Miracle-dz/UniFAR-FTUs huggingface dataset:
Segment modeling alternatives for continuous speech recognition

This dissertation presents alternative parametric statistical models of phonetically-based segments for use in continuous speech recognition (CSR). A categorization of segment modeling approaches is proposed according to two characteristics: the assumed form of the probability distribution and the representation chosen for segment observations. The question of distribution form divides models into two groups: those based on conditional probability densities of feature given label and those using a posteriori probabilities of label given feature. The second characteristic concerns whether a model uses a variable or fixed-length representation of observed speech segments. The choices for both characteristics have important implications, particularly for context modeling and score normalization. In this work, specific segment models are developed in order to understand the benefits and limitations that follow from these choices. Mixture distributions are a particular type of conditional density with appealing modeling properties. Under a special case of segment models using variable-length representations and conditional densities, various forms of Gaussian mixture models are examined for the individual samples of the feature sequence. Within this framework, a systematic comparison of both existing and novel mixture modeling techniques is conducted. Parameter-tying alternatives for frame-level mixtures are explored and good performance is demonstrated with this approach. Within the conditional-density variable-length framework, a generalization of mixture distributions that captures properties of the complete segment is proposed in the form of a segment-level mixture model. This approach models intra-segment correlation indirectly using a mixture of segment-length models, each of which uses conditionally independent time samples. Parameter estimation formulae are derived and the model is explored experimentally. The alternative assumption of modeling based on a posteriori probabilities is examined through the development of a recognition formalism using classification and segmentation scoring. Posterior distributions have been less well studied than conditional densities in the context of CSR, and this work introduces a theoretically consistent, segment-level posterior distribution model using context-dependent models. Issues concerning fixed versus variable-length representations and segmentation scoring are explored experimentally. Finally, some general conclusions are drawn concerning the practical and theoretical trade-offs for the models examined.
Effect of Nitrogen Rates and Flowering Dates on Fiber Quality of Cotton (Gossypium hirsutum L.)

Nitrogen (N) supply during boll setting and maturation period of cotton can be critical in determining fiber quality. The study aims to investigate the relationship between N rates and formation of fiber length, strength, maturity and micronaire in bolls with different flowering dates. Field experiments were conducted using two cotton cultivars (Kemian 1 and NuCOTN 33B) and three N fertilization rates (0, 240, and 480 kg N ha -1 ) in Nanjing and Xuzhou in 2005 and in Anyang in 2007, China. The fiber length, strength, maturity, micronaire, and N concentration per unit area (N A) of the subtending leaf of cotton boll were analyzed. N fertilization rates, flowering dates, and N fertilization rates × flowering dates significantly (P ≤ 0.05) affected N A and the formation of fiber length, strength, maturity and micronaire. N fertilization rates affected fiber quality by influencing NA which was significantly related to rate and duration of the fiber quality formation process. The optimal N A for fiber quality formation was varied. For bolls flowering before August 25, when mean daily temperature during boll maturation period (MDT BMP ) was higher than 21°C, NA in the 240 kg N ha -1
Evaluation of Interpretability Methods and Perturbation Artifacts in Deep Neural Networks

The challenge of interpreting predictions from deep neural networks has prompted the development of numerous interpretability methods. Many of interpretability meth-ods attempt to quantify the importance of input features with respect to the class probabilities, and are called importance estimators or saliency maps. A popular approach to evaluate such interpretability methods is to perturb input features deemed important for predictions and observe the decrease in accuracy. However, perturbation-based evaluation methods may confound the sources of accuracy degradation. We conduct computational experiments that allow to empirically estimate the ﬁdelity of interpretability methods and the contribution of perturbation artifacts. All considered importance estimators clearly outperform a random baseline, which contradicts the ﬁndings of ROAR [1]. We further compare our results to the crop-and-resize evaluation framework [2], which are largely in agreement. Our study suggests that we can estimate the impact of artifacts and thus empirically evaluate interpretability methods without retraining.
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization

A conventional wisdom in statistical learning is that large models require strong regularization to prevent overﬁtting. Here we show that this rule can be violated by linear regression in the underdetermined n (cid:28) p situation under realistic conditions. Using simulations and real-life high-dimensional datasets, we demonstrate that an explicit positive ridge penalty can fail to provide any improvement over the minimum-norm least squares estimator. Moreover, the optimal value of ridge penalty in this situation can be negative. This happens when the high-variance directions in the predictor space can predict the response variable, which is often the case in the real-world high-dimensional data. In this regime, low-variance directions provide an implicit ridge regularization and can make any further positive ridge penalty detrimental. We prove that augmenting any linear model with random covariates and using minimum-norm estimator is asymptotically equivalent to adding the ridge penalty. We use a spiked covariance model as an analytically tractable example and prove that the optimal ridge penalty in this case is negative when n (cid:28) p .
How Segment Anything Model (SAM) Boost Medical Image Segmentation?

Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introduction of the Segment Anything Model (SAM), the prompt-driven paradigm has entered the realm of image segmentation, bringing with a range of previously unexplored capabilities. However, it remains unclear whether it can be applicable to medical image segmentation due to the significant differences between natural images and medical images. In this work, we summarize recent efforts to extend the success of SAM to medical image segmentation tasks, including both empirical benchmarking and methodological adaptations, and discuss potential future directions for SAM in medical image segmentation. Although directly applying SAM to medical image segmentation cannot obtain satisfying performance on multi-modal and multi-target medical datasets, many insights are drawn to guide future research to develop foundation models for medical image analysis. We also set up a continuously updated paper list and open-source project summary to boost the research on this topic at https://github.com/YichiZhang98/SAM4MIS.
Experiences of Internet traffic monitoring with tstat

Since the early days of the Internet, network traffic monitoring has always played a strategic role in understanding and characterizing users¿ activities. In this article, we present our experience in engineering and deploying Tstat, an open source passive monitoring tool that has been developed in the past 10 years. Started as a scalable tool to continuously monitor packets that flow on a link, Tstat has evolved into a complex application that gives network researchers and operators the possibility to derive extended and complex measurements thanks to advanced traffic classifiers. After discussing Tstat capabilities and internal design, we present some examples of measurements collected deploying Tstat at the edge of several ISP networks in past years. While other works report a continuous decline of P2P traffic with streaming and file hosting services rapidly increasing in popularity, the results presented in this article picture a different scenario. First, P2P decline has stopped, and in the last months of 2010 there was a counter tendency to increase P2P traffic over UDP, so the common belief that UDP traffic is negligible is not true anymore. Furthermore, streaming and file hosting applications have either stabilized or are experiencing decreasing traffic shares. We then discuss the scalability issues software-based tools have to cope with when deployed in real networks, showing the importance of properly identifying bottlenecks.
Grain-size evolution of polar firn: a new empirical grain growth parameterization based on X-ray microcomputer tomography measurements

Abstract Firn microstructure properties from six different sites in Greenland and Antarctica are investigated by means of X-ray microcomputer tomography. The optical effective radius is calculated from the specific surface area (SSA) and used as a measure of grain size. It is shown that the recently introduced spherical approximation of firn grains using the effective radius Reff is representative of grain size in the microwave frequency region. The measured profiles show the well-known increase of grain size with depth at all sites, where the increase is largest at near-surface depths. A large variability in grain size on the decimeter-to-centimeter scale as a result of different grain properties of single layers is superimposed on the overall trend at each site. A simple empirical parameterization of grain-size evolution is developed which allows the rapid grain growth in the uppermost layers of the firn to be predicted. The growth is driven by strong seasonal and diurnal temperature gradients. The model can be used to simulate grain-size profiles required by models of firn/microwave interaction (e.g. for retrieval of accumulation rates from satellite microwave sensors) in a more realistic fashion.
Glaciological properties of the Antarctic ice sheet from RADARSAT-1 synthetic aperture radar imagery

Abstract The RADARSAT-1 Antarctic Mapping Project (RAMP) is a partnership between the Canadian Space Agency and the US. National Aeronautics and Space Administration. The goal of the project is to create the first complete high-resolution radar mosaic of all of Antarctica for studies of Antarctic glaciology, geology coastal processes and climate. The major participants in RAMP are the Ohio State University the Alaska SAR Facility, the Jet Propulsion Laboratory, Pasadena, GA, and Vexcel Corporation. Phase one of the project was the Antarctic Imaging Gampaign-1 (AIG) which occurred during the period 9 September-20 October 1997. The AIG relied on the abilities of the Canadian RADARSAT-1 to rotate in orbit and to image with a variety of look angles to acquire imagery from the Antarctic coast to the pole. Its primary goal was the acquisition of image data. The nearly flawless execution of the mission also enabled additional collections of exact repeat orbit data. These data, covering a large part of the interior Antarctic, are potentially suitable for interferometric analysis of surface velocity. This paper describes the AIG and the various datasets obtained! It also reviews early scientific findings on the large-scale characteristics of the ice sheet including ice divides and ice streams. The overall conclusion is that these data, which constitute complete high-resolution microwave coverage, represent a new view of the Antarctic, revealing in considerable detail the glaciology, coastal processes and geology of the Southern Continent.
Articulating the Task at Hand and Making Information Relevant to It

