Environment-limited transfer of angular momentum in Bose liquids

Impurity motion in a many-body environment has been a central issue in the field of low-temperature physics for decades. In bosonic quantum fluids, the onset of a drag force experienced by point-like objects is due to collective environment excitations, driven by the exchange of linear momentum between the impurity and the many-body bath. In this work we consider a rotating impurity, with the aim of exploring how angular momentum is exchanged with the surrounding bosonic environment. In order to elucidate these issues, we employ a quasiparticle approach based on the angulon theory, which allows us to effectively deal with the non-trivial algebra of quantized angular momentum in the presence of a many-body environment. We uncover how impurity dressing by environmental excitations can establish an exchange channel, whose effectiveness crucially depends on the initial state of the impurity. Remarkably, we find that there is a critical value of initial angular momentum, above which this channel effectively freezes

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally generated top–down information. In the neocortex, top–down projections primarily target layer 1, which contains NDNF (neuron-derived neurotrophic factor)-expressing interneurons and the dendrites of pyramidal cells. Here, we investigate the hypothesis that NDNF interneurons shape cortical computations in an unconventional, layer-specific way, by exerting presynaptic inhibition on synapses in layer 1 while leaving synapses in deeper layers unaffected. We first confirm experimentally that in the auditory cortex, synapses from somatostatin-expressing (SOM) onto NDNF neurons are indeed modulated by ambient Gamma-aminobutyric acid (GABA). Shifting to a computational model, we then show that this mechanism introduces a distinct mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons. This motif can control inhibition in a layer-specific way and introduces competition between NDNF and SOM interneurons for dendritic inhibition onto pyramidal cells on different timescales. NDNF interneurons can thereby control cortical information flow by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition

Global increase in the occurrence and impact of multiyear droughts

Persistent multiyear drought (MYD) events pose a growing threat to nature and humans in a changing climate. We identified and inventoried global MYDs by detecting spatiotemporally contiguous climatic anomalies, showing that MYDs have become drier, hotter, and led to increasingly diminished vegetation greenness. The global terrestrial land affected by MYDs has increased at a rate of 49,279 ± 14,771 square kilometers per year from 1980 to 2018. Temperate grasslands have exhibited the greatest declines in vegetation greenness during MYDs, whereas boreal and tropical forests have had comparably minor responses. With MYDs becoming more common, this global quantitative inventory of the occurrence, severity, trend, and impact of MYDs provides an important benchmark for facilitating more effective and collaborative preparedness toward mitigation of and adaptation to such extreme events

Reaction precursor-mediated formation of stable supercrystals in colloidal nanocrystal synthesis: PbTe case

Supercrystals represent three-dimensional orderings of colloidal nanocrystals (NCs), showcasing collective properties in photonics, phononics, and electronics applications.1,2 Recent studies have shown that such assemblies are directly produced during nanocrystal reactions.3–6 However, a fundamental understanding of in situ formed supercrystals that withstand typical NC purification processes remains underexplored, which is important for further use. Herein, we report the reaction precursor-mediated formation of stable PbTe supercrystals. Rationalizing the formation of these assemblies through small-angle x-ray scattering (SAXS) measurements, we unveil their formation mechanism. Our findings reveal that the supercrystal formation occurs in the presence of an excess of lead oleates in the crude solution. It should be noted that the formed supercrystals can be stabilized under specific conditions determined by the lead oleate cluster concentration, content of trioctylphosphine telluride (TOP-Te), NC size and the need of an annealing step at mild conditions. Furthermore, this approach allows for the continuous growth of a secondary phase within the supercrystal; for example in the case of PbTe supercrystals, a PbS shell can be grown on each PbTe NC constituent, resulting in core-shell PbTe-PbS supercrystals. Our work elucidates that reaction precursors play an important role in in situ SC formation and stabilization, implying the possibility of applying this knowledge to other NC reactions

Machine learning analysis of the factors influencing university-industry collaborations

In the dynamic arena of innovation, the relations between academia and industry are a keystone for breakthroughs and practical applications. Yet, the groundwork of these pivotal University-Industry (U-I) partnerships remains covered in complexity. This paper delves into these intricate relations, unraveling the factors that help successful collaborations. Grounded in the Resource-Based Theory, our study transcends traditional analytical boundaries, leveraging a neural network model to understand a comprehensive dataset from the UK’s Higher Education Statistics Agency, SCIMAGO Rankings, and Clarivate Publications. This novel approach helps to make clear the interplay of academic load, administrative support, scientific output, and university rank in sculpting U-I collaboration dynamics. Our findings suggest that reduced academic load and robust administrative support significantly bolster U-I collaborations. However, the influence of scientific output and university ranking is more nuanced, challenging the common belief. High scientific output, while indicative of expertise, doesn't always align with industry goals. Similarly, while higher-ranked universities could attract more collaborations, the benefits are not universal. This paper not only contributes to a deeper understanding of U-I collaborations, but also provides actionable insights for university administrators, policymakers, and industry leaders. In a world where innovation is key, understanding these collaborative dynamics is crucial for fostering partnerships that push the boundaries of research and practical application

