Unraveling Thermal Transport Properties of MoTe2 Thin Films Using the Optothermal Raman Technique.

Understanding phonon transport and thermal conductivity of layered materials is not only critical for thermal management and thermoelectric energy conversion but also essential for developing future optoelectronic devices. Optothermal Raman characterization has been a key method to identify the properties of layered materials, especially transition-metal dichalcogenides. This work investigates the thermal properties of suspended and supported MoTe2 thin films using the optothermal Raman technique. We also report the investigation of the interfacial thermal conductance between the MoTe2 crystal and the silicon substrate. To extract the thermal conductivity of the samples, temperature- and power-dependent measurements of the in-plane E2g1 and out-of-plane A1g optical phonon modes were performed. The results show remarkably low in-plane thermal conductivities at room temperature, at around 5.16 ± 0.24 W/m·K and 3.72 ± 0.26 W/m·K for the E2g1 and the A1g modes, respectively, for the 17 nm thick sample. These results provide valuable input for the design of electronic and thermal MoTe2-based devices where thermal management is vital.

Efficient differentially private learning improves drug sensitivity prediction.

BACKGROUND: Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising solution: privacy is considered sufficient if presence of individual patients cannot be distinguished. However, differentially private learning with current methods does not improve predictions with feasible data sizes and dimensionalities. RESULTS: We show that useful predictors can be learned under powerful differential privacy guarantees, and even from moderately-sized data sets, by demonstrating significant improvements in the accuracy of private drug sensitivity prediction with a new robust private regression method. Our method matches the predictive accuracy of the state-of-the-art non-private lasso regression using only 4x more samples under relatively strong differential privacy guarantees. Good performance with limited data is achieved by limiting the sharing of private information by decreasing the dimensionality and by projecting outliers to fit tighter bounds, therefore needing to add less noise for equal privacy. CONCLUSIONS: The proposed differentially private regression method combines theoretical appeal and asymptotic efficiency with good prediction accuracy even with moderate-sized data. As already the simple-to-implement method shows promise on the challenging genomic data, we anticipate rapid progress towards practical applications in many fields. REVIEWERS: This article was reviewed by Zoltan Gaspari and David Kreil.