Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of stocking rate on growth and reproductive performance of breeding snails (Archachatina marginata).

It has been established that performance of livestock is affected by stocking density. Overcrowding has been found to have negative effects on growth and reproductive performance hence, this study was conducted to establish appropriate stocking density for breeding snails. A total of two hundred and twenty-five breeding African giant land snails (Archachatina marginata) without shell damage were selected for this trial which lasted 6 months. The snails had an average live weight of 437.41 ± 11.56 g. They were randomly allotted to 5 different treatments TK, TL, TM, TN TO, with stocking rates of 5, 10, 15, 20 and 25 snails per square metre, respectively. Each treatment was replicated thrice in a completely randomized design. Feed intake, weight gain, shell length and width were measured while reproductive parameters in terms of number and weight of the eggs collected, incubation period and mean weight of the hatchlings at day old were also taken. The results showed no significant (P > 0.05) differences in mean feed intake and weight gain in TK, TL, and TM while the lowest mean feed intake and weight gain were recorded in To, stocked at 25 snails per square metre. There were no significant differences in the mean monthly shell length increment (P > 0.05). The highest mortality of 25.3% was recorded in TOstocked with 25 breeding snails per square metre. The highest mean total egg collection of 32.61 eggs was recorded in TK which was relatively the same with TL, TM and TN (P > 0.05). The mean weight of the eggs were relatively the same (P > 0.05) in TK, TL , TM and TN . The lowest mean egg weight was recorded in To. It could be concluded that one metre square apartment could house up to 15 breeding snails without any adverse effect on growth, reproduction and state of health of the breeding snails. Key words: Egg size, hatchling’s weight, Nigeria, snails, stocking rate, weight gain.
GPS L5 Software Receiver Implementation in FGI-GSRx

As a part of GPS modernization project U.S has introduced a third civil signal called L5. The new signal is intended to satisfy the challenging demands for safety-of-life transport and applications requiring higher precision. A software receiver for GPS L5 signal has been implemented and tested with the FGI-GSRx software-defined multi-frequency multi-GNSS receiver. This paper outlines the implementation of GPS L5 signal processing chain and offers a brief description of the L5 signal characteristics. It also demonstrates the comparison between GPS L1 C/A and L5 signals at different phases. Due to its modern signal characteristics and greater processing capacities, GPS L5 is expected to perform better than the legacy GPS L1 C/A signal.
Climate change will cause non-analogue vegetation states in Africa and commit vegetation to long-term change

Abstract. Vegetation responses to changes in environmental drivers can be subject to temporal lags. This implies that vegetation is committed to future changes once environmental drivers stabilize. Understanding the trajectories of such committed changes is important as they affect future carbon storage, vegetation structure and community composition and therefore need consideration in conservation management. In this study, we investigate whether transient vegetation states can be represented by a time-shifted trajectory of equilibrium vegetation states, or if they are vegetation states without analogue in conceivable equilibrium states. We use a dynamic vegetation model, the aDGVM, to assess deviations between simulated transient and equilibrium vegetation states in Africa between 1970 and 2099 for the RCP4.5 and 8.5 scenarios. Euclidean distance between simulated transient and equilibrium vegetation states based on selected state variables was used to determine lag times and similarity of vegetation states. We found that transient vegetation states over time increasingly deviated from equilibrium states in both RCP scenarios, but that deviation was more pronounced in RCP8.5 during the second half of the 21st century. Trajectories of transient vegetation change did not follow a virtual trajectory of equilibrium states, but represented non-analogue composite states resulting from multiple lags with respect to vegetation processes and composition. Lag times between transient and most similar equilibrium vegetation states increased over time and were most pronounced in savanna and woodland areas, where disequilibrium in savanna tree cover frequently acted as main driver for dissimilarities. Fire additionally enhanced lag times and Euclidean distance between transient and equilibrium vegetation states due to its restraining effect on vegetation succession. Long lag times can be indicative of high rates of change in environmental drivers, of meta-stability and non-analogue vegetation states, and of augmented risk for future tipping points. For long-term planning, conservation managers should therefore strongly focus on areas where such long lag times and high residual Euclidean distance between most similar transient and equilibrium vegetation states have been simulated.
Global Texture Enhancement for Fake Face Detection In the Wild: Supplementary File

