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1. Forward Guidance and Household Expectations

   https://doi.org/10.1093/jeea/jvad003  

   Coibion, Olivier; Georgarakos, Dimitris; Gorodnichenko, Yuriy; Weber, Michael (January 2023, Journal of the European Economic Association)

   Abstract We compare causal effects of forward guidance about future interest rates on households’ expectations of inflation and nominal mortgage rates to the effects of communication about inflation in a randomized control trial using more than 20,000 U.S. consumers in the Nielsen Homescan Panel. We elicit consumers’ expectations, and then provide 22 different forms of information regarding past, current, and/or future interest rates and inflation. Information treatments about current or future interest rates all have similar and offsetting effects on interest rate and inflation expectations, yielding limited pass-through into perceived real rates. Information about mortgage rates has much more powerful effects on interest rate perceptions, with no offsetting effects on inflation expectations, thereby delivering much larger changes in perceived real rates. Revisions in perceived real rates causally lead to changes in the ex-post purchases of durable goods by households. 

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2. FFCL: Forward-Forward Net with Cortical Loops, Training and Inference on Edge Without Backpropogation

   https://doi.org/10.1145/3649476.3660391  

   Karkehabadi, Ali; Homayoun, Houman; Sasan, Avesta (June 2024, ACM)

   The Forward-Forward Learning (FFL) algorithm is a recently proposed solution for training neural networks without needing memory-intensive backpropagation. During training, labels accompany input data, classifying them as positive or negative inputs. Each layer learns its response to these inputs independently. In this study, we enhance the FFL with the following contributions: 1) We optimize label processing by segregating label and feature forwarding between layers, enhancing learning performance. 2) By revising label integration, we enhance the inference process, reduce computational complexity, and improve performance. 3) We introduce feedback loops akin to cortical loops in the brain, where information cycles through and returns to earlier neurons, enabling layers to combine complex features from previous layers with lower-level features, enhancing learning efficiency. 

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3. Forward stability and model path selection

   https://doi.org/10.1007/s11222-024-10395-8  

   Kissel, Nicholas; Mentch, Lucas (April 2024, Statistics and Computing)

   Jasra, Ajay (Ed.)

   Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. This work develops the idea of forward stability and proposes a novel, computationally-efficient approach to finding collections of accurate models we refer to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss. 

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4. Compress-and-Forward via Multilevel Coding

   Wan, H.; Host-Madsen, A.; Nosratinia, A. (July 2019, IEEE International Symposium on Information Theory)

   We investigate the performance of discrete (coded) modulations in the full-duplex compress-forward relay channel using multilevel coding. We numerically analyze the rates assigned to component binary codes of all levels. LDPC codes are used as the component binary codes to provide error protection. The compression at the relay is done via a nested scalar quantizer whose output is mapped to a codeword through LDPC codes. A compound Tanner graphical model and information-exchange algorithm are described for joint decoding of both messages sent from the source and relay. Simulation results show that the performance of the proposed system based on multilevel coding is better than that based on BICM, and is separated from the SNR threshold of the known CF achievable rate by two factors consisting approximately of the sum of the shaping gain (due to scalar quantization) and the separation of the LDPC code implementation from AWGN capacity. 

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5. Valleytronics: Opportunities, Challenges, and Paths Forward

   https://doi.org/10.1002/smll.201801483  

   Vitale, Steven A.; Nezich, Daniel; Varghese, Joseph O.; Kim, Philip; Gedik, Nuh; Jarillo‐Herrero, Pablo; Xiao, Di; Rothschild, Mordechai (August 2018, Small)

   Abstract A lack of inversion symmetry coupled with the presence of time‐reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at theKandK′points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes, or excitons as a completely new paradigm in information processing. The opportunities and challenges associated with manipulation of the valley degree of freedom for practical quantum and classical information processing applications were analyzed during the 2017 Workshop on Valleytronic Materials, Architectures, and Devices; this Review presents the major findings of the workshop. 

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