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

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.

$t$-aspect subconvexity for $GL(2) times GL(2)$ $L$-function

In this paper we shall prove a subconvexity bound for $GL(2) times GL(2)$ $L$-function in $t$-aspect by using a $GL(1)$ circle method.

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.

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.

SARAS 2 constraints on global 21-cm signals from the Epoch of Reionization

Spectral distortions in the cosmic microwave background over the 40--200~MHz band are imprinted by neutral hydrogen in the intergalactic medium prior to the end of reionization. This signal, produced in the redshift range $z = 6-34$ at the rest frame wavelength of 21 cm, has not been detected yet; and poor understanding of high redshift astrophysics results in a large uncertainty in the expected spectrum. The SARAS~2 radiometer was purposely designed to detect the sky-averaged 21-cm signal. The instrument, deployed at the Timbaktu Collective (Southern India) in April--June 2017, collected 63~hr of science data, which were examined for the presence of the cosmological 21-cm signal. In our previous work the first-light data from SARAS~2 radiometer were analyzed with Bayesian likelihood-ratio tests using $264$ plausible astrophysical scenarios. In this paper we re-examine the data using an improved analysis based on the frequentist approach and forward modeling. We show that SARAS~2 data rejects 27 models, out of which 25 are rejected at a significance $>5sigma$. All the rejected models share the scenario of inefficient heating of the primordial gas by the first population of X-ray sources along with rapid reionization.

SARAS 2: A Spectral Radiometer for probing Cosmic Dawn and the Epoch of Reionization through detection of the global 21 cm signal

The global 21 cm signal from Cosmic Dawn (CD) and the Epoch of Reionization (EoR), at redshifts $z sim 6-30$, probes the nature of first sources of radiation as well as physics of the Inter-Galactic Medium (IGM). Given that the signal is predicted to be extremely weak, of wide fractional bandwidth, and lies in a frequency range that is dominated by Galactic and Extragalactic foregrounds as well as Radio Frequency Interference, detection of the signal is a daunting task. Critical to the experiment is the manner in which the sky signal is represented through the instrument. It is of utmost importance to design a system whose spectral bandpass and additive spurious can be well calibrated and any calibration residual does not mimic the signal. SARAS is an ongoing experiment that aims to detect the global 21 cm signal. Here we present the design philosophy of the SARAS 2 system and discuss its performance and limitations based on laboratory and field measurements. Laboratory tests with the antenna replaced with a variety of terminations, including a network model for the antenna impedance, show that the gain calibration and modeling of internal additives leave no residuals with Fourier amplitudes exceeding 2~mK, or residual Gaussians of 25 MHz width with amplitudes exceeding 2~mK. Thus, even accounting for reflection and radiation efficiency losses in the antenna, the SARAS~2 system is capable of detection of complex 21-cm profiles at the level predicted by currently favoured models for thermal baryon evolution.

t-Aspect Subconvexity Bound for $GL(2)$ L-functions

Let $f $ be a holomorphic Hecke eigenforms or a Hecke-Maass cusp form for the full modular group $ SL(2, mathbb{Z})$. In this paper we shall use circle method to prove the Weyl exponent for $GL(2)$ $L$-functions. We shall prove that [ L left( frac{1}{2} + it right) ll_{f, epsilon} left( 1 + |t|right)^{1/3 + epsilon}, ] for any $epsilon > 0.$

Wlowercase{eyl} lowercase {bound for $p$-power twist of} $GL(2)$ L-lowercase{functions }

Let $f$ be a cuspidal eigenform (holomorphic or Maass) on the full modular group $SL(2, mathbb{Z})$ . Let $chi$ be a primitive character of modulus $P$. We shall prove the following results: 1. Suppose $P = p^r$, where $p$ is a prime and $requiv 0 (textrm{mod} 3)$. Then we have [ Lleft( f otimes chi, frac{1}{2}right) ll_{f, epsilon} P^{1/3 +epsilon}, ] where $epsilon > 0$ is any positive real number. 2. Suppose $chi$ factorizes as $chi= chi_1 chi_2$, where $ chi_i$s are primitive character modulo $P_i$, where $P_i$ are primes, $P^{1/2 -epsilon} ll P_i ll P^{1/2 + epsilon}$ for $i=1,2$ and $P=P_1 P_2$. We have the Burgess bound [ Lleft( f otimes chi, frac{1}{2}right) ll_{f, epsilon} P^{3/8 +epsilon}, ] where $epsilon > 0$ is any positive real number.