintenance (CBM) for multi-unit systems. The maintenance decision of a unit is usually dependent on other units in a multi-unit system, inducing an exponentially large state space, which makes CBM of large multi-unit systems a very challenging engineering problem. In this work, we first propose two heuristic decision policies for multi-unit systems, namely the binary action policy and the $(n,N_1,N_2)$-policy. Then we propose a multi-step lookahead rollout approach using the two heuristic policies to solve the challenging CBM problem. By applying the binary action policy, we can effectively reduce the action space and thus reduce the computational load in the rollout, while the $(n,N_1,N_2)$-policy can be an excellent base policy for the rollout to improve upon. The theoretical gap between the proposed rollout approach and the optimal policy is also derived. The study further shows extensive experimentation to demonstrate the effectiveness of the proposed lookahead rollout approach for solving the CBM problem for small (3 and 5 units), medium (10 and 15 units), and large (20, 30, 40, and 50 units) scale systems.