Synergetic enhancement of power factor and suppression of lattice thermal conductivity via electronic structure modification and nanostructuring on Ni and B co-doped p-type Si-Ge alloy

For simultaneously achieving the high-power factor and low lattice thermal conductivity of Si-Ge based thermoelectric materials, we employed, in this study, constructively modifying the electronic structure near the chemical potential and nano-structuring by low temperature and high-pressure sintering on nano-crystalline powders. Nickel was doped to create the impurity states near the edge of the valence band for enhancing the power factor with boron for tuning the carrier concentration. The nanostructured samples with the nominal composition of Si0.65-xGe0.32Ni0.03Bx (x = 0.01, 0.02, 0.03, and 0.04) were synthesized by the mechanical alloying followed low-temperature and high-pressure sintering process. A large magnitude of Seebeck coefficient reaching 321 {mu}VK-1 together with a small electrical resistivity of 4.49 m{Omega}cm, leads to a large power factor of 2.3 Wm-1K-2 at 1000 K. With successfully reduced thermal conductivity down to 1.47 Wm-1K-1, a large value of ZT ~1.56 was obtained for Si0.65-xGe0.32Ni0.03B0.03 at 1000 K

3D Scene Compression through Entropy Penalized Neural Representation Functions

Some forms of novel visual media enable the viewer to explore a 3D scene from arbitrary viewpoints, by interpolating between a discrete set of original views. Compared to 2D imagery, these types of applications require much larger amounts of storage space, which we seek to reduce. Existing approaches for compressing 3D scenes are based on a separation of compression and rendering: each of the original views is compressed using traditional 2D image formats; the receiver decompresses the views and then performs the rendering. We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints. The function is implemented as a neural network and jointly trained for reconstruction as well as compressibility, in an end-to-end manner, with the use of an entropy penalty on the parameters. Our method significantly outperforms a state-of-the-art conventional approach for scene compression, achieving simultaneously higher quality reconstructions and lower bitrates. Furthermore, we show that the performance at lower bitrates can be improved by jointly representing multiple scenes using a soft form of parameter sharing.

Capacitor Type Thin-Film Heat Flow Switching Device

We developed a capacitor type heat flow switching device, in which electron thermal conductivity of the electrodes is actively controlled through the carrier concentration varied by an applied bias voltage. The devices consist of an amorphous p-type Si-Ge-Au alloy layer, an amorphous SiO$_2$ as the dielectric layer, and a n-type Si substrate. Both amorphous materials are characterized by very low lattice thermal conductivity, less than 1 Wm-1K-1. The Si-Ge-Au amorphous layer with 40 nm in thickness was deposited by means of molecular beam deposition technique on the 100 nm thick SiO$_2$ layer formed at the top surface of Si substrate. Bias voltage-dependent thermal conductivity and heat flow density of the fabricated device were evaluated by a time-domain thermoreflectance method at room temperature. Consequently, we observed a 55 percent increase in thermal conductivity.

Selective Enhancement in Phonon Scattering leads to High Thermoelectric Figure of Merit in ZnO -- Graphene Oxide Core-shell Nanohybrids

ZnO is a promising candidate as an environment friendly thermoelectric (TE) material. However, the poor TE figure of merit (zT) needs to be addressed to achieve significant TE efficiency for commercial applications. Here we demonstrate that selective enhancement in phonon scattering leads to increase in zT of RGO encapsulated Al-doped ZnO core shell nanohybrids, synthesized via a facile and scalable method. The incorporation of 1 at% Al with 1.5 wt% RGO into ZnO (AGZO) has been found to show significant enhancement in zT (=0.52 at 1100 K) which is an order of magnitude larger compared to that of bare undoped ZnO. Photoluminescence and X-ray photoelectron spectroscopy measurements confirm that RGO encapsulation significantly quenches surface oxygen vacancies in ZnO along with nucleation of new interstitial Zn donor states. Tunneling spectroscopy reveals that the band gap of ~ 3.4 eV for bare ZnO reduces effectively to ~ 0.5 eV upon RGO encapsulation, facilitating charge transport. The electrical conductivity enhancement also benefits from the more than 95% densification achieved, using the spark plasma sintering method, which aids reduction of GO into RGO. The same Al doping and RGO capping synergistically brings about drastic reduction of thermal conductivity, through enhanced phonon-phonon and point defect-phonon scatterings. These opposing effects on electrical and thermal conductivities enhances the power factors as well as the zT value. Overall, a practically viable route for synthesis of oxide - RGO TE material which could find its practical applications for the high-temperature TE power generation.

