Probing weak dipole-dipole interaction using phase-modulated non-linear spectroscopy

Phase-modulated non-linear spectroscopy with higher harmonic demodulation has recently been suggested to provide information on many-body excitations. In the present work we theoretically investigate the application of this method to infer the interaction strength between two particles that interact via weak dipole-dipole interaction. To this end we use full numerical solution of the Schr\"odinger equation with time-dependent pulses. For interpretation purpose we also derive analytical expressions in perturbation theory. We find one can detect dipole-dipole interaction via peak intensities (in contrast to line-shifts which typically are used in conventional spectroscopy). We provide a detailed study on the dependence of these intensities on the parameters of the laser pulse and the dipole-dipole interaction strength. Interestingly, we find that there is a phase between the first and second harmonic demodulated signal, whose value depends on the sign of the dipole-dipole interaction.Comment: 12 pages, 8 figures, Supporting information provided with the source file

P-values for high-dimensional regression

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the data from the second split. This yields asymptotic error control under minimal conditions. It involves, however, a one-time random split of the data. Results are sensitive to this arbitrary choice: it amounts to a `p-value lottery' and makes it difficult to reproduce results. Here, we show that inference across multiple random splits can be aggregated, while keeping asymptotic control over the inclusion of noise variables. We show that the resulting p-values can be used for control of both family-wise error (FWER) and false discovery rate (FDR). In addition, the proposed aggregation is shown to improve power while reducing the number of falsely selected variables substantially.Comment: 25 pages, 4 figure

Singular robust room-temperature spin response from topological Dirac fermions

Topological insulators are a class of solids in which the nontrivial inverted bulk band structure gives rise to metallic surface states that are robust against impurity scattering. In three-dimensional (3D) topological insulators, however, the surface Dirac fermions intermix with the conducting bulk, thereby complicating access to the low energy (Dirac point) charge transport or magnetic response. Here we use differential magnetometry to probe spin rotation in the 3D topological material family (Bi$_2$Se$_3$, Bi$_2$Te$_3$, and Sb$_2$Te$_3$). We report a paramagnetic singularity in the magnetic susceptibility at low magnetic fields which persists up to room temperature, and which we demonstrate to arise from the surfaces of the samples. The singularity is universal to the entire family, largely independent of the bulk carrier density, and consistent with the existence of electronic states near the spin-degenerate Dirac point of the 2D helical metal. The exceptional thermal stability of the signal points to an intrinsic surface cooling process, likely of thermoelectric origin, and establishes a sustainable platform for the singular field-tunable Dirac spin response.Comment: 20 pages, 14 figure

Disorder Effects in Charge Transport and Spin Response of Topological Insulators

Topological insulators are a class of solids in which the non-trivial inverted bulk band structure gives rise to metallic surface states that are robust against impurity backscattering. First principle calculations predicted Bi2Te3, Sb2Te3 and Bi2Se3 to be three-dimensional (3D) topological insulators with a single Dirac cone on the surface. The topological surface states were subsequently observed by angle-resolved photoemission (ARPES) and scanning tunneling microscopy (STM). The investigations of charge transport through topological surfaces of 3D topological insulators, however, have faced a major challenge due to large charge carrier densities in the bulk donated by randomly distributed defects such as vacancies and antisites. This bulk disorder intermixes surface and bulk conduction channels, thereby complicating access to the low-energy (Dirac point) charge transport or magnetic response and resulting in the relatively low measured carrier mobilities. Moreover, charge inhomogeneity arising from bulk disorder can result in pronounced nanoscale spatial fluctuations of energy on the surface, leading to the formation of surface \u27puddles\u27 of different carrier types. Great efforts have been made to combat the undesirable effects of disorder in 3D topological insulators and to reduce bulk carriers through chemical doping, nanostructure fabrication, and electric gating. In this work we have developed a new way to reduce bulk carrier densities using high-energy electron irradiation, thereby allowing us access to the topological surface quantum channels. We also found that disorder in 3D topological insulators can be beneficial. It can play an important part in enabling detection of unusual magnetic response from Dirac fermions and in uncovering new excitations, namely surface superconductivity in Dirac \u27puddles\u27. In Chapter 3 we show how by using differential magnetometry we could probe spin rotation in the 3D topological material family (Bi2Se3, Bi2Te3 and Sb2Te3), and describe our detection of paramagnetic singularity in the magnetic susceptibility at low magnetic fields that persists up to room temperature, and which we have demonstrated to arise from the surfaces of the samples. The singularity is universal to the entire family, largely independent of the bulk carrier density, and consistent with the existence of electronic states near the spin-degenerate Dirac point of the 2D helical metal. The exceptional thermal stability of the signal points to an intrinsic surface cooling process, probably of thermoelectric organ, and establishes a sustainable platform for the singular field-tunable Dirac spin response. In Chapter 4 we describe our discovery of surface superconductivity in a hole-conducting topological insulator Sb2Te3 with transition to zero resistance induced through a minor tuning of growth chemistry that depletes bulk conduction channels. The depletion shifts Fermi energy towards the Dirac point as witnessed by over two orders of magnitude reduced bulk hole density and by the largest carrier mobility (~ 25,000 cm2 V-1s-1) found in any topological material. Direct evidence from transport, the unprecedentedly large diamagnetic screening, and the presence of up to ~ 25 meV gaps in differential conductance detected by scanning tunneling spectroscopy (STM) reveal the superconducting condensate to emerge first in surface puddles at unexpectedly high temperature, near 50 K. Percolative Josephson paths mediated by diffusing quasiparticles establish global phase coherence around 9 K. Rich structure of this state lends itself to manipulation and tuning via growth conditions and the topological material\u27s parameters such as Fermi velocity and mean free path. In Chapter 5 we describe a new approach we have developed to reaching stable charge neutrality in 3D topological materials. The technique uses swift (~ 2.5 MeV energy) electron beams to compensate charged bulk defects and bring the Fermi level back into the bulk gap. By controlling the beam fluence we could tune bulk conductivity from p- (hole-like) to n-type (electron-like), crossing the Dirac point and back, while preserving the robust topological signatures of surface channels. We establish that at charge neutrality conductance has a two-dimensional (2D) character with a minimum value on the order of ten conductance quanta G0 = e2 / h. From quantum interference contribution to 2D conductance we demonstrate in two systems, Bi2Te3 and Bi2Se3, that at charge neutrality only two quantum channels corresponding to two topological surfaces are present. The charge neutrality point achieved using electron irradiation with long penetration range shows a route to intrinsic quantum transport of the topological states unconstrained by the bulk size

