Recovery Rates and Macroeconomic Conditions: The Role of Loan Covenants

For U.S. firms from 1988 to 2007, firms with stricter loan covenants had higher firm-level default recovery rates. Covenants were stricter, moreover, when set during downturns in the business cycle. This implies a negative dependence of recovery rates on lagged macroeconomic conditions. That is, bank loan contracts established in economic recessions have tight covenants, leading later to higher recovery rates. My empirical evidence suggests that private creditors have significant influence on firms' bankruptcy decisions through the channel of covenants in debt contracts.Recovery rate, Bankruptcy, Loan covenant, Creditor control, Business cycle

Design and optimization of joint iterative detection and decoding receiver for uplink polar coded SCMA system

SCMA and polar coding are possible candidates for 5G systems. In this paper, we firstly propose the joint iterative detection and decoding (JIDD) receiver for the uplink polar coded sparse code multiple access (PC-SCMA) system. Then, the EXIT chart is used to investigate the performance of the JIDD receiver. Additionally, we optimize the system design and polar code construction based on the EXIT chart analysis. The proposed receiver integrates the factor graph of SCMA detector and polar soft-output decoder into a joint factor graph, which enables the exchange of messages between SCMA detector and polar decoder iteratively. Simulation results demonstrate that the JIDD receiver has better BER performance and lower complexity than the separate scheme. Specifically, when polar code length N=256 and code rate R=1/2 , JIDD outperforms the separate scheme 4.8 and 6 dB over AWGN channel and Rayleigh fading channel, respectively. It also shows that, under 150% system loading, the JIDD receiver only has 0.3 dB performance loss compared to the single user uplink PC-SCMA over AWGN channel and 0.6 dB performance loss over Rayleigh fading channel

Genome-Wide Identification and Evolutionary Analysis of the Animal Specific ETS Transcription Factor Family

The ETS proteins are a family of transcription factors (TFs) that regulate a variety of biological processes. We made genome-wide analyses to explore the classification of the ETS gene family. We identified 207 ETS genes which encode 321 ETS TFs from ten animal species. Of the 321 ETS TFs, 155 contain only an ETS domain, about 50% contain a ETS_PEA3_N or a SAM_PNT domain in addition to an ETS domain, the rest (only four) contain a second ETS domain or a second ETS_PEA3_N domain or an another domain (AT_hook or DNA_pol_B). A Neighbor-Joining phylogenetic tree was constructed using the amino acid sequences of the ETS domain of the ETS TFs. The results revealed that the ETS genes of the ten species can be divided into two distinct groups. Group I contains one nematode ETS gene and 18 vertebrate animal ETS genes. Group II contains the majority of the ETS TFs and can be further divided into eleven subgroups. The sequence motifs outside the DNA-binding domain and the conservation of the exon-intron structural patterns of the ETS TFs in human, cattle, and chicken further support the phylogenetic classification among these ETS TFs. Extensive duplication of the ETS genes was found in the genome of each species. The duplicated ETS genes account for ~69% of the total of ETS genes. Furthermore, we also found there are ETS gene clusters in all of the ten animal species. Statistical analysis of the Gene Ontology annotations of the ETS genes showed that the ETS proteins tend to be related to RNA biosynthetic process, biopolymer metabolic process and macromolecule metabolic process expected from the common GO categories of transcriptional factors. We also discussed the functional conservation and diversification of ETS TFs

Experimental investigation on the surface and subsurface damages characteristics and formation mechanisms in ultra-precision grinding of SiC

Surface and subsurface damages appear inevitably in the grinding process, which will influence the performance and lifetime of the machined components. In this paper, ultra-precision grinding experiments were performed on Reaction-bonded Silicon Carbide (RB-SiC) ceramics to investigate surface and subsurface damages characteristics and formation mechanisms in atomic scale. The surface and subsurface damages were measured by a combination of scanning electron microscopy (SEM), atomic force microscopy (AFM), raman spectroscopy and transmission electron microscope (TEM) techniques. Ductile-regime removal mode is achieved below critical cutting depth, exhibiting with obvious plough stripes and pile-up. The brittle fracture behavior is noticeably influenced by the microstructures of RB-SiC such as impurities, phase boundary and grain boundary. It was found that subsurface damages in plastic zone mainly consist of stacking faults (SFs), twins and limited dislocations. No amorphous structure can be observed in both 6H-SiC and Si particles in RB-SiC ceramics. Additionally, with the aid of high resolution TEM analysis, SFs and twins were found within the 6H-SiC closed packed plane i.e. (0001). At last, based on the SiC structure characteristic, the formation mechanisms of SFs and twins was discussed, and a schematic model was proposed to clarify the relationship between plastic deformation induced defects and brittle fractures

Solving Inverse Problems with Reinforcement Learning

In this paper, we formally introduce, with rigorous derivations, the use of reinforcement learning to the field of inverse problems by designing an iterative algorithm, called REINFORCE-IP, for solving a general type of non-linear inverse problem. By choosing specific probability models for the action-selection rule, we connect our approach to the conventional regularization methods of Tikhonov regularization and iterative regularization. For the numerical implementation of our approach, we parameterize the solution-searching rule with the help of neural networks and iteratively improve the parameter using a reinforcement-learning algorithm~-- REINFORCE. Under standard assumptions we prove the almost sure convergence of the parameter to a locally optimal value. Our work provides two typical examples (non-linear integral equations and parameter-identification problems in partial differential equations) of how reinforcement learning can be applied in solving non-linear inverse problems. Our numerical experiments show that REINFORCE-IP is an efficient algorithm that can escape from local minimums and identify multi-solutions for inverse problems with non-uniqueness.Comment: 33 pages, 10 figure