Competence-Based Analysis of Language Models

Despite the recent success of large pretrained language models (LMs) on a variety of prompting tasks, these models can be alarmingly brittle to small changes in inputs or application contexts. To better understand such behavior and motivate the design of more robust LMs, we propose a general experimental framework, CALM (Competence-based Analysis of Language Models), where targeted causal interventions are utilized to damage an LM's internal representation of various linguistic properties in order to evaluate its use of each representation in performing a given task. We implement these interventions as gradient-based adversarial attacks, which (in contrast to prior causal probing methodologies) are able to target arbitrarily-encoded representations of relational properties, and carry out a case study of this approach to analyze how BERT-like LMs use representations of several relational properties in performing associated relation prompting tasks. We find that, while the representations LMs leverage in performing each task are highly entangled, they may be meaningfully interpreted in terms of the tasks where they are most utilized; and more broadly, that CALM enables an expanded scope of inquiry in LM analysis that may be useful in predicting and explaining weaknesses of existing LMs

A climate-dependent global model of ammonia emissions from chicken farming

Ammonia (NH3) has significant impacts on the environment, which can influence climate and air quality and cause acidification and eutrophication in terrestrial and aquatic ecosystems. Agricultural activities are the main sources of NH3 emissions globally. Emissions of NH3 from chicken farming are highly dependent on climate, affecting their environmental footprint and impact. In order to investigate the effects of meteorological factors and to quantify how climate change affects these emissions, a process-based model, AMmonia–CLIMate–Poultry (AMCLIM–Poultry), has been developed to simulate and predict temporal variations in NH3 emissions from poultry excretion, here focusing on chicken farms and manure spreading. The model simulates the decomposition of uric acid to form total ammoniacal nitrogen, which then partitions into gaseous NH3 that is released to the atmosphere at an hourly to daily resolution. Ammonia emissions are simulated by calculating nitrogen and moisture budgets within poultry excretion, including a dependence on environmental variables. By applying the model with global data for livestock, agricultural practice and meteorology, we calculate NH3 emissions from chicken farming on a global scale (0.5∘ resolution). Based on 2010 data, the AMCLIM–Poultry model estimates NH3 emissions from global chicken farming of 5.5 ± 1.2 Tg N yr−1, about 13 % of the agriculture-derived NH3 emissions. Taking account of partial control of the ambient environment for housed chicken (layers and broilers), the fraction of excreted nitrogen emitted as NH3 is found to be up to 3 times larger in humid tropical locations than in cold or dry locations. For spreading of manure to land, rain becomes a critical driver affecting emissions in addition to temperature, with the emission fraction being up to 5 times larger in the semi-dry tropics than in cold, wet climates. The results highlight the importance of incorporating climate effects into global NH3 emissions inventories for agricultural sources. The model shows increased emissions under warm and wet conditions, indicating that climate change will tend to increase NH3 emissions over the coming century

Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP

In recent RAG approaches, rerankers play a pivotal role in refining retrieval accuracy with the ability of revealing logical relations for each pair of query and text. However, existing rerankers are required to repeatedly encode the query and a large number of long retrieved text. This results in high computational costs and limits the number of retrieved text, hindering accuracy. As a remedy of the problem, we introduce the Efficient Title Reranker via Broadcasting Query Encoder, a novel technique for title reranking that achieves a 20x-40x speedup over the vanilla passage reranker. Furthermore, we introduce Sigmoid Trick, a novel loss function customized for title reranking. Combining both techniques, we empirically validated their effectiveness, achieving state-of-the-art results on all four datasets we experimented with from the KILT knowledge benchmark

A dynamical process-based model AMmonia–CLIMate v1.0 (AMCLIM v1.0) for quantifying global agricultural ammonia emissions – Part 1: Land module for simulating emissions from synthetic fertilizer use