A hierarchical attention network-based approach for depression detection from transcribed clinical interviews

The high prevalence of depression in society has given rise to a need for new digital tools that can aid its early detection. Among other effects, depression impacts the use of language. Seeking to exploit this, this work focuses on the detection of depressed and non-depressed individuals through the analysis of linguistic information extracted from transcripts of clinical interviews with a virtual agent. Specifically, we investigated the advantages of employing hierarchical attention-based networks for this task. Using Global Vectors (GloVe) pretrained word embedding models to extract low-level representations of the words, we compared hierarchical local-global attention networks and hierarchical contextual attention networks. We performed our experiments on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, which contains audio, visual, and linguistic information acquired from participants during a clinical session. Our results using the DAIC-WoZ test set indicate that hierarchical contextual attention networks are the most suitable configuration to detect depression from transcripts. The configuration achieves an Unweighted Average Recall (UAR) of .66 using the test set, surpassing our baseline, a Recurrent Neural Network that does not use attention.Funding by EU- sustAGE (826506), EU-RADAR-CNS (115902), Key Program of the Natural Science Foundation of Tianjin, CHINA (18JCZDJC36300) and BMW Group Research Pages 221-225 https://www.isca-speech.org/archive/Interspeech_2019/index.htm

Stable topological insulators achieved using high energy electron beams

Topological insulators are transformative quantum solids with immune-to-disorder metallic surface states having Dirac band structure. Ubiquitous charged bulk defects, however, pull the Fermi energy into the bulk bands, denying access to surface charge transport. Here we demonstrate that irradiation with swift ($\sim 2.5$ MeV energy) electron beams allows to compensate these defects, bring the Fermi level back into the bulk gap, and reach the charge neutrality point (CNP). Controlling the beam fluence we tune bulk conductivity from \textit{p}- (hole-like) to \textit{n}-type (electron-like), crossing the Dirac point and back, while preserving the Dirac energy dispersion. The CNP conductance has a two-dimensional (2D) character on the order of ten conductance quanta $G_0 =e^2/h$, and reveals, both in Bi$_2$Te$_3$ and Bi$_2$Se$_3$, the presence of only two quantum channels corresponding to two topological surfaces. The intrinsic quantum transport of the topological states is accessible disregarding the bulk size.Comment: Main manuscript - 12 pages, 4 figures; Supplementary file - 15 pages, 11 figures, 1 Table, 4 Note

The MuSe 2021 Multimodal Sentiment Analysis Challenge: sentiment, emotion, physiological-emotion, and stress

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities. The purpose of MuSe 2021 is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), the sentiment analysis community (symbol-based), and the health informatics community. We present four distinct sub-challenges: MuSe-Wilder and MuSe-Stress which focus on continuous emotion (valence and arousal) prediction; MuSe-Sent, in which participants recognise five classes each for valence and arousal; and MuSe-Physio, in which the novel aspect of 'physiological-emotion' is to be predicted. For this year's challenge, we utilise the MuSe-CaR dataset focusing on user-generated reviews and introduce the Ulm-TSST dataset, which displays people in stressful depositions. This paper also provides detail on the state-of-the-art feature sets extracted from these datasets for utilisation by our baseline model, a Long Short-Term Memory-Recurrent Neural Network. For each sub-challenge, a competitive baseline for participants is set; namely, on test, we report a Concordance Correlation Coefficient (CCC) of .4616 CCC for MuSe-Wilder; .5088 CCC for MuSe-Stress, and .4908 CCC for MuSe-Physio. For MuSe-Sent an F1 score of 32.82% is obtained

MitoTrace: A Computational Framework for Analyzing Mitochondrial Variation in Single-Cell RNA Sequencing Data

Genetic variation in the mitochondrial genome is linked to important biological functions and various human diseases. Recent progress in single-cell genomics has established single-cell RNA sequencing (scRNAseq) as a popular and powerful technique to profile transcriptomics at the cellular level. While most studies focus on deciphering gene expression, polymorphisms including mitochondrial variants can also be readily inferred from scRNAseq. However, limited attention has been paid to investigate the single-cell landscape of mitochondrial variants, despite the rapid accumulation of scRNAseq data in the community. In addition, a diploid context is assumed for most variant calling tools, which is not appropriate for mitochondrial heteroplasmies. Here, we introduce MitoTrace, an R package for the analysis of mitochondrial genetic variation in bulk and scRNAseq data. We applied MitoTrace to several publicly accessible data sets and demonstrated its ability to robustly recover genetic variants from scRNAseq data. We also validated the applicability of MitoTrace to scRNAseq data from diverse platforms. Overall, MitoTrace is a powerful and user-friendly tool to investigate mitochondrial variants from scRNAseq data