Kiwa is a membrane-embedded defense supercomplex activated at phage attachment sites

Bacteria and archaea deploy diverse antiviral defense systems, many of which remain mechanistically uncharacterized. Here, we characterize Kiwa, a widespread two-component system composed of the transmembrane sensor KwaA and the DNA-binding effector KwaB. Cryogenic electron microscopy (cryo-EM) analysis reveals that KwaA and KwaB assemble into a large, membrane-associated supercomplex. Upon phage binding, KwaA senses infection at the membrane, leading to KwaB binding of ejected phage DNA and inhibition of replication and late transcription, without inducing host cell death. Although KwaB can bind DNA independently, its antiviral activity requires association with KwaA, suggesting spatial or conformational regulation. We show that the phage-encoded DNA-mimic protein Gam directly binds and inhibits KwaB but that co-expression with the Gam-targeted RecBCD system restores protection by Kiwa. Our findings support a model in which Kiwa coordinates membrane-associated detection of phage infection with downstream DNA binding by its effector, forming a spatially coordinated antiviral mechanism

Monitoring robustness and individual fairness

In automated decision-making, it is desirable that outputs of decision-makers be robust to slight perturbations in their inputs, a property that may be called input-output robustness. Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past led to dissimilar outputs. This way, monitoring will complement existing offline ''robustification'' approaches to increase the trustworthiness of AI decision-makers. We show that the monitoring problem can be cast as the fixed-radius nearest neighbor (FRNN) search problem, which, despite being well-studied, lacks suitable online solutions. We present our tool Clemont, which offers a number of lightweight monitors, some of which use upgraded online variants of existing FRNN algorithms, and one uses a novel algorithm based on binary decision diagrams--a data-structure commonly used in software and hardware verification. We have also developed an efficient parallelization technique that can substantially cut down the computation time of monitors for which the distance between input-output pairs is measured using the L∞norm. Using standard benchmarks from the literature of adversarial and semantic robustness and individual fairness, we perform a comparative study of different monitors in Clemont, and demonstrate their effectiveness in correctly detecting robustness violations at runtime

On a question of Davenport and diagonal cubic forms over Fq(t)

Given a non-singular diagonal cubic hypersurface X⊂Pn−1 over Fq(t) with char(Fq)≠3, we show that the number of rational points of height at most |P| is O(|P|3+ε) for n=6 and O(|P|2+ε) for n=4. In fact, if n=4 and char(Fq)>3 we prove that the number of rational points away from any rational line contained in X is bounded by O(|P|3/2+ε). From the result in 6 variables we deduce weak approximation for diagonal cubic hypersurfaces for n≥7 over Fq(t) when char(Fq)>3 and handle Waring's problem for cubes in 7 variables over Fq(t) when char(Fq)≠3. Our results answer a question of Davenport regarding the number of solutions of bounded height to x31+x32+x33=x34+x35+x36 with xi∈Fq[t]

Aharonov–Casher theorems for Dirac operators on manifolds with boundary and APS boundary condition

The Aharonov–Casher theorem is a result on the number of the so-called zero modes of a system described by the magnetic Pauli operator in R2. In this paper we address the same question for the Dirac operator on a flat two-dimensional manifold with boundary and Atiyah–Patodi–Singer boundary condition. More concretely we are interested in the plane and a disc with a finite number of circular holes cut out. We consider a smooth compactly supported magnetic field on the manifold and an arbitrary magnetic field inside the holes

Quantum rotor in a two-dimensional mesoscopic Bose gas

We investigate a molecular quantum rotor in a two-dimensional Bose-Einstein condensate. The focus is on studying the angulon quasiparticle concept in the crossover from few- to many-body physics. To this end, we formulate the problem in real space and solve it with a mean-field approach in the frame co-rotating with the impurity. We show that the system starts to feature angulon characteristics when the size of the bosonic cloud is large enough to screen the rotor. More importantly, we demonstrate the departure from the angulon picture for large system sizes or large angular momenta where the properties of the system are determined by collective excitations of the Bose gas