Ammonia (NH3) emissions mainly originate from agricultural practices and can have multiple adverse impacts on the environment. With the substantial increase of synthetic fertilizer use over the past decades, volatilization of NH3 has become a major loss of N applied to land. Since NH3 can be strongly influenced by both environmental conditions and local management practices, a better estimate of NH3 emissions from fertilizer use requires improved understanding of the relevant processes. This study describes a new process-based model, AMmonia–CLIMate (AMCLIM), for quantifying agricultural NH3 emissions. More specifically, the present paper focuses on the development of a module (AMCLIM–Land) that is used for simulating NH3 emissions from synthetic fertilizer use. (Other modules, together termed as AMCLIM-Livestock, simulate NH3 emissions from agricultural livestock, are described in Part 2). AMCLIM–Land dynamically models the evolution of N species in soils by incorporating the effects of both environmental factors and management practices to determine the NH3 emissions released from the land to the atmosphere. Based on simulations for 2010, NH3 emissions resulting from the synthetic fertilizer use are estimated at 15.0 Tg N yr-1, accounting for around 17 % of applied fertilizer N. Strong spatial and seasonal variations are found. Higher emissions typically occur in agricultural intensive countries (such as China, India, Pakistan and US), and mostly reach the maximum in the summer season. Volatilization rates indicate that hotter environments can result in more N lost due to NH3 emissions, and show how other factors including soil moisture and pH can greatly affect volatilization of NH3. The AMCLIM model also allows estimation of how application techniques and fertilizer type have impacts on the NH3 emissions, pointing to the importance of improving management practice to tackle nutrient loss and of appropriate data-gathering to record management practices internationally

The EU and the Catalan Crisis

List of all differentially expressed proteins (n = 45) identified in the study. (XLSX 11 kb

Analysis of atmospheric ammonia over South and East Asia based on the MOZART-4 model and its comparison with satellite and surface observations

Limited availability of atmospheric ammonia (NH3) observations limits our understanding of controls on its spatial and temporal variability and its interactions with the ecosystem. Here we used the Model for Ozone and Related chemical Tracers version 4 (MOZART-4) global chemistry transport model and the Hemispheric Transport of Air Pollution version 2 (HTAP-v2) emission inventory to simulate global NH3 distribution for the year 2010. We presented a first comparison of the model with monthly averaged satellite distributions and limited ground-based observations available across South Asia. The MOZART-4 simulations over South Asia and East Asia were evaluated with the NH3 retrievals obtained from the Infrared Atmospheric Sounding Interferometer (IASI) satellite and 69 ground-based monitoring stations for air quality across South Asia and 32 ground-based monitoring stations from the Nationwide Nitrogen Deposition Monitoring Network (NNDMN) of China. We identified the northern region of India (Indo-Gangetic Plain, IGP) as a hotspot for NH3 in Asia, both using the model and satellite observations. In general, a close agreement was found between yearly averaged NH3 total columns simulated by the model and IASI satellite measurements over the IGP, South Asia (r=0.81), and the North China Plain (NCP), East Asia (r=0.90). However, the MOZART-4-simulated NH3 column was substantially higher over South Asia than East Asia, as compared with the IASI retrievals, which show smaller differences. Model-simulated surface NH3 concentrations indicated smaller concentrations in all seasons than surface NH3 measured by the ground-based observations over South and East Asia, although uncertainties remain in the available surface NH3 measurements. Overall, the comparison of East Asia and South Asia using both MOZART-4 model and satellite observations showed smaller NH3 columns in East Asia compared with South Asia for comparable emissions, indicating rapid dissipation of NH3 due to secondary aerosol formation, which can be explained by larger emissions of acidic precursor gases in East Asia

Design and realization of low dropout voltage regulator and voltage reference in deep-submicron CMOS for emerging Internet-of-Things and satellites

This Ph.D. program pertains to the design and realization of two fundamental analog circuits in deep-submicron CMOS – a Low DropOut voltage regulator (LDO) and a voltage reference. The target applications for the LDO and the voltage reference are respectively the emerging Internet-of-Things (IoTs) and both the said IoTs and satellites. Some of the most imperative considerations for the design of contemporary Integrated Circuits (ICs) and Systems-on-Chip (SoCs) for IoTs include a small form factor, complex functionality, yet low power and low cost. These attributes are often realized by employing deep-submicron CMOS processes, e.g., 65nm, resulting in a somewhat challenging operating environment, including a low supply voltage, large thermal gradient, severe noise (coupling), etc. For satellites operating in an extraterrestrial environment, the operating environment for ICs/SoCs is even more challenging due to the extended temperature range and the need for immunity to radiation effects, i.e., ‘radiation-hardened’.Doctor of Philosoph

