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The recent synthesis of the chromium selenide compound LiCrSe$_2$ constitutes a valuable addition to the ensemble of two-dimensional triangular lattice antiferromagnets (2D-TLA). In this work we present the very first comprehensive study of the combined low temperature nuclear and magnetic structure established in this material. Details on the connection between Li-ion dynamics and structural changes are also presented along with a direct link between atomic structure and spin order via a strong magnetoelastic coupling. LiCrSe$_2$ was found to undergo a first order structural transition from a trigonal crystal system with space group $P\bar{3}m1$ to a monoclinic one with space group $C2/m$ at $T_{\rm s}=30$~K. Such restructuring of the lattice is accompanied by a magnetic transition at $T_{\rm N}=30$~K, with the formation of a complex spin arrangement for the Cr$^{3+}$ moments. Refinement of the magnetic structure with neutron diffraction data and complementary muon spin rotation analysis reveal the presence of two incommensurate magnetic domains with a up-up-down-down arrangement of the spins with ferromagnetic (FM) double chains coupled antiferromagnetically (AFM). In addition to this unusual arrangement, the spin axial vector is modulated both in direction and modulus, resulting in a spin density wave-like order with periodic suppression of the Cr moment along the chains. This behavior is believed to appear as a result of strong competition between direct exchange AFM and superexchange FM couplings established between both nearest neighbor and next nearest neighbor Cr$^{3+}$ ions. We finally conjecture that the resulting magnetic order is stabilized via subtle vacancy/charge order within the Li layers, potentially causing a mix of two different magnetic phases within the sample.

Two$-$dimensional (2D) triangular lattices antiferromagnets (2D$-$TLA) often manifest intriguing physical and technological properties, due to the strong interplay between lattice geometry and electronic properties. The recently synthesized 2$-$dimensional transition metal dichalcogenide LiCrTe$_2$, being a 2D$-$TLA, enriched the range of materials which can present such properties. In this work, muon spin rotation ($μ^+$SR) and neutron powder diffraction (NPD) have been utilized to reveal the true magnetic nature and ground state of LiCrTe$_2$. From high$-$resolution NPD the magnetic spin order at base$-$temperature is not, as previously suggested, helical, but rather collinear antiferromagnetic (AFM) with ferromagnetic (FM) spin coupling within the $ab-$plane and AFM coupling along the $c-$axis. The ordered magnetic Cr moment is established as $μ_{\rm Cr}=$ 2.36 $μ_{\rm B}$. From detailed $μ^+$SR measurements we observe an AFM ordering temperature $T_{\rm N}\approx$ 125 K. This value is remarkably higher than the one previously reported by magnetic bulk measurements. From $μ^+$SR we are able to extract the magnetic order parameter, whose critical exponent allows us to categorize LiCrTe$_2$ in the 3D Heisenberg AFM universality class. Finally, by combining our magnetic studies with high$-$resolution synchrotron X$-$ray diffraction (XRD), we find a clear coupling between the nuclear and magnetic spin lattices. This suggests the possibility for a strong magnon$-$phonon coupling, similar to what has been previously observed in the closely related compound LiCrO$_2$.

Pressure effect in NbSe2 resembles hallmark features of unconventional superconductivity in cuprates., We report on muon spin rotation experiments probing the magnetic penetration depth λ(T) in the layered superconductors in 2H-NbSe2 and 4H-NbSe2. The current results, along with our earlier findings on 1T′-MoTe2 (Guguchia et al.), demonstrate that the superfluid density scales linearly with Tc in the three transition metal dichalcogenide superconductors. Upon increasing pressure, we observe a substantial increase of the superfluid density in 2H-NbSe2, which we find to correlate with Tc. The correlation deviates from the abovementioned linear trend. A similar deviation from the Uemura line was also observed in previous pressure studies of optimally doped cuprates. This correlation between the superfluid density and Tc is considered a hallmark feature of unconventional superconductivity. Here, we show that this correlation is an intrinsic property of the superconductivity in transition metal dichalcogenides, whereas the ratio Tc/TF is approximately a factor of 20 lower than the ratio observed in hole-doped cuprates. We, furthermore, find that the values of the superconducting gaps are insensitive to the suppression of the charge density wave state.