Building truly "context-aware" environments presents a greater challenge than using data transmitted by ubiquitous computing devices: It requires shared understanding between humans and their computational environments. This essay articulates some specific problems that can be addressed by representing context. It explores the unique possibilities of design environments that model and represent domains, tasks, design guidelines, solutions, and their rationale, and the larger context of such environments embedded in the physical world. Context in design is not a fixed entity sensed by devices, but it is emerging and it is unbounded. Context-aware environments must address these challenges to be more supportive to all stakeholders who design and evolve complex design artifacts.
Distributed damage, faulting, and friction

We present a formulation for mechanical modeling of geological processes in the seismogenic crust using damage rheology. The seismogenic layer is treated as an elastic medium where distributed damage, modifying the elastic stiffness, evolves as a function of the deformation history. The model damage rheology is based on thermodynamic principles and fundamental observations of rock deformation. The theoretical analysis leads to a kinetic equation for damage evolution having two principal coefficients. The first is a criterion for the transition between strength degradation and recovering (healing), and is related to friction. The second is a rate coefficient of damage evolution which can have different values or functional forms for positive (degradation) and negative (healing) evolution. We constrain these coefficients by fitting model predictions to laboratory data, including coefficient of friction in sawcut setting, intact strength in fracture experiments, first yielding in faulting experiments under three-dimensional strain, onset and evolution of acoustic emission, and dynamic instability. The model damage rheology accounts for many realistic features of three-dimensional deformation fields associated with an earthquake cycle. These include aseismic deformation, gradual strength degradation, development of process zones and branching faults around high-damage areas, strain localization, brittle failure, and state dependent friction. Some properties of the model damage rheology (e.g., cyclic stick-slip behavior with possible accompanying creep) are illustrated with simplified analytical results. The developments of the paper provide an internally consistent framework for simulating long histories of crustal deformation, and studying the coupled evolution of regional earthquakes and faults. This is done in a follow up work.
Paleoecology and geochronology of glacial Lake Hind during the Pleistocene–Holocene transition: A context for Folsom surface finds on the Canadian Prairies

Stratigraphic and paleoecologic (palynomorph, macrobotanical) data obtained from a cutbank of the Souris River in southwestern Manitoba establish some fundamental parameters of Folsom land‐use in association with a proglacial lake on the Canadian Prairies. By dating the regression of glacial Lake Hind, we observed that recorded Folsom sites are restricted to areas of the Hind basin drained shortly before 10,400 yr B.P. This pattern may therefore record the interception of seasonal resources on recently‐drained proglacial lake surfaces. Based on paleovegetation reconstructions, we note that these surfaces were rapidly colonized by emergent and aquatic vegetation following regression, generating a viable resource base for Folsom hunter‐gatherers. However, low plant productivity and diversity may have greatly limited the extent to which this locale was exploited, in contrast to nonperiglacial regions on the Plains. We also suggest that wetland plant succession during the Pleistocene‐Holocene transition was due, at least locally, to climate‐forced fluctuations in groundwater levels. © 2003 Wiley Periodicals, Inc.
Strong motion and broadband teleseismic analysis of the 1991 Sierra Madre, California, earthquake

Short period and broadband teleseismic waveform data and three-component strong motion records were analyzed to obtain the source parameters of the 1991 Sierra Madre earthquake. Close-in strong motion velocity records (analyzed from 5 s to 5 Hz) show two distinct pulses about 0.35 s apart, requiring some rupture complexity. The near-field shear wave displacement pulse from this event has a relatively short duration (about 1 s) for the magnitude of the event, requiring a particularly high average stress drop. To further constrain the rupture process, the data were used in a finite fault source inversion to determine the temporal and spatial distribution of slip. We chose a fault plane orientation striking S 62°W and dipping 50° toward the northwest as required by the distribution of aftershocks, the first motion mechanism and the teleseismic bodywave point source inversion. In addition to the aftershock locations, depth constraints are provided by teleseismic short period and broadband recordings which require a centroid depth of 10–11 km. Our inverse modeling results indicate that both the teleseismic and strong motion data sets can be fit with a compact rupture area, about 12 km2, southwest and up-dip from the hypocenter. The average slip is approximately 50 cm, and the maximum slip is 120 cm. The seismic moment obtained from either of the separate data sets or both sets combined is about 2.8 ± 0.3 × 1024 dyn cm and the potency is 0.01 km3. Using the area of significant slip estimated from the finite fault inversion, the resulting stress drop is on the order of 150–200 bars.
Radiocarbon content of pre‐bomb marine mollusks and variations in the 14C Reservoir age for coastal areas of the Barents and Kara Seas, Russia