To compare with existing works and evaluate the GramNet when training and testing images are in different semantic classes, we follow the setting in [4], which is also the “leave one out” setting in [5]. We evaluate on CycleGAN [6] dataset in which images of one category are set aside for testing while the remaining for training. There are a total number of 14 categories: Horse (H), Zebra (Z), Yosemite Summer (S), Yosemite Winter (W), Apple (A), Orange (O), Facades (F), CityScape Photo (City), Satellite Image (Map), Ukiyoe (U), Van Gogh (V), Cezanne (C), Monet (M) and Photo (P). Following [5], we also exclude the sketch and pixel-level semantic map from the dataset. We train Gram-Net with the same training strategy as in the main paper. Table 1 shows that Gram-Net achieves 98.49% mean accuracy of all the settings, which outperforms existing works. To be noted, we use ResNet-50 as our backbone compared to DesnetNet-121[3] in [5] and Xception71 [1] in [4]. We expect that deeper backbone networks will further benefit our performance. More importantly, [5] fails when GANs adopted in training and testing are with different upsampling structures. However, as shown in Table 3 cross-GAN setting ( StyleGAN: nearest-upsampling and PGGAN: deconvolution upsampling ) in the main paper, our approach works almost perfectly in this setting.
LOCALIZATION OF NORTH ATLANTIC RIGHT WHALE SOUNDS IN THE BAY OF FUNDY USING A SONOBUOY ARRAY

A free-drifting 14-sonobuoy array was used to localize North Atlantic right whales (Eubalaena glacialis) in the Grand Manan Basin area of the Bay of Fundy. This area is a primary summerlautumn right whale habitat and overlaps an international shipping lane. The three-hour deployment on a single day provided twodimensional localization of 94 right whale sounds based on arrival time differences determined from spectrogram cross-correlation analysis. The sounds were of two distinct types: tonal and gunshot. Maximum detection distances were about 30 km for both types of sound. The mean RMS location error was 1.8 km for tonal-type sounds and 2.5 km for gunshot-type sounds. The average RMS error was 20% of the average distance from the receiving hydrophones, the primary source of error being uncertainty in the sonobuoy positions.
Vernalization requirement of wild beet Beta vulgaris ssp. maritima: among population variation and its adaptive significance

1 Seven populations of Beta vulgaris ssp. maritima (wild beet) situated along a latitudinal cline were studied for their vernalization requirement and its consequences for fitness. 2 Various cold regimes were applied in glasshouses and experimental gardens with plants of different ages. Three additional experimental sites (on the French Mediterranean, Atlantic and North Sea coasts) situated near three of the sampled populations, and thus including a reciprocal transplant design, were used to evaluate the influence of latitude under natural conditions. Survival and plant size were measured over 3 years. 3 The vernalization requirement was greater in plants from more northern origins. The level of cold required to allow flowering overcompensated for the colder springs, so that northern plants in northern sites flowered less than southern plants in southern sites. 4 Young seedlings were more difficult to vernalize than plants that had already developed vegetative rosettes. 5 Differences in vernalization requirement seem to be an adaptive response to spring temperatures and season length in a particular latitude. A whole winter vernalization almost always led to flowering in the subsequent year whatever the latitude or geographical origin. 6 Plants from the Atlantic and Channel coasts showed the highest lifetime reproductive success at all sites. Southern populations were better adapted to disturbed habitats as shown by their higher first‐year reproductive success. The North Sea population had a lower reproductive success than the Atlantic populations, even in its native environment.
ELF: An Early-Exiting Framework for Long-Tailed Classification

The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples. This long-tail causes classifiers to overfit to the majority class. To mitigate this, prior solutions commonly adopt class rebalancing strategies such as data resampling and loss reshaping. However, by treating each example within a class equally, these methods fail to account for the important notion of example hardness, i.e., within each class some examples are easier to classify than others. To incorporate this notion of hardness into the learning process, we propose the EarLy-exiting Framework(ELF). During training, ELF learns to early-exit easy examples through auxiliary branches attached to a backbone network. This offers a dual benefit-(1) the neural network increasingly focuses on hard examples, since they contribute more to the overall network loss; and (2) it frees up additional model capacity to distinguish difficult examples. Experimental results on two large-scale datasets, ImageNet LT and iNaturalist'18, demonstrate that ELF can improve state-of-the-art accuracy by more than 3 percent. This comes with the additional benefit of reducing up to 20 percent of inference time FLOPS. ELF is complementary to prior work and can naturally integrate with a variety of existing methods to tackle the challenge of long-tailed distributions.
CSI-Based Versus RSS-Based Secret-Key Generation Under Correlated Eavesdropping