The paper analyzes the use of a Multi Agent System for Model Based Diagnosis. In a large dynamical system, it is often infeasible or even impossible to maintain a model of the whole system. Instead, several incomplete models of the system have to be used to detect possible faults. These models may also be physically be distributed. A Multi Agent System of diagnostic agents may offer solutions for establishing a global diagnosis. If we use a separate agent for each incomplete model of the system, establishing a global diagnosis becomes a problem cooperation and negotiation between the diagnostic agents. This raises the question whether `a set of diagnostic agents, each having an incomplete model of the system, can (efficiently) determine the same global diagnosis as an ideal single diagnostic agent having the combined knowledge of the diagnostic agents?''

When agents need to interact in order to solve some (possibly common) problem, resolving potential conflicts beforehand is often preferred to coordination during execution. Agents may lose some flexibility, but their course of action will be more predictable and often also more efficient, obtaining a socially optimal outcome instead of a local optimum. One way to resolve conflicts beforehand is to give extra constraints to each of the agents such that when they all meet these constraints, the resulting execution is conflictfree. A set of constraints that meets this requirement is called a decoupling of the original problem; if it also maximizes the social welfare (i.e. the sum of the valuations of all the agents), it is called optimal. Representing interesting multiagent problems as a constraint problem, we show that finding an optimal decoupling is at least as hard as finding a solution for the constraint problem. We therefore focus on a constraint problem that is efficiently solvable, but still very relevant and interesting in the context of multiple agents executing their actions, i.e. the Simple Temporal Problem (STP). Two more technical results, then, are that we resolve the open question whether finding an optimal decoupling of the STP is NP-hard (it is), and if all agents have linear valuation functions, this decoupling problem can be solved efficiently.

A model for discrete event systems (DES) can be learned from observations. We propose a simple type of timed automaton to model DES where the timing of the events is important. Learning such an automaton is proven to be NP-complete by a reduction from the problem of learning deterministic finite state automata (DFA) without time. Based on this reduction, we show how the currently best learning algorithm for DFAs (state merging) can be adapted to deal with time information.

We argue that timed models are a suitable framework for the detection of behavior in real-world event systems. A timed model which detects behavior is constructible by a domain expert. The inference of these timed models from data is a hard problem. We prove the inference of a class of timed automata (event recording automata) to be harder than the inference of finite automata.

Molybdenum ditelluride, MoTe2, is a versatile material where the topological phase can be readily tuned by manipulating the associated structural phase transition. The fine details of the band structure of MoTe2, key to understanding its topological properties, have proven difficult to disentangle experientially due to the multiband character of the material. Through experimental optical conductivity spectra, we detect two strong low-energy interband transitions. Both are linked to excitations between spin-orbit split bands. The lowest interband transition shows a strong thermal shift, pointing to a chemical potential that dramatically decreases with temperature. With the help of ab initio calculations and a simple two-band model, we give qualitative and quantitative explanations of the main features in the temperature-dependent optical spectra up to 400 meV.

In recent years,we have seen amajor shift in computing systems: data volumes are growing very fast, but hardware capabilities to store, process, and transfer the massive data are not speeding up at the same rate. Today, data are generated from a variety of sources, such as social networking websites, business transactions, banking sectors, etc. These data are valuable and contain lots of vital information if they are analyzed efficiently. The processing capabilities of single machines, however, are not sufficient enough, whichmakes it harder to use them for data analysis. As a result, most web companies, but also the traditional business organizations, research labs, and universities, are scaling out their major computational frameworks to clusters of thousands of machines. To find the hidden and interesting insights from the data, in addition to simple queries, also complex machine learning algorithms and graphs processing are becoming a common choice in many areas. Nowadays, the problem to collect, store and analyze these data is called the Big Data problem.