Fourteen mollusks, collected alive between 1900 and 1945 from the Russian Barents and Kara seas, were analyzed by AMS 14C dating to evaluate variations in the 14C marine reservoir for arctic coastal sites, which is important for correcting ages in paleoenvironmental time‐series and advancing understanding of the exchange of carbon. The 14C ages on the mollusks reveal a range of marine reservoir values (R(t)) from 159 14C yr to 764 14C yr. The oldest R(t) values of 764 to 620 14C yr are for the bivalve Portlandia arctica, which often inhabit cold and low salinity waters and muddy substrates. The depleted 14C content for this bivalve reflects possibly the incorporation of old carbon from freshwater inputs and/or the consumption of old organic matter from the underlying sediments and pore waters. Other mollusks with sessile habitats and pelagic food sources gave significantly lower R(t) values between 159 and 344 14C yr. The youngest R(t) values indicate enrichment in 14C and may partially reflect enhanced transfer of 14C‐enriched CO2 from the atmosphere to the ocean surface with wind‐generated wave agitation. This study underscores that a variety of processes can lead to variable 14C depletion and enrichment of surface waters yielding a ca. 600 year age span for contemporaneous arctic mollusks. There may be added uncertainty in the 14C reservoir correction for deposit‐feeder species such as Portlandia sp. and perhaps for certain benthic foraminifera (e.g. Nonion labradoricum) because these taxa often incorporate old organic matter from the substrate. A reservoir correction of ≥700 years may be more appropriate for infaunal, deposit‐eater species, particularly in glacier‐dominated environments. Mollusks and foraminifera with sessile habits and pelagic food sources should be selected preferentially for 14C dating, because their shells may more closely reflect the 14C content of the global‐ocean mixed layer.
Automatic Glucose Control During Meals and Exercise in Type 1 Diabetes: Proof-of-Concept in Silico Tests Using a Switched LPV Approach

Keeping the blood glucose levels within the safe range during meals and exercise still represents a major hurdle not only for patients with type 1 diabetes (T1D), but also for Artificial Pancreas (AP) systems. One of the reasons a fully (autonomous) closed-loop solution has not been released onto the market yet is the slow action of current insulin analogs. To partially overcome this limitation, the authors have previously designed a switched control strategy equipped with an insulin-on-board (IOB) safety loop that mitigates meal-related glucose excursions without carbohydrate counting. In this letter, a similar strategy based on a Linear Parameter-Varying (LPV) control law has been adapted to safely handle also exercise challenges with minimum user intervention. In silico results using the UVA/Padova simulator evidence that the proposed closed-loop scheme is feasible under moderate-intense exercise bouts by effectively and safely reducing the risk of hypoglycemia.
A relation between lava discharge rate, thermal insulation, and flow area set using lidar data

A simple linear relation can be used to link time averaged discharge rate (TADR) and lava flow area (A). The relation applies to given insulation conditions, as described by the characteristic flow surface temperature (Te), and will vary from case‐to‐case depending on rheological and topographic influences on flow spreading. Most flows have insulation conditions that change through time, modifying the relationship between TADR and area as insulation conditions evolve. Using lidar data we can define TADR, the flow area that the discharge feeds and Te, allowing generation of a case‐specific relation to convert satellite‐data‐derived flow areas to TADR. For Etna's 2006 lava flow field we obtain a relation whereby TADR = 5.6 × 10−6 A for well insulated conditions (Te = 100°C) and TADR = 1.5 × 10−4 A for poorly insulated conditions (Te = 600°C).
Testing two different precipitation datasets to compute the standardized precipitation index over the Horn of Africa

Severe droughts are a frequent problem for the Horn of Africa. In this article, we test the possibility of using the standardized precipitation index (SPI) for monitoring drought conditions in this region. The SPI is a statistical indicator evaluating the lack or surplus of precipitation over different time scales, thus allowing the time-related impacts of the moisture deficit to be distinguished. The SPI is calculated as a function of long-term average precipitation, using continuous, long-term series of historical accumulated monthly precipitation records. The SPI for a given rainfall amount is then given in units of standard deviation from the mean of an equivalent normally distributed probability distribution function. As a consequence, wetter and drier climates and periods can be represented and monitored in the same way. For this study, the 3, 6, 9 and 12 month SPI values have been calculated for the period from 1985 to 2008 over the Horn of Africa using Global Precipitation Climatology Centre (GPCC) data and European Centre for Medium Range Weather Forecasting (ECMWF) ERA-40 reanalysis data interpolated to a 0.5° grid. Both SPI datasets have been compared for selected time periods by means of a root-mean-square-error, correlation-coefficient and contingency-table approach. First results demonstrate the feasibility of using the SPI for drought monitoring at regional scales over Africa, but further investigation is needed in order to identify the most suitable precipitation dataset for the SPI computation over Africa.