We compute the next-to-leading-power corrections in the N-jettiness variable to the production of a prompt photon and a jet at next-to-leading order in perturbative QCD in the qq annihilation channel. We employ the k⊥ jet algorithm and assume that the N-jettiness value divided by the jet transverse momentum is the smallest parameter in the problem; in particular it should be small compared to the jet radius R.
Visionlanguage models demonstrate unprecedented performance and generalization across a wide range of tasks and scenarios. Integrating these foundation models into robotic navigation systems opens pathways toward building general-purpose robots. Yet, evaluating these models navigation capabilities remains constrained by costly real-world trials, overly simplified simulations, and limited benchmarks. We introduce NaviTrace, a high-quality Visual Question Answering benchmark where a model receives an instruction and embodiment type (human, legged robot, wheeled robot, bicycle) and must output a 2D navigation trace in image space. Across 1000 scenarios and more than 3000 expert traces, we systematically evaluate eight state-of-the-art VLMs using a newly introduced semantic-aware trace score. This metric combines Dynamic Time Warping distance, goal endpoint error, and embodiment-conditioned penalties derived from per-pixel semantics and correlates with human preferences. Our evaluation reveals consistent gap to human performance caused by poor spatial grounding and goal localization. NaviTrace establishes a scalable and reproducible benchmark for real-world robotic navigation.
Accurate plant phenotyping is essential for crop breeding and the development of health monitoring systems. Traditional phenotyping methods, such as visual observation and leaf counting, are inefficient and labor-intensive. Although automated approaches are more efficient, they still require extensive labeled datasets for training segmentation networks, which can be costly and time-consuming to prepare. This paper builds on Williams et al. and uses SAM for initial segmentation, then compares two approaches for selecting leaf segments: geometric and color-based filtering with automatically determined thresholds, and a lightweight CNN trained on minimal data. The CNN method delivers superior performance, with an Average Recall AR75 of 63 % and an Average Precision AP75 of 58 % (IoU threshold = 75 %) using only four training images, and minimal annotation work. These findings highlight the potential of combining SAM with CNN-based filtering for robust plant phenotyping applications, offering a scalable solution that makes advanced phenotyping more accessible and less dependent on extensive data preparation. Ph & auml;notypisierung ist ein zentrales Werkzeug f & uuml;r die Z & uuml;chtung und & Uuml;berwachung von Nutzpflanzen, doch herk & ouml;mmliche Verfahren wie visuelle Beobachtung und manuelle Blattz & auml;hlung sind arbeitsintensiv und wenig skalierbar. Moderne, automatisierte Ans & auml;tze beruhen meist auf & uuml;berwachten Segmentierungsverfahren und erfordern umfangreich annotierte Datens & auml;tze. In dieser Arbeit bauen wir auf den Ergebnissen von Williams et al. auf und nutzen das Segment Anything Model (SAM) f & uuml;r eine initiale, weitgehend datenunabh & auml;ngige Segmentierung. Darauf aufbauend vergleichen wir zwei Strategien zur Auswahl relevanter Blattsegmente: (i) geometrische und farbbasierte Filterung mit automatisch bestimmten Schwellenwerten und (ii) ein leichtgewichtiges CNN, das mit einem Minimaldatensatz trainiert wird. Die CNN-basierte Methode erreicht eine durchschnittliche Recall (AR75) von 63 % und eine durchschnittliche Precision (AP75) von 58 % bei Verwendung von lediglich vier Trainingsbildern. Dies unterstreicht das Potenzial der Kombination von Vision-Foundation-Modellen wie SAM mit CNN-gest & uuml;tzter Filterung f & uuml;r robustes, skalierbares Pflanzen-Ph & auml;notypisierung und reduziert zugleich die Abh & auml;ngigkeit von umfangreicher Datenannotation.
Organic-inorganic polymer hybrids were synthesized using a copolymer of DL-3-[α-4-(4,5-diphenyl-1H-imidazole-2-yl)-phenylacryloylamine]-ε-caprolactam and ε-caprolactam (PDC) and three alkoxysilanes: phenyltrimethoxysilane (PhTMOS), tolyltrimethoxysilane (TolTMOS), and tetramethoxysilane (TMOS). Despite the π−π interactions between the aromatic rings in the PDC side chains and those in the inorganic matrix, opaque composites were obtained when PhTMOS or TolTMOS was used as alkoxysilanes. On the other hand, when TMOS was used, the resulting polymer hybrids were transparent. FT-IR spectra of various polymer hybrids prepared by varying the PDC/TMOS ratio revealed that the peak corresponding to the C = O stretching vibration of the amide group in PDC shifted to lower wavenumbers as the proportion of inorganic oxide increased. This indicates that hydrogen bonds are formed between the amide groups in the organic polymer and the hydroxy groups in the inorganic oxide, resulting in a transparent polymer hybrid. Furthermore, TMOS and PDC were mixed in formic acid and stirred at room temperature under UV irradiation for 1 h. The mixture was then kept at 40 °C for 2 weeks, followed by drying under reduced pressure at 60 °C for 2 days. In the hybrid prepared with UV irradiation, UV irradiation partially promoted the crosslinking reaction via dimerization of the cinnamoyl group, improving the thermal stability of the hybrid material.
The Key4hep project aims to provide a turnkey software solution for the full experiment lifecycle, based on established community tools. Several future collider communities (CEPC, CLIC, EIC, FCC, and ILC) have joined to develop and adapt their workflows to use the common data model EDM4hep and common framework. Besides sharing of existing experiment workflows, one focus of the Key4hep project is the development and integration of new experiment independent software libraries. Ongoing collaborations with projects such as ACTS, CLUE, PandoraPFA and the OpenDataDector show the potential of Key4hep as an experiment-independent testbed and development platform. In this talk, we present the challenges of an experiment-independent framework along with the lessons learned from discussions of interested communities (such as LUXE) and recent adopters of Key4hep in order to discuss how Key4hep could be of interest to the wider HEP community while staying true to its goal of supporting future collider designs studies.
