answer:We present a hierarchical learning framework for navigation within an embodied learning setting

answer:{"verdict": "SUPPORT", "evidence": ["Here, we identify several mechanisms by which a SARS-CoV open reading frame (ORF) activates intracellular stress pathways and targets the innate immune response."]}

answer:KITTI

answer:Creating a hologram effect cannot be done with SVG alone, as SVG is simply a vector image format. However, it can be achieved by applying CSS styles if displayed on a web page. For a full "hologram" effect complete with light rays, you may need to incorporate some sort of animation or 3D effect, which can't be accomplished with SVG by itself, but you can use CSS3 transformations and animations, and possibly a JavaScript library.Assuming you want to add some "hologram" effects using CSS, you could do something like this:```html<style> svg.hologram { animation: hologram-effect 2s infinite; mix-blend-mode: screen; position: relative; } svg.hologram:before, svg.hologram:after { content: ''; position: absolute; top: 0; left: 0; width: 100%; height: 100%; background: url(YOUR_SVG_HERE); animation: hologram-glow 2s infinite; mix-blend-mode: screen; } svg.hologram:before { transform: rotate(-45deg); animation-duration: 1.5s; } @keyframes hologram-effect { to { transform: translateX(5px); } } @keyframes hologram-glow { 50% { opacity: .5; } }</style><svg class="hologram"> <!-- YOUR SVG CONTENT HERE --></svg>```Just replace `YOUR_SVG_HERE` with the URL to your SVG or use a SVG string, and replace `<!-- YOUR SVG CONTENT HERE -->` with the content of your SVG.This will animate the SVG with a slight horizontal movement to simulate the hologram instability, and add some "glows" with different orientations to simulate the light dispersion of a hologram.Please note that the hologram effect quality highly depends on the complexity and colors of your SVG, and you might need to adjust the properties, add blurs, etc. SVG formatting does not support genuine light ray effects just by itself.For more advanced graphic effects like real 3D and light dispersion, you would need to use a WebGL based library, like Three.js.

answer:You can try the following code in Go:```gopackage mainimport ( "fmt" "sort")func sortArray(input []int) []int { result := make([]int, len(input)) copy(result, input) if len(result) < 2 { return result } sum := result[0] + result[len(result)-1] // If the sum of the first and last elements is even if sum%2 == 0 { // Sort the array in descending order sort.Sort(sort.Reverse(sort.IntSlice(result))) } else { // If the sum of the first and last elements is odd sort.Ints(result) } return result}func main() { array := []int{5, 2, 1, 4, 3} sortedArray := sortArray(array) fmt.Println(sortedArray)}```This is a fairly straight conversion of the given C++ code into Go. Here's what it does:1. Defined a function `sortArray` that takes a slice of integers as input and returns a newly created slice of integers (not affecting the original slice).2. Inside the function, first we created `result` which is a copy of our input slice.3. Then, checked if the length of `result` is less than 2. If so, returned `result` as it is. This is because a list of length smaller than 2 is already sorted by definition.4. Calculated the sum of the first and last elements of the result slice.5. Checked whether `sum` is an even number. If so, sorted `result` in descending order. If `sum` is not even (i.e., it's odd), sorted `result` in ascending order.6. Finally, after sorting, `result` is returned.7. Note that Go's sort package provides the `sort.Ints` function to sort a slice of integers in increasing order, and the `sort.Reverse` function to reverse a slice. To get a reversed (i.e., descending) sort, we have to reverse the result of `sort.Ints`, which gives `sort.Sort(sort.Reverse(sort.IntSlice(result)))`.8. As in Go, the function to print an array is `fmt.Println()`, so the sorted array result is printed in the `main()` function.

answer:To implement Topic Modeling using Non-Negative Matrix Factorization (NMF), you need to follow these steps:Step 1: Load the DataWe start with importing libraries and loading the csv file into a pandas dataframe.```pythonimport pandas as pdimport numpy as npfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.decomposition import NMFdata = pd.read_csv('https://raw.githubusercontent.com/crow-intelligence/growth-hacking-sentiment/master/dataset/ratings.csv')```Step 2: Text preprocessingFor the reviews, we may have to clean up the text so that the outcome is not affected by punctuations, common words, etc. You can achieve this with techniques such as tokenization, removing stop words and lemmatization.```pythonimport nltknltk.download('stopwords')nltk.download('wordnet')from nltk.corpus import stopwords from nltk.stem.wordnet import WordNetLemmatizerimport stringstop = set(stopwords.words('english'))exclude = set(string.punctuation)lemma = WordNetLemmatizer()def clean(doc): stop_free = " ".join([i for i in doc.lower().split() if i not in stop]) punc_free = ''.join(ch for ch in stop_free if ch not in exclude) normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split()) return normalizeddoc_clean = [clean(doc).split() for doc in data['review']]```Step 3: Term Frequency-Inverse Document Frequency (TF-IDF) Vectorization For NMF to work with text data, the text has to be transformed into a matrix of TF-IDF features.```pythonvectorizer = TfidfVectorizer( max_features=2000, # keep top 2000 terms min_df=5, # minimum doc freq stop_words='english', # remove stop words lowercase=True, # convert all words to lowercase token_pattern='[a-zA-Z0-9]{3,}', # num chars > 3 max_df=0.6, # set max freq to be used)data_vectorized = vectorizer.fit_transform(data['review'])```Step 4: Perform NMFWe perform the NMF and output the ten most common words for each discovered topic.```pythonnmf = NMF(n_components=5, random_state=42, alpha=.1, l1_ratio=.5)nmf.fit(data_vectorized)``````pythondef display_topics(model, feature_names, no_top_words): for topic_idx, topic in enumerate(model.components_): print("Topic %d:" % (topic_idx)) print(" ".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]))no_top_words = 10display_topics(nmf, vectorizer.get_feature_names(), no_top_words)```It's important to note that the number of topics/n_components in NMF is a hyperparameter that you can tweak and optimize. As more topics can give you more specific subtopics, and fewer topics results in more broader topics.Please note: You may need to install the required python packages. You can do this with pip:```python!pip install nltk sklearn pandas numpy```