LambdaOpt: Learn to Regularize Recommender Models in Finer Levels

Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical variables are usually long-tailed, with the head made up of highly frequent values and a long tail of rare ones. This phenomenon results in the data sparsity issue, making it essential to regularize the models to ensure generalization. The common practice is to employ grid search to manually tune regularization hyperparameters based on the validation data. However, it requires non-trivial efforts and large computation resources to search the whole candidate space; even so, it may not lead to the optimal choice, for which different parameters should have different regularization strengths. In this paper, we propose a hyperparameter optimization method, LambdaOpt, which automatically and adaptively enforces regularization during training. Specifically, it updates the regularization coefficients based on the performance of validation data. With LambdaOpt, the notorious tuning of regularization hyperparameters can be avoided; more importantly, it allows fine-grained regularization (i.e. each parameter can have an individualized regularization coefficient), leading to better generalized models. We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models. Extensive experiments on two public benchmarks demonstrate the superiority of our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201

Shear Behavior of Screw Connection between Cold-Formed Steel and Gypsum Sheathing at Elevated Temperatures

The screw connections between cold-formed steel (CFS) and gypsum sheathing play an important role in the axial and lateral performance of CFS wall panels. Previous researches were mainly focus on the shear behavior of such screw connections at room temperature. This paper carried out a preliminary experimental investigation on the mechanical performance of screw connections with single layer gypsum sheathing at elevated temperatures. Limited to the cavity dimension of the furnace, the single-lap test of CFS coupon -fastener-sheathing connection was adopted and compared with the previous test results of sheathing-to-profile screw connections at room temperature. The failure of screw connections with single layer gypsum sheathing was identified as the breaking of the sheathing edge at elevated temperatures and a sharp decrease of the shear strength was observed beyond 150 °C. In addition, the load-displacement curves of screw connections were well predicted by an exponential model with the post-peak branch at elevated temperatures

Fully Printed High-Frequency Phased-Array Antenna on Flexible Substrate

To address the issues of flexible electronics needed for surface-to-surface, surface-to-orbit, and back-to-Earth communications necessary for manned exploration of the Moon, Mars, and beyond, a room-temperature printing process has been developed to create active, phased-array antennas (PAAs) on a flexible Kapton substrate. Field effect transistors (FETs) based on carbon nanotubes (CNTs), with many unique physical properties, were successfully proven feasible for phased-array antenna systems. The carrier mobility of an individual CNT is estimated to be at least 100,000 sq cm/V(dot)s. The CNT network in solution has carrier mobility as high as 46,770 sq cm/V(dot)s, and has a large current-density carrying capacity of approx. 1,000 mA/sq cm , which corresponds to a high carrying power of over 2,000 mW/ sq cm. Such high carrier mobility, and large current carrying capacity, allows the achievement of high-speed (>100 GHz), high-power, flexible electronic circuits that can be monolithically integrated on NASA s active phasedarray antennas for various applications, such as pressurized rovers, pressurized habitats, and spacesuits, as well as for locating beacon towers for lunar surface navigation, which will likely be performed at S-band and attached to a mobile astronaut. A fully printed 2-bit 2-element phasedarray antenna (PAA) working at 5.6 GHz, incorporating the CNT FETs as phase shifters, is demonstrated. The PAA is printed out at room temperature on 100-mm thick Kapton substrate. Four CNT FETs are printed together with microstrip time delay lines to function as a 2-bit phase shifter. The FET switch exhibits a switching speed of 0.2 ns, and works well for a 5.6-GHz RF signal. The operating frequency is measured to be 5.6 GHz, versus the state-of-the-art flexible FET operating frequency of 52 MHz. The source-drain current density is measured to be over 1,000 mA/sq cm, while the conventional organic FETs, and single carbon nanotube-based FETs, are typically in the mA to mA/sq cm range. The switching voltage used is 1.8 V, while the state-of-the-art flexible FET has a gate voltage around 50 V. The gate voltage can effectively control the source-drain current with an ON-OFF ratio of over 1,000 obtained at a low Vds bias of 1.8 V. The azimuth steering angles of PAA are measured at 0deg, -14.5deg, -30deg, and 48.6deg. The measured far-field patterns agree well with simulation results. The efficiency of the 2-bit 2-element PAA is measured to be 39 percent, including the loss of transmission line, FET switch, and coupling loss of RF probes. With further optimization, the efficiency is expected to be around 50-60 percent

Identification of a cosmid clone containing the Neurospora crassa lys-5 and un-4 genes, isolation of a partial lys-5 cDNA and associated chromosome walking.

The un-4 gene of Neurospora crassa was cloned to determine the limits of a chromosome walk on linkage group VI (LGVI) and to allow analysis of un loci on LGVI. Subsequent analysis identified the lys-5 locus on the same cosmid clone as un-4. We have isolated and sequenced a partial lys-5 cDNA clone and initiated a chromosome walk from the lys-5, un-4 cosmid clone

Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training

Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full forward and backward pass over the entire model. We propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model and plugged into the aligned large model for rapid cross-lingual transfer. We explore two approaches to learn mini-models: MiniJoint, which jointly pretrains the primary model and the mini-model using a single transformer with a secondary MLM head at a middle layer; and MiniPost, where we start from a regular pretrained model, build a mini-model by extracting and freezing a few layers, and learn a small number of parameters on top. Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the performance of the standard approach using 2.3x less compute on average.Comment: Findings of ACL 2023 Camera Read