Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable results. If this calibration error is sufficiently high, reconstruction can
fail entirely without any indication to the user. In this work, we highlight the necessity of building autocalibration into NLOS reconstruction in order to handle mis-calibration. We propose a forward model of NLOS measurements that is differentiable with respect to both, the hidden scene albedo, and virtual illumination and detector positions. With only a mean squared error loss and no regularization, our model enables joint reconstruction and recovery of calibration parameters by minimizing the measurement residual using gradient descent. We demonstrate our method is able to produce robust reconstructions using simulated and real data where the calibration error applied causes other state of the art algorithms to fail.
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining m
odel accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient to such reconstruction attacks and is based on reduction of distance correlation between raw data and learned representations during training and inference with image datasets. We prevent such reconstruction of raw data while maintaining information required to sustain good classification accuracies.
In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection. We employ adversarial perturbations for obfuscating certain variables in raw data while preservi
ng the rest. Current adversarial perturbation methods are used for data poisoning with minimal perturbations of the raw data such that the machine learning models performance is adversely impacted while the human vision cannot perceive the difference in the poisoned dataset due to minimal nature of perturbations. We instead apply relatively maximal perturbations of raw data to conditionally damage models classification of one attribute while preserving the model performance over another attribute. In addition, the maximal nature of perturbation helps adversely impact human perception in classifying hidden attribute apart from impacting model performance. We validate our result qualitatively by showing the obfuscated dataset and quantitatively by showing the inability of models trained on clean data to predict the hidden attribute from the perturbed dataset while being able to predict the rest of attributes.
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each method outperforms the other in terms of communication efficiency. We c
onsider various practical scenarios of distributed learning setup and juxtapose the two methods under various real-life scenarios. We consider settings of small and large number of clients as well as small models (1M - 6M parameters), large models (10M - 200M parameters) and very large models (1 Billion-100 Billion parameters). We show that increasing number of clients or increasing model size favors split learning setup over the federated while increasing the number of data samples while keeping the number of clients or model size low makes federated learning more communication efficient.
Phase-stable electromagnetic pulses in the THz frequency range offer several unique capabilities in time-resolved spectroscopy. However, the diversity of their application is limited by the covered spectral bandwidth. In particular, the upper frequen
cy limit of photoconductive emitters - the most widespread technique in THz spectroscopy - reaches only up to 7 THz in regular transmission mode due to the absorption by infrared-active optical phonons. Here, we present ultra-broadband (extending up to 70 THz) THz emission from Au implanted Ge emitter which is compatible with a fibre laser operating at 1.1 and 1.55 {mu}m wavelengths at a repetition rates of 10 and 20 MHz, respectively. This opens a perspective for the development of compact THz photonic devices operating up to multi-THz frequencies and compatible with Si CMOS technology.
The magnetic, magneto-transport and ARPES studies of Fe and S co-doped Bi2Se3 were investigated. With doping concentration magneto-resistance (MR) gradually decreases and for a certain doping concentration giant negative MR is observed which persists
up to room temperature. Magnetic measurement indicates that the negative MR is observed when ferromagnetic ordering is induced with Fe doping. The magnetic ordering can be attributed with the RKKY interaction. Positive MR reappears with larger doping concentration which may be attributed to the decrease of FM ordering due to the turning off of the spin-orbit coupling leading to the destruction of non-trivial bulk state. This in-effect de-hybridizes the conduction band with the Fe spin. The ARPES data also indicates that above a critical doping concentration (x>0.09) the non-trivial bulk state is completely destroyed.
The magneto-transport and magnetization measurements of Sb1.90Cu0.10Te3 were performed at different temperatures and different fields. Magneto-transport measurement at high field indicates the coexistence of both bulk and surface states. The magnetiz
ation shows the induced antiferromagnetic ordering with Cu doping and the observed quantum oscillation in it indicates that magnetization in Sb1.90Cu0.10Te3 is the bulk property. The non linearity in Hall data suggests the existence of anomalous and topological Hall effect. The anomalous and topological Hall effect (THE) from measured hall data of Cu doped Sb2Te3 topological insulator have been evaluated.
Structural, resistivity, thermoelectric power and magneto-transport properties of Cu doped Bi2Te3 topological insulators have been investigated. The occurrence of the tuning of charge carriers from n type to p type by Cu doping at Te sites of Bi2Te3
is observed both from Hall effect and thermoelectric power measurements. Carrier mobility decreases with the doping of Cu which provides evidence of the movement of Fermi level from bulk conduction band to the bulk valence band. Thermoelectric power also increaseswith doping of Cu.Moreover linear magnetoresistance (LMR) has been observed at high magnetic field in pure Bi2Te3 which is associated to the gapless topological surface states protected by time reversal symmetry (TRS), whereas doping of Cu breaks TRS and an opening of band gap occurs which quenches the LMR.
