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Innovating under Uncertainty: The Patent-Eligibility of Artificial Intelligence after Alice Corp. v. CLS Bank International
Item type: Book Chapter
Whalen R.; Zingg R. (2026)
Artificial intelligence-related inventions raise complex questions of how to define the boundaries around patentable subject matter. In the United States, many claim that the recent doctrinal developments by the Supreme Court have led to incoherence and excessive uncertainty within the innovation community. In response, policymakers and stakeholders have suggested legislative amendments to address these concerns. We first review these developments, and subsequently use the patent examination record to empirically test the claims of increased uncertainty. We find that, although uncertainty did spike following the Supreme Court's holding in Alice, it quickly returned to levels comparable to its historic norm. This has implications both for those advocating for legislative changes to the law of eligible subject matter, as well as other jurisdictions considering adopting a test similar to that applied in Alice.
Fixed target experiments at CERN
Item type: Book Chapter
Crivelli P.; Lanfranchi G. (2026)
CERN’s fixed-target experiments have been at the core of the CERN physics program for over 50 years. They form a group of small and medium size experiments, very lively and productive, with a broad physics program. Their scope range from the study of quark gluon plasma in the interactions of heavy ions with a fixed target, to the measurement of ultra rare kaon decays, and the search for feebly-interacting particles and light dark matter. This chapter will guide you inside the CERN North Area at the Prevessin site that hosts a large variety of fixed-target experiments. We will focus on two experiments currently running, NA62 and NA64, both world leaders in their field: Kaon physics for NA62 and dark sector searches for NA64, respectively. We will explain how these two experiments have been designed, how they are operated, the physics results they have obtained, and their future perspectives.
Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection
Item type: Conference Paper
Kovacevic N.; Husler B.; Zhuang D.; et al. (2026)
Personal health data from wearables are typically presented through dashboards of charts and summary statistics, requiring users to actively interpret patterns and implications. We explore an alternative interaction paradigm: engaging with personal health data through an embodied conversational agent that facilitates objective data reflection in dialogue with the user. We present a system that combines lightweight preprocessing of wearable data with a Unity-based embodied character. Internally, the system follows a dual-agent design in which an Observer agent extracts descriptive statistics and temporal trends, and a Presenter agent communicates these findings through "spoken statistics," intentionally refraining from clinical advice to isolate the impact of the interaction modality. We evaluate this approach through a simulated-self user study (N=5) using a within-subject design. Participants adopted health personas and goals derived from the LifeSnaps dataset to compare traditional dashboard exploration with embodied conversational reflection. Our evaluation focuses on perceived understanding, the specificity of generated actions, and the cognitive shift from passive viewing to active sensemaking. The paper contributes a functional prototype, a design pattern for objective health data narrative generation, and early empirical insights into how embodiment affects the interpretation of personal health metrics.
Spectral and Polarization Control of High Purity Lithium Niobate Nanophotonic Photon-pair Sources
Item type: Conference Paper
Kuttner T.; Borello M.; Grange R.; et al. (2026)
We demonstrate an integrated lithium niobate SPDC source generating spectrally separable, deterministically split photon-pairs, with strongly tunable spectra towards degenerate or non-degenerate operation, which we use to demonstrate single source HOM interference with 92% visibility.
On Direction - Magnitude Decomposition in Low-Rank Matrix Optimization
Item type: Conference Paper
Wei Y.; Zhang L.; Li B.; et al. (2026)
This work studies direction-magnitude decomposition with a surrogate Riemannian gradient descent method as a framework for low-rank matrix optimization. We apply this framework to overparameterized matrix sensing and completion, and establish linear convergence from random initialization for their population counterpart, namely overparameterized matrix factorization. This yields an exponential acceleration over vanilla gradient descent. Moreover, this framework admits a strictly improved iteration complexity bound compared with standard Riemannian gradient descent. These results highlight the advantages of our proposed framework and indicate its potential for a broader class of low-rank optimization problems.