Physical-layer security (PLS) has the potential to strongly enhance the overall system security as an alternative to or in combination with conventional cryptographic primitives usually implemented at higher network layers. Secret-key generation relying on wireless channel reciprocity is an interesting solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this article, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS). Moreover, as opposed to previous works, we take into account the fact that the eavesdropper’s observations might be correlated and we consider the high signal-to-noise ratio (SNR) regime where we can find simple analytical expressions for the secret-key capacity. As already found in previous works, we find that RSS-based secret-key generation is heavily penalized as compared to CSI-based systems. At high SNR, we are able to precisely and simply quantify this penalty: a halved pre-log factor and a constant penalty of about 0.69 bit, which disappears as Eve’s channel gets highly correlated.
Limits to Depth Efficiencies of Self-Attention

Self-attention architectures, which are rapidly pushing the frontier in natural language processing, demonstrate a surprising depth-inefficient behavior: Empirical signals indicate that increasing the internal representation (network width) is just as useful as increasing the number of self-attention layers (network depth). In this paper, we theoretically study the interplay between depth and width in self-attention, and shed light on the root of the above phenomenon. We invalidate the seemingly plausible hypothesis by which widening is as effective as deepening for self-attention, and show that in fact stacking self-attention layers is so effective that it quickly saturates a capacity of the network width. Specifically, we pinpoint a "depth threshold" that is logarithmic in $d_x$, the network width: $L_{\textrm{th}}=\log_{3}(d_x)$. For networks of depth that is below the threshold, we establish a double-exponential depth-efficiency of the self-attention operation, while for depths over the threshold we show that depth-inefficiency kicks in. Our predictions strongly accord with extensive empirical ablations in Kaplan et al. (2020), accounting for the different behaviors in the two depth-(in)efficiency regimes. By identifying network width as a limiting factor, our analysis indicates that solutions for dramatically increasing the width can facilitate the next leap in self-attention expressivity.
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation

The autoregressive language model (ALM) trained with maximum likelihood estimation (MLE) is widely used in unconditional text generation. Due to exposure bias, the generated texts still suffer from low quality and diversity. This presents statistically as a discrepancy between the real text and generated text. Some research shows a discriminator can detect this discrepancy. Because the discriminator can encode more information than the generator, discriminator has the potentiality to improve generator. To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely. A critical reason for the failure is the difference between the discriminator input and the ALM input. We propose a novel mechanism by adding a filter which has the same input as the discriminator. First, discriminator detects the discrepancy signals and passes to filter directly (or by learning). Then, we use the filter to reject some generated samples with a sampling-based method. Thus, the original generative distribution is revised to reduce the discrepancy. Two ALMs, RNN-based and Transformer-based, are experimented. Evaluated precisely by three metrics, our mechanism consistently outperforms the ALMs and all kinds of GANs across two benchmark data sets.
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series

Recent advances in nonlinear Independent Component Analysis (ICA) provide a principled framework for unsupervised feature learning and disentanglement. The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index. This requires manual segmentation of data into non-stationary segments which is computationally expensive, inaccurate and often impossible. These models are thus not fully unsupervised. We remedy these limitations by combining nonlinear ICA with a Hidden Markov Model, resulting in a model where a latent state acts in place of the observed segment-index. We prove identifiability of the proposed model for a general mixing nonlinearity, such as a neural network. We also show how maximum likelihood estimation of the model can be done using the expectation-maximization algorithm. Thus, we achieve a new nonlinear ICA framework which is unsupervised, more efficient, as well as able to model underlying temporal dynamics.
Deconstructing Claims of Post-Treatment Bias in Observational Studies of Discrimination

In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether arrested individuals would have been subsequently charged or convicted had they been a different race. It has long been known that such counterfactual questions face measurement challenges related to omitted-variable bias, and conceptual challenges related to the definition of causal estimands for largely immutable characteristics. Another concern, raised most recently in Knox et al. [2020], is post-treatment bias. The authors argue that many studies of discrimination condition on intermediate outcomes, like being arrested, which themselves may be the product of discrimination, corrupting statistical estimates. Here we show that the Knox et al. critique is itself flawed, suffering from a mathematical error. Within their causal framework, we prove that a primary quantity of interest in discrimination studies is nonparametrically identifiable under a standard ignorability condition that is common in causal inference with observational data. More generally, though, we argue that it is often problematic to conceptualize discrimination in terms of a causal effect of protected attributes on decisions. We present an alternative perspective that avoids the common statistical difficulties, and which closely relates to long-standing legal and economic theories of disparate impact. We illustrate these ideas both with synthetic data and by analyzing the charging decisions of a prosecutor's office in a large city in the United States.
SkyHaul: An Autonomous Gigabit Network Fabric in the Sky

