Giant negative magnetoresistance of spin polarons in magnetic semiconductors–chromium-doped Ti2O3 thin films

Epitaxial Cr-doped Ti2O3 films show giant negative magnetoresistance up to –365% at 2 K. The resistivity of the doped samples follows the behavior expected of spin (magnetic) polarons at low temperature. Namely, rho= rho0 exp(T0/T)p, where p = 0.5 in zero field. A large applied field quenches the spin polarons and p is reduced to 0.25 expected for lattice polarons. The formation of spin polarons is an indication of strong exchange coupling between the magnetic ions and holes in the system

Baselines for Identifying Watermarked Large Language Models

We consider the emerging problem of identifying the presence and use of watermarking schemes in widely used, publicly hosted, closed source large language models (LLMs). We introduce a suite of baseline algorithms for identifying watermarks in LLMs that rely on analyzing distributions of output tokens and logits generated by watermarked and unmarked LLMs. Notably, watermarked LLMs tend to produce distributions that diverge qualitatively and identifiably from standard models. Furthermore, we investigate the identifiability of watermarks at varying strengths and consider the tradeoffs of each of our identification mechanisms with respect to watermarking scenario. Along the way, we formalize the specific problem of identifying watermarks in LLMs, as well as LLM watermarks and watermark detection in general, providing a framework and foundations for studying them

Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation

In recent years, knowledge distillation has become a cornerstone of efficiently deployed machine learning, with labs and industries using knowledge distillation to train models that are inexpensive and resource-optimized. Trojan attacks have contemporaneously gained significant prominence, revealing fundamental vulnerabilities in deep learning models. Given the widespread use of knowledge distillation, in this work we seek to exploit the unlabelled data knowledge distillation process to embed Trojans in a student model without introducing conspicuous behavior in the teacher. We ultimately devise a Trojan attack that effectively reduces student accuracy, does not alter teacher performance, and is efficiently constructible in practice.Comment: ICLR 2023 Workshop on Backdoor Attacks and Defenses in Machine Learnin

A Vector Error Correction Model (VECM) Approach in explaining the relationship between Fixed Investment and Economic Growth in Rural China

A rural economy can be affected by fixed investment in a rural area positively or negatively Investment in fixed assets is one of the core measures of capital spending in rural China and the rural economy is a prominent part of china s national economy It is important to study the dynamic relationship between fixed investment and economic growth in rural China Based on time-series data from 1990 to 2016 this paper employed a Vector Error Correction Model VECM approach to lead the stationarity test Cointegration test stability test and granger causality test The result indicated that in the long term Fixed Investment fluctuation promotes GDP growth in rural China while GDP fluctuation is not the source of fixed investment increase in rural Chin

Enhanced hydrogen storage in Ni/Ce composite oxides

The properties of dried (but not calcined) coprecipitated nickel ceria systems have been investigated in terms of their hydrogen emission characteristics following activation in hydrogen. XRD and BET data obtained on the powders show similarities to calcined ceria but it is likely that the majority of the material produced by the coprecipitation process is largely of an amorphous nature. XPS data indicate very little nickel is present on the outermost surface of the particles. Nevertheless, the thermal analytical techniques (TGA, DSC and TPD-MS) indicate that the hydrogen has access to the catalyst present and the nickel is able to generate hydrogen species capable of interacting with the support. Both unactivated and activated materials show two hydrogen emission features, viz. low temperature and high temperature emissions (LTE and HTE, respectively) over the temperature range 50 and 500 °C. A clear effect of hydrogen interaction with the material is that the activated sample not only emits much more hydrogen than the corresponding unactivated one but also at lower temperatures. H2 dissociation occurs on the reduced catalyst surface and the spillover mechanism transfers this active hydrogen into the ceria, possibly via the formation and migration of OH− species. The amount of hydrogen obtained (0.24 wt%) is 10× higher than those observed for calcined materials and would suggest that the amorphous phase plays a critical role in this process. The affiliated emissions of CO and CO2 with that of the HTE hydrogen (and consumption of water) strongly suggests a proportion of the hydrogen emission at this point arises from the water gas shift type reaction. It has not been possible from the present data to delineate between the various hydrogen storage mechanisms reported for ceria