Quantifying climate-dependent ammonia emissions from global agriculture: from development of the AMmonia–CLIMate (AMCLIM) model to its application and implications

Ammonia (NH3) is one of the primary forms of reactive nitrogen and can negatively affect the environment and human health. It has adverse impacts on air, water, soil quality and ecosystems. Emissions of NH3 mainly originate from agricultural activities and are found to be strongly dependent on environmental conditions. Current emission inventories often consider the effects of environmental factors in a limited way. To address this deficiency in existing estimates, a dynamic, process based emissions model, AMmonia–CLIMate (AMCLIM) has been developed to quantify agricultural NH3 emissions. AMCLIM simulates important physical, chemical and biological processes that are sensitive to climatic conditions in agricultural systems, and focuses on major livestock farming and synthetic fertilizer use. The model has been applied to different scales, with modelled results evaluated by comparison with site measurements, showing close agreement. When applied at the global scale, AMCLIM operates at high spatial and temporal resolution, and AMCLIM is thought to be the first model that simulates NH3 emissions from all individual sectors using a consistent process based modelling approach, with high levels of detail. For the year 2010, global agricultural NH3 emissions estimated by AMCLIM are 44.9±4.4 Tg N yr-1, equivalent to 22±2 % of agricultural nitrogen input (synthetic fertilizer and livestock excreta) being lost through NH3 volatilization. The global estimates of AMCLIM are consistent with other models and studies. Around 1/3 of the NH3 emissions result from synthetic fertilizer use, with 2/3 associated with livestock farming (including housing, manure management, land application of manure and grazing). China, India, US, Brazil and Pakistan result in the largest estimated emissions, together accounting for nearly 60 % of global NH3 emissions. Cattle are the largest emitter group among livestock, followed by pigs, chicken, sheep and goats. Emissions of NH3 not only show large spatial variations but also exhibit significant seasonal variation. The highest estimated NH3 emissions are found in July, mainly driven by the planting season and high temperatures in the northern hemisphere. The impacts of temperature, wind speed and water availability on NH3 volatilization have been investigated. Increasing temperature and wind speed facilitates volatilization to cause more NH3 emissions, with temperature normally being the most critical factor especially under cold conditions. A global sensitivity test to temperature indicates that annual NH3 emissions may increase by around 7 % due to a (uniform) 2 °C warming, compared to the base value of year 2010. Ammonia emissions tend to be larger under drier conditions, but wetter soils can either result in higher or lower emissions, depending on complex interactions with other variables. Soil pH is also a critical factor as alkaline soils typically lead to more intense volatilization. Mitigation measures have been simulated and have been found to be effective in reducing NH3 emissions. These include decreasing nitrogen application rates and improving livestock feeding materials, covering stored manure and better land application techniques (e.g., incorporation and deep placement). It is estimated that a potential 40 % abatement of global NH3 emissions can be achieved when applying a suite of the tested measures. Using AMCLIM, it is estimated that NH3 emissions have increased from 39.8 Tg N yr-1 in 2000 to 45.2 Tg N yr-1 in 2018, and future emissions by 2100 are projected to go up to 51 to 55 Tg N yr-1 due to warming alone (with 2018 activity) and can reach 59 to 102 Tg N yr-1 when also combined with growing livestock numbers and synthetic fertilizer usage. It is suggested that climate change and food production pose risks for future agricultural NH3 emissions at different spatial scales. Regional and local environment and agricultural systems may suffer the consequences of the warming effect, while the globe may face challenges due to increased food production

Design and analysis of ultra-robust analog library cells for aerospace applications

Radiation effects which may degrade integrated circuit performance significantly make it challengeable for IC designer to design circuits using commercial CMOS process for aerospace applications in old days. However, thanks to the fast development of commercial CMOS technology, nowadays, deep submicron devices with ultra thin silicon oxide have got intrinsic hardness towards radiation effects. With the help of Radiation Hardened by Design (RHBD) approach, circuit which is robust to radiation environment can be implemented using commercial deep submicron CMOS process.Bachelor of Engineerin