End-to-end Learning of Compressible Features

Pre-trained convolutional neural networks (CNNs) are powerful off-the-shelf feature generators and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression, while general purpose lossy compression methods based on energy compaction (e.g. PCA followed by quantization and entropy coding) are sub-optimal, as they are not tuned to task specific objective. We propose a learned method that jointly optimizes for compressibility along with the task objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that our method produces features that are an order of magnitude more compressible, while having a regularization effect that leads to a consistent improvement in accuracy.

Anomalous dependence of thermoelectric parameters on carrier concentration and electronic structure in Mn-substituted Fe2CrAl Heusler alloy

We investigate the high temperature thermoelectric properties of Heusler alloys Fe2-xMnxCrAl (0<x<1). Substitution of 12.5% Mn at Fe-site (x = 0.25) causes a significant increase in high temperature resistivity (r{ho}) and an enhancement in the Seebeck coefficient (S), as compared to the parent alloy. However, as the concentration of Mn is increased above 0.25, a systematic decrement in the magnitude of both parameters is noted. These observations have been ascribed (from theoretical analysis) to a change in band gap and electronic structure of Fe2CrAl with Mn-substitution. Due to absence of mass fluctuations and lattice strain, no significant change in thermal conductivity is seen across this series of Heusler alloys. Additionally, S drastically changes its magnitude along with a crossover from negative to positive above 900 K, which has been ascribed to the dominance of holes over electrons in high temperature regime. In this series of alloys, S and r{ho} shows a strong dependence on the carrier concentration and strength of d-d hybridization between Fe/Mn and Cr atoms.

Deep Implicit Volume Compression

We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly compress the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algorithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively reducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.

Investigation of Thermoelectric properties of Magnetic Insulator FeRuTiSi Using First Principle Calculation

In this work, we have investigated the electronic structure and thermoelectric properties of quaternary heusler alloy, FeRuTiSi, using first principle DFT tools implemented in WIEN2k and BoltzTraP code. Electronic structure calculations using TB-mBJ potential shows appearance of flat band at the conduction band edge, thus electron in conduction band have the large effective mass (me*), and therefore mainly contribute for negatively large value of Seebeck coefficient (S). This alloy has indirect band gap of 0.59 eV, and shows the n-type transport behavior. Under the constant relaxation time approximation (tau = 10 -14 s), temperature dependent Seebeck coefficient, electrical conductivity (sigma), and electronic thermal conductivity (ke) were also estimated. The maximum figure-of-merit (ZT), for the FeRuTiSi compound is found to be ~0.86 at 840 K, with n-type doping, which suggests that this quaternary alloy can be a good candidate among the n-type material for thermoelectric applications in high-temperature reg

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

Batch Normalization (BN) uses mini-batch statistics to normalize the activations during training, introducing dependence between mini-batch elements. This dependency can hurt the performance if the mini-batch size is too small, or if the elements are correlated. Several alternatives, such as Batch Renormalization and Group Normalization (GN), have been proposed to address this issue. However, they either do not match the performance of BN for large batches, or still exhibit degradation in performance for smaller batches, or introduce artificial constraints on the model architecture. In this paper we propose the Filter Response Normalization (FRN) layer, a novel combination of a normalization and an activation function, that can be used as a replacement for other normalizations and activations. Our method operates on each activation channel of each batch element independently, eliminating the dependency on other batch elements. Our method outperforms BN and other alternatives in a variety of settings for all batch sizes. FRN layer performs $approx 0.7-1.0%$ better than BN on top-1 validation accuracy with large mini-batch sizes for Imagenet classification using InceptionV3 and ResnetV2-50 architectures. Further, it performs $>1%$ better than GN on the same problem in the small mini-batch size regime. For object detection problem on COCO dataset, FRN layer outperforms all other methods by at least $0.3-0.5%$ in all batch size regimes.

Scalable Model Compression by Entropy Penalized Reparameterization

We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a latent space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using a simple arithmetic coder after training. Classification accuracy and model compressibility is maximized jointly, with the bitrate--accuracy trade-off specified by a hyperparameter. We evaluate the method on the MNIST, CIFAR-10 and ImageNet classification benchmarks using six distinct model architectures. Our results show that state-of-the-art model compression can be achieved in a scalable and general way without requiring complex procedures such as multi-stage training.