Domestic Value Added and Employment Generated by Chinese Exports: A Quantitative Estimation

We develop an input-output methodology to estimate how Chinese exports affect the country’s total domestic value added (DVA) and employment for 1995 and 2002. Total DVA generated by exports is obtained by subtracting all direct and indirect imported intermediate goods from the gross value of exports, and total employment is obtained by adding all direct and indirect employment generated by exports. To implement these estimations, we use hitherto unpublished Chinese government data to construct several completely new datasets, including an input-output table with separate input-output and employment-output coefficients for processing and non-processing exports. In 2002 (1995), for every US$1,000 dollar of Chinese exports, DVA and employment are estimated to be US$466 (US$545) and 0.242 (0.375) person-year, respectively.Input-output tables, Chinese exports, employment, domestic value added

Consistent Explanations in the Face of Model Indeterminacy via Ensembling

This work addresses the challenge of providing consistent explanations for predictive models in the presence of model indeterminacy, which arises due to the existence of multiple (nearly) equally well-performing models for a given dataset and task. Despite their similar performance, such models often exhibit inconsistent or even contradictory explanations for their predictions, posing challenges to end users who rely on these models to make critical decisions. Recognizing this issue, we introduce ensemble methods as an approach to enhance the consistency of the explanations provided in these scenarios. Leveraging insights from recent work on neural network loss landscapes and mode connectivity, we devise ensemble strategies to efficiently explore the underspecification set -- the set of models with performance variations resulting solely from changes in the random seed during training. Experiments on five benchmark financial datasets reveal that ensembling can yield significant improvements when it comes to explanation similarity, and demonstrate the potential of existing ensemble methods to explore the underspecification set efficiently. Our findings highlight the importance of considering model indeterminacy when interpreting explanations and showcase the effectiveness of ensembles in enhancing the reliability of explanations in machine learning

Nondestructive Handheld Fourier Transform Infrared (FT-IR) Analysis of Spectroscopic Changes and Multivariate Modeling of Thermally Degraded Plain Portland Cement Concrete and its Slag and Fly Ash-Based Analogs

Concrete is by far the world’s most common construction material. Modern concrete is a mixture of industrial pozzolanic cement formulations and aggregate fillers. The former acts as the glue or binder in the final inorganic composite; however, when exposed to a fire the degree of concrete damage is often difficult to evaluate nondestructively. Fourier transform infrared (FT-IR) spectroscopy through techniques such as transmission, attenuated total reflectance, and diffuse reflectance have been rarely used to evaluate thermally damaged concrete. In this paper, we report on a study assessing the thermal damage of concrete via the use of a non-destructive handheld FT-IR with a diffuse reflectance sample interface. In situ measurements can be made on actual damaged areas, without the need for sample preparation. Separate multivariate models were developed to determine the equivalent maximal temperature endured for three common industrial concrete formulations. The concrete mixtures were successfully modelled displaying high predictive power as well as good specificity. This has potential uses in forensic investigation and remediation services particularly for fires in buildings

Magnetic Properties of (γ-Fe₂O₃)₈₀Ag₂₀ Nanocomposites Prepared in Reverse Micelles

The magnetic properties of nanoparticles of gamma-Fe2O3 prepared by reverse micelles have been studied by dc magnetization, transverse ac susceptibility, and Mössbauer spectroscopy. The nanoparticles of gamma-Fe2O3 in the nanocomposite (gamma-Fe2O3)80Ag20 exhibit superparamagnetic behavior. The blocking temperatures determined by the three methods indicate the superparamagnetic nature of (gamma-Fe2O3)80Ag20 above 70-80 K and show correlation with measuring time. The average particle diameter obtained by transmission electron microscopy of the gamma-Fe2O3 particles is ~10 nm and that of the Ag particles is ~20 nm. The average particle size determined from the magnetic analyses for the gamma-Fe2O3 particles is ~12 nm. Mössbauer spectra obtained between 4.2 and 295 K clearly reveal the presence of superparamagnetic relaxation at temperatures above ~80 K. The Mössbauer spectra reveal at most 1% of paramagnetic Fe2+ ions in the 295-K spectrum