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1. Slice-Aware Service Restoration with RecoveryTrucks for Optical Metro-Access Networks

   Sifat Ferdousi, Massimo Tornatore (December 2019, IEEE Globecom 2019)

   Next-generation optical metro-access networks are expected to support end-to-end virtual network slices for critical 5G services. However, disasters affecting physical infrastructures upon which network slices are mapped can cause significant disruption in these services. Operators can deploy recovery units or trucks to restore services based on slice requirements. In this study, we investigate the problem of slice-aware service restoration in metro-access networks with specialized recovery trucks to restore services after a disaster failure. We model the problem based on classical vehicle-routing problem to find optimal routes for recovery trucks to failure sites to provide temporary backup service until the network components are repaired. Our proposed slice-aware service-restoration approach is formulated as a mixed integer linear program with the objective to minimize penalty of service disruption across different network slices.We compare our slice-aware approach with a slice-unaware approach and show that our proposed approach can achieve significant reduction in service-disruption penalty 

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2. G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation

   https://doi.org/10.1007/s11548-024-03319-4  

   Harb, Samir Farag; Ali, Asem; Yousuf, Mohamed; Elshazly, Salwa; Farag, Aly (February 2025, International Journal of Computer Assisted Radiology and Surgery)

   Abstract Purpose This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings. Methods The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e., support). Segmentation starts by detecting the rectum using a Markov Random Field-based algorithm. Then, supervised sequential episodic training is applied to the remaining slices, while contrastive learning is employed to enhance feature discriminability, thereby improving segmentation accuracy. Results The proposed method, evaluated on 98 abdominal scans of prepped patients, achieved a Dice coefficient of 97.3% and a polyp information preservation accuracy of 98.28%. Statistical analysis, including 95% confidence intervals, underscores the method’s robustness and reliability. Clinically, this high level of accuracy is vital for ensuring the preservation of critical polyp details, which are essential for accurate automatic diagnostic evaluation. The proposed method performs reliably in scenarios with limited annotated data. This is demonstrated by achieving a Dice coefficient of 97.15% when the model was trained on a smaller number of annotated CT scans (e.g., 10 scans) than the testing dataset (e.g., 88 scans). Conclusions The proposed sequential segmentation approach achieves promising results in colon segmentation. A key strength of the method is its ability to generalize effectively, even with limited annotated datasets—a common challenge in medical imaging. 

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3. Temporally resolved parametric assessment of Z‐magnetization recovery (TOPAZ): Dynamic myocardial T ₁ mapping using a cine steady‐state look‐locker approach

   https://doi.org/10.1002/mrm.26887  

   Weingärtner, Sebastian; Shenoy, Chetan; Rieger, Benedikt; Schad, Lothar R.; Schulz‐Menger, Jeanette; Akçakaya, Mehmet (August 2017, Magnetic Resonance in Medicine)

   PurposeTo develop and evaluate a cardiac phase‐resolved myocardial T₁mapping sequence. MethodsThe proposed method for temporally resolved parametric assessment of Z‐magnetization recovery (TOPAZ) is based on contiguous fast low‐angle shot imaging readout after magnetization inversion from the pulsed steady state. Thereby, segmented k‐space data are acquired over multiple heartbeats, before reaching steady state. This results in sampling of the inversion‐recovery curve for each heart phase at multiple points separated by an R‐R interval. Joint T₁andestimation is performed for reconstruction of cardiac phase‐resolved T₁andmaps. Sequence parameters are optimized using numerical simulations. Phantom and in vivo imaging are performed to compare the proposed sequence to a spin‐echo reference and saturation pulse prepared heart rate–independent inversion‐recovery (SAPPHIRE) T₁mapping sequence in terms of accuracy and precision. ResultsIn phantom, TOPAZ T₁values with integratedcorrection are in good agreement with spin‐echo T₁values (normalized root mean square error = 4.2%) and consistent across the cardiac cycle (coefficient of variation = 1.4 ± 0.78%) and different heart rates (coefficient of variation = 1.2 ± 1.9%). In vivo imaging shows no significant difference in TOPAZ T₁times between the cardiac phases (analysis of variance:P = 0.14, coefficient of variation = 3.2 ± 0.8%), but underestimation compared with SAPPHIRE (T₁time ± precision: 1431 ± 56 ms versus 1569 ± 65 ms). In vivo precision is comparable to SAPPHIRE T₁mapping until middiastole (P > 0.07), but deteriorates in the later phases. ConclusionsThe proposed sequence allows cardiac phase‐resolved T₁mapping with integratedassessment at a temporal resolution of 40 ms. Magn Reson Med 79:2087–2100, 2018. © 2017 International Society for Magnetic Resonance in Medicine. 

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4. Time-sliced quantum circuit partitioning for modular architectures

   https://doi.org/10.1145/3387902.3392617  

   Baker, Jonathan M.; Duckering, Casey; Hoover, Alexander; Chong, Frederic T. (May 2020, CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers)

   Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. We reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable. 

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5. Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories

   https://doi.org/10.1021/acs.jctc.4c01725  

   Korchagina, Kseniia; Schwartz, Steven D (April 2025, Journal of Chemical Theory and Computation)

   This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita−Bayliss−Hillman (MBH) reaction. 

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