We design and build SKYHAUL, the first large-scale, autonomous, self-organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mmWave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth-intensive, latency-sensitive cooperative applications (e.g., LTE coverage during disasters, surveillance during rescue in challenging terrains), the network of UAVs allows these applications to be executed at operating ranges that are far beyond the line-of-sight distances that limit individual UAVs today. To realize the challenging vision of deploying and maintaining an airborne mmWave mesh backhaul to cater to dynamic applications, SKYHAUL's design incorporates various elements: (1) Role-specific UAV operations that simultaneously address application tracking and backhaul connectivity (2) Novel algorithms to jointly address the problem of deployment (position, yaw of UAVs) and traffic routing across the UAV network; and (3) A provably optimal solution for fast and safe reconfiguration of UAV backhaul during application dynamics. We implement SKYHAUL on four DJI Matrice 600 Pros to demonstrate its practicality and performance through autonomous flight operations, complemented by large scale simulations.
Introduction of Channel Catfish Ictalurus punctatus (Rafinesque) in Pakistan and its Performance During Acclimatization and Pond Culture

Two thousand fingerlings of channel catfish Ictalurus punctatus were imported from Thailand in a bid to introduce this fish in Pakistan. Five percent mortality occurred during transportation. Experiments were designed to observe catfish acclimatization in tanks or raceways for which five hundred fingerlings (average weight 10.86±1.20g) were placed in five tanks of 2000-liter water capacity and another 500 fingerlings (average weight, 10.56±0.68g) were kept in five raceways of 5000-liter water capacity each. The fish were fed on imported diet for a period of 75 days. Mean weight gain of 27.22±1.75 g and 31.5±1.04 g and surv~7.5% and 95.9% were recorded in tanks and raceways, respectively. For studying growth of fish two stocking densities (3,000 and 3,500/ha) were maintained in ponds (0.04 ha) from December 2003 - November 2004. The weight gain was significantly higher in low stocking density (1,263.3 ± 60.9 g) compared with high stocking density (1,184.9±57.1 g). Fish production and survival between two stocking densities was not different (P>0.05).
The Hubble tension in light of the Full-Shape analysis of Large-Scale Structure data

The disagreement between direct late-time measurements of the Hubble constant from the SH0ES collaboration, and early-universe measurements based on the ΛCDM model from the Planck collaboration might, at least in principle, be explained by new physics in the early universe. Recently, the application of the Effective Field Theory of Large-Scale Structure to the full shape of the power spectrum of the SDSS/BOSS data has revealed a new, rather powerful, way to measure the Hubble constant and the other cosmological parameters from Large-Scale Structure surveys. In light of this, we analyze two models for early universe physics, Early Dark Energy and Rock 'n' Roll, that were designed to significantly ameliorate the Hubble tension. Upon including the information from the full shape to the Planck, BAO, and Supernovae measurements, we find that the degeneracies in the cosmological parameters that were introduced by these models are well broken by the data, so that these two models do not significantly ameliorate the tension.
Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring

Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable, if such driving maneuvers are detected beforehand and the driver is assisted accordingly. In fact, various recent research has focused on the automated prediction of driving maneuver based on hand-crafted features extracted mainly from in-cabin driver videos. Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers’ intention based on both in-cabin and traffic scene videos. More specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder to extract motion features from the out-cabin traffic, (2) train a classifier which considers motions from both in- and outside of the cabin jointly for maneuver intention anticipation, (3) experimentally prove that the in- and outside image features have complementary information. Our evaluation based on the publicly available dataset Brain4cars shows that our framework achieves a prediction with the accuracy of 83.98% and $F_{1}-$ score of 84.3%.
Continual Learning in Recurrent Neural Networks with Hypernetworks

The last decade has seen a surge of interest in continual learning (CL), and a variety of methods have been developed to alleviate catastrophic forgetting. However, most prior work has focused on tasks with static data, while CL on sequential data has remained largely unexplored. Here we address this gap in two ways. First, we evaluate the performance of established CL methods when applied to recurrent neural networks (RNNs). We primarily focus on elastic weight consolidation, which is limited by a stability-plasticity trade-off, and explore the particularities of this trade-off when using sequential data. We show that high working memory requirements, but not necessarily sequence length, lead to an increased need for stability at the cost of decreased performance on subsequent tasks. Second, to overcome this limitation we employ a recent method based on hypernetworks and apply it to RNNs to address catastrophic forgetting on sequential data. By generating the weights of a main RNN in a task-dependent manner, our approach disentangles stability and plasticity, and outperforms alternative methods in a range of experiments. Overall, our work provides several key insights on the differences between CL in feedforward networks and in RNNs, while offering a novel solution to effectively tackle CL on sequential data.