The full-fledged development of qudits in superconducting circuits is hindered by limited interaction toolkit and stringent requirements on frequencies and anharmonicities. Here, the authors propose and demonstrate an alternative scheme to perform multi-qudit gates in transmon-based devices, which is based on Raman-assisted two-photon interactions.
Researchers have advanced smart coatings by manipulating nanoparticles to reconfigure themselves by using microscopy and computer simulation.
Metal organic frameworks (MOFs) on a bipyridinium basis can be excellent candidates to observe an ET due to their structural arrangement. Recently, Guo et al.12 reported a 2D semiconductor MOF composed by π-stacked redox active N-methylpyridinium cations, sandwiched by cyanide-bridged layers that shows light and temperature-induced color change with the formation of stable radicals even in an ambient atmosphere. In this case, the observed thermo-and photo-activated ET within these materials involves the pyridinium unit as acceptor and the uncoordinated CN- moiety as donor. These MOF structures are closely related to Prussian blue and/or Hofmann clathrate derivatives which offer excellent structural platforms to establish multifunctionality, due to their intrinsic magnetic properties: photomagnetism, magnetic ordering, valence tautomerism and/or a spin crossover (SCO)22,23,24,25,26.
In general, Hofmann clathrates are formed by three major building blocks, first off, aion metal center, second, a ligand (L) and lastly, a cyanometallate anion [M(CN)x]n– (x = 2 or 4, n = 1 or 2). Usually, this combination affords 2D planar metal−cyanide−metal sheets completed by a mono-or bidentate neutral organic ligand L, most often ammonia27, pyridine2830,31,32, triazole33,34,35,36,37 or pyrazine38,39,40 derivatives, with a general formula of {Fe(L)x[µ4-M’(CN)4]} (x = 1 or 2; M’= Ni, Pd or Pt) or {Fe(L)x[µ2-M(CN)2]} (x = 1 or 2; M= Cu, Ag or Au)28. The modulation of the ligand field around the iron center through the different units leads to the introduction of the SCO properties. To our knowledge, at least three exceptions to this general formula have been described in literature: i) if the ligand is a strong chelate, for example a quinoline derivative (aqin), 1D chains of [Fe(aqin)2(μ2-M(CN)4)] (M = NiII and PtII) are obtained, where [M(CN)4]2- acts as a bridge between the iron sites; ii) if the ligand is tetradentate, a {[Fe(μ4-bztpy)μ2-Pt(CN)4]·0.5bztpy} structure emerges, where 2D sheets, formed by the ligand and the FeII metal center, are again interconnected by [M(CN)4]2- building blocks42, and iii) very recently, Real et al. described two isomorphous structures, where the ligand carries a positive charge due to a spontaneous protonation during the crystallization process. The authors suggested that the positive charge could be responsible for an additional class of Hofmann-clathrates with the general formula {Fe(L)2[µ2-M(CN)4]}. It should be noted that in all of the above cases, the specific properties (electronic, steric, etc.) of the ligands are responsible for the final structure of these modified Hofmann-type clathrates, which, nevertheless, maintain a SCO behavior. Thus, we think that Hofmann-type clathrates offer an excellent structural platform to establish multifunctionality through introduction of a redox-active ligand, such as a bipyridinium-type derivative.
In this work, we explore this idea by synthesizing a large family of non-innocent ligands, which contain three functional building blocks (Fig. 1a; see Supplementary Figs 1 – 5 for characterization), the most important being a monocationic pyridinium unit that is responsible for the redox activity12,44,45,46,47. Attached to it, an aryl group allows for the fine-tuning of the delocalized electron density of the ligand by modifying the substituent R in para position. Lastly, a pyridine group is added to ensure the coordination to the iron center. The pyridinium moiety contains an electropositive, quaternary nitrogen atom that can be reduced to the highly air-sensitive neutral (radical) species (Fig. 1b). As demonstrated through several examples in the literature, the stability of the radical state can be largely improved by e.g. incorporating it in a MOF structure. In the case of the compound reported by Guo et al.12 the stabilization of the radical is due to π−π and cation-π interactions within the network. With this idea in mind, the abovementioned redox-active ligands were used to synthesize a family of Hofmann-type MOFs, which, as we show below, host a wealth of electronic phenomena, including both SCO and ET processes as well as redox activity…
Antiferromagnets are promising for nano-oscillator in terahertz frequency. However, realizing antiferromagnetic moment oscillation via spin-orbit torque remains elusive. Here, the authors demonstrate oscillations in Mn2Au films.
A journal of the UK-based Biochemical Society is retracting 25 papers after finding “systematic manipulation of our peer-review and publication processes by multiple individuals,” according to a statement provided to Retraction Watch.
The batch of retractions for Bioscience Reports is “the first time that we have issued this many retractions in one go for articles that we believe to be connected,” managing editor Zara Manwaring said in an email.
As academic publishing grapples with its papermill problem, many firms are retracting articles by the dozens, hundreds, or even thousands after discovering foul play.
IOP Publishing has retracted a total of 350 papers from two different 2021 conference proceedings because an “investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.”
The case is just the latest involving the discovery of papers full of gibberish – aka “tortured phrases” – thanks to the work of Guillaume Cabanac, a computer scientist at the University of Toulouse, Cyril Labbé, of University Grenoble-Alpes and Alexander Magazinov, of Skoltech, in Moscow. The tool detects papers that contain phrases that appear to have been translated from English into another language, and then back into English, likely with the involvement of paper-generating software.
The papers were in the Journal of Physics: Conference Series (232 articles), and IOP Conference Series: Materials Science and Engineering (118 articles), plus four editorials.
Sometimes leaving well-enough alone is the best policy. Ask Teja Santosh Dandibhotla.
Upset that a paper of his had been retracted from the Journal of Physics: Conference Series, Santosh, a computer scientist at the CVR College of Engineering in Hyderabad, India, contacted us to plead his case. (We of course do not make decisions about retractions, we reminded him.)
Santosh’s article, “Intelligent defaulter Prediction using Data Science Process,” had been pulled along with some 350 other papers in two conference proceedings because IOP Publishing had “uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation.”
The team has released the width-pruned version of the model on Hugging Face under the Nvidia Open Model License, which allows for commercial use. This makes it accessible to a wider range of users and developers who can benefit from its efficiency and performance.
“Pruning and classical knowledge distillation is a highly cost-effective method to progressively obtain LLMs [large language models] of smaller size, achieving superior accuracy compared to training from scratch across all domains,” the researchers wrote. “It serves as a more effective and data-efficient approach compared to either synthetic-data-style fine-tuning or pretraining from scratch.”
This work is a reminder of the value and importance of the open-source community to the progress of AI. Pruning and distillation are part of a wider body of research that is enabling companies to optimize and customize LLMs at a fraction of the normal cost. Other notable works in the field include Sakana AI’s evolutionary model-merging algorithm, which makes it possible to assemble parts of different models to combine their strengths without the need for expensive training resources.
Explore the nuances of the transformer architecture behind Llama 3 and its prospects for the GenAI